text:
\section{Introduction}
\label{sec:introduction}  %
 Regional ventilation is the term used to characterize the
volume of fresh gas per unit time that enters or exits the lung at
the acinar (gas exchange) level. Disruption of regional ventilation
can reflect alterations to airways (physiological or pathological),
alterations in parenchymal mechanics, changes to the muscles of
respiration, body posture effects and inhaled gas properties. Thus,
measures of regional lung mechanics can serve as a sensitive test of
the status of the respiratory system and should be considerably more
sensitive and informative than global pulmonary function test.
Recent advances in multi-detector-row CT (MDCT), %
4DCT respiratory gating methods, and image processing
techniques enable us to study pulmonary function at the regional
level with high resolution anatomical information compared to other
methods.  MDCT can be used to acquire multiple static breath-hold CT
images of the lung taken at different lung volumes, or 4DCT images
of the lung acquired during spiral scanning using a low pitch and
retrospectively reconstructed at different respiratory phases with
proper respiratory control~\cite{Keall2002,low:1254,pan:627}. Image
registration can be applied to these data to estimate a deformation
field that transforms the lung from one volume configuration to the
other. This deformation field can be analyzed to estimate local lung
tissue expansion, calculate voxel-by-voxel intensity change, and
make biomechanical measurements.  When combined with image
segmentation
algorithms~\cite{Reinhardt2008752,ding2008b,ding2009,ding2009b},
functional and biomechanical measurements can be reported on a lung,
lobe, and sublobar basis, and can be used to interpret regional lung
function relative to specific segments of bronchial tree. Such
measurements of pulmonary function have proven useful as a planning
tool during RT planning~\cite{Yaremko2007562, Yamamoto2010} and may be useful for
tracking the progression of toxicity to nearby normal tissue during
RT and can be used to evaluate the effectiveness of a treatment
post-therapy~\cite{ding:1261}.

Early studies using CT to study regional air volume changes have
proved to enhance our understanding of normal lung function.
Several groups have proposed methods that couple image registration
and CT imaging to study regional lung function. Guerrero et al.\ have
used optical-flow registration to compute lung ventilation from
4DCT~\cite{Guerrero2005630,guerrero2006a} with an intensity-based
ventilation measure. Christensen et al.\ used image registration to
match images across cine-CT sequences and estimate rates of local
tissue expansion and contraction~\cite{christensen2007a} using a
Jacobian-based ventilation measure. While they were able to show that
their accumulated measurements matched well with the global
measurements, they were not able to compare the registration-based
measurements to local measures of regional tissue
ventilation. Recently, Castillo et al.\ compared the intensity-based
and Jacobian-based calculations of ventilation from 4DCT with the
ventilation from $^{99m}Tc$-labeled aerosol
SPECT/CT~\cite{Castillo2010}. A statistically higher correlation to
the SPECT/CT based ventilation was found for intensity-based based
calculation over the Jacobian-based calculation. However, the
comparison of the two techniques was based on the Dice similarity
coefficient between the thresholded masks within 20\% variation from
the 4DCT and from SPECT/CT. Though their experiment is novel and
important, since the average mask size is about 490.5 mL (with average
subject exhale volume 2452.7 mL, and 5 sub-masks per subject), the
comparison is more global than regional. In addition, as shown in
Section~\ref{sect:meth_regvent}, both the intensity-based and
Jacobian-based ventilation measures are based on the assumption that
regional lung volume change is due solely to air content change,
which may not always be a valid assumption.   Other factors,
such as blood volume change, may also introduce the regional lung
volume change.

The physiologic significance of these registration-based measures of
respiratory function can be established by comparing to more
conventional measurements, such as nuclear medicine or contrast
wash-in/wash-out studies with CT or MR\@.  Xenon-enhanced CT
(Xe-CT) measures regional ventilation by observing the gas wash-in
or wash-out rate on serial CT
images~\cite{Marcucci02012001,tajik2002,Chon200565}
Xe-CT imaging has the advantage of high temporal
resolution and spatial resolution and reflects a measure of fresh
gas delivery to the gas exchange units of the lung.  Although the
dynamic Xe-CT method is limited in Z-axis coverage,
requires expensive Xe gas, and is technically challenging, it serves as
the gold standard of regional ventilation and can be used to compare
with registration-based measures of regional lung function in animal
studies for validation purposes.

This paper describes three measures to estimate regional ventilation
from image registration of CT images: the specific air volume change
calculated from the Jacobian (SAJ); the specific air volume change
calculated by the corrected Jacobian (SACJ); and the specific air
volume change by intensity change (SAI). We show that the SAI
ventilation measure can be derived from the SAJ measure by making the
assumption that there is no tissue volume change between
registration volumes.  We evaluate these three measures by comparing
them with a Xe-CT measure of ventilation in a regional basis (20 mm $\times$ 20
mm $\times$ 20 mm cube, or 8 mL). Among these three
registration based measures, we show that the corrected
Jacobian-based measure, SACJ, has the best correlation with the
Xe-CT derived measure of specific ventilation.

 %

\section{Material and methods}
\label{sect:meth}

\subsection{Method Overview}
\label{sect:meth_overview}

Our goal is to validate and compare the measures used to estimate
regional lung ventilation from image registration by comparing them
to Xe-CT estimated ventilation.
Figure~\ref{fig:ventmeasureflowchart} shows a block diagram of the
entire process. Two types of data were acquired for each animal: a
4DCT scan and a Xe-CT scan. In order to make our comparisons under
the same physiological conditions, each animal was scanned and
mechanically ventilated with the same respiratory rate, tidal volume
(TV) and positive end-expiratory pressure (PEEP) during the two
types of scans. The data sets from the 4DCT scan were reconstructed
in volumes at eight phases of the respiratory cycle. For this study
we focus on the data sets from two of the phases, a volume near end
expiration (\ee{}) and a volume near end inspiration (\ei{}). For
the Xe-CT scan, 45 distinctive partial lung volumetric scans were
performed at volume near end expiration, or the initial end
expiration scan (\eeinit{}) to the last expiration scan (\eelast{}).

The nonlinear image registration is used to define the
transformation T1 between the \ee{} and \ei{} in order to measure
the regional lung ventilation from the 4DCT scan. The Xe-CT-based
estimated regional lung ventilation is computed on the \eeinit{} by
using Pulmonary Analysis Software Suite 11.0 (PASS) software by
finding the constant of the exponential rise of the density from
xenon gas wash-in over multiple breaths~\cite{guo2008}. The same
nonlinear image registration is also applied to define the
transformation T2 which maps the \eeinit{} to the \ee{} so that the
Xe-CT based estimate of ventilation can be mapped into the same
coordinate system as that of the registration-based estimate of
ventilation. Additional details on the registration algorithm and
other processing steps are given below.
\begin{figure}[tpb]
  \centering
  \includegraphics[width=\textwidth]{flowchart.eps}
  \caption{Figure shows the two types of images, a image pair of full lung volumetric phases \ee{} and \ei{} from a 4DCT scan and a Xe-CT scan acquired at end of expiration over 45 respiratory cycles (\eeinit{} to \eelast{}), which are analyzed during the
    processing.  Transformation T1 registers end inspiration
    (EI) to end expiration (EE) data and can be used to assess local
    lung function via calculations of three ventilation measures: specific air volume change
by specific volume change (SAJ), specific air volume change by
corrected Jacobian (SACJ), and specific air volume change by
intensity (SAI). The 45 distinctive partial lung volumetric Xe-CT
scans \eeinit{} to \eelast{} are used to calculate Xe-CT-based
measure of specific ventilation (sV). Transformation T2 maps the sV
data into the coordinate system of the
    \ee{} image~(end expiration phase of the 4DCT scan) to allow direct comparison with
    the 4DCT and registration-based measures of ventilation. Both \ee{} and \eeinit{} are at volumes near end inspiration.  (Shaded boxes indicate CT image data; white boxes
    indicated derived or calculated data; thick arrows indicate image
    registration transformations being calculated; thin solid lines
    indicate other operations.)}
 \label{fig:ventmeasureflowchart}
\end{figure}

\subsection{Image Data Sets}
\label{sect:meth_data}

Appropriate animal ethics approval was obtained for these protocols
from the University of Iowa Animal Care and Use Committee and the
study adhered to NIH guidelines for animal experimentation. Four
adult male sheep A, B, C, and D (with weights 44.0, 37.8, 40.4, and
46.7 kg) were used for this study.  The sheep were
anesthetized using intravenous pentobarbital and pancuronium to
ensure adequate sedation and to prevent spontaneous breathing.
Animals were positive pressure ventilated during experiments using a
custom built dual Harvard apparatus piston ventilator designed for
computer control. The 4DCT images were acquired with the animals in
the supine position using the dynamic imaging protocol with a pitch
of 0.1, slice collimation of 0.6 mm, rotation time of 0.5 sec, slice
thickness of 0.75 mm, slice increment of 0.5 mm, 120 kV, 400 mAs,
and kernel B30f.
The airway pressure signal was simultaneously recorded with
the X-ray projections and images were reconstructed retrospectively
at 0, 25, 50, 75, and 100\% of the inspiration duration and 75, 50
and 25\% of the expiration duration. The 0\% (\ee{}) and 100\%
(\ei{}) inspiration phases were used for later ventilation
measurements. A slab of twelve contiguous axial slices were imaged
over 45 breaths for Xe-CT scans. Images were acquired using
respiratory gating by triggering the scan during end-expiration with
80 keV energy (for higher density resolution, approximately 2 HU per
1\% Xe), 160 mAs tube current, a 360$^\circ$ rotation, a 0.33 sec
scan time, and 2.4 mm slice thickness. Respiratory gating is
achieved using a custom built LabVIEW program which controls the
ventilators and triggers the CT scanner. The respiratory rate (RR)
for four animals ranged from 15 to 18 breaths per minute with an
inspiratory-expiratory ratio of 1:1 which was sufficient to maintain
a normocapnic state. Both of the two types of images were acquired
with a matrix of 512 by 512 and without moving the animal between
scans.

\subsection{Image Registration}
\label{sect:meth_reg} A tissue volume and vesselness measure
preserving nonrigid registration (TVP)
algorithm~\cite{cao:762309,caowbir2010} is used to estimate the
transformations \ei{} to \ee{} and \eeinit{} to \ee{}. The TVP
algorithm minimizes the sum of squared tissue volume difference
(SSTVD)~\cite{Yin2009,yin:4213,cao2009a,Yin20102159} and vesselness
measure difference (SSVMD), utilizing the rich image intensity
information and natural anatomic landmarks provided by the vessels.
This method has been shown to be effective at registering across
lung CT images with high accuracy~\cite{cao:762309,caowbir2010}.

Let $I_1$ and $I_2$ represent two 3D image volumes to be registered.
The vector $\mathbf{x}=(x_1, x_2, x_3)^T$ defines the voxel
coordinate within an image. The algorithm finds the optimal
transformation $\mathbf{h}$ that maps the template image $I_1$ to
the target image $I_2$ by minimizing the cost function

\begin{eqnarray}
  C_{\text{TOTAL}}&=&\rho \int_\mathbf{\Omega}
 \left[V_2(\mathbf{x})-V_1(\mathbf{h(x)})\right]^2 d\mathbf{x}
 +\chi \int_\mathbf{\Omega}
  \left[F_2(\mathbf{x})-F_1(\mathbf{h(x)})\right]^2d\mathbf{x}
  \label{eq:total_cost}.
\end{eqnarray}
 where $\Omega$ is the union domain of the lung regions in
images $I_1$ and $I_2$. $V_2$ and  $V_1$ are the tissue volumes as
defined in Equation~\ref{eq:TissueVolume}. $F_2$ and $F_1$ are the
vesselness measures as defined in Equation~\ref{eq:fangi_fcn}. The
transformation $\mathbf{h}$ is a $(3 \times 1)$ vector-valued
function that maps a point $\mathbf{h(x)}$ in the target image to
its corresponding location in the template image.  The first
integral of the cost function defines the SSTVD cost and the second
integral of the cost function defines the SSVMD cost.

The SSTVD cost assumes that the measured Hounsfield units (HU) in
the lung CT images is a function of tissue and air content.
Following the air-tissue mixture model by Hoffman et
al.~\cite{Hoffman1985}, from the CT value of a given voxel, the
tissue volume can be estimated as
\begin{equation}
\label{eq:TissueVolume} V(\mathbf{x}) = \nu(\mathbf{x})
\frac{I(\mathbf{x})-HU_{air}
}{HU_{tissue}-HU_{air}}=\nu(\mathbf{x})\beta(I(\mathbf{x})),
\end{equation}
and the air volume can be estimated as
\begin{equation}
\label{eq:AirVolume} V'(\mathbf{x}) = \nu(\mathbf{x})
\frac{HU_{tissue}-I(\mathbf{x})
}{HU_{tissue}-HU_{air}}=\nu(\mathbf{x})\alpha(I(\mathbf{x})),
\end{equation}
\noindent where $\nu (\mathbf{x})$ denotes the volume of voxel
$\mathbf{x}$ and $I(\mathbf{x})$ is the intensity of a voxel at
position $\mathbf{x}$.  $HU_{air}$ and $HU_{tissue}$ refer to the
intensity of air and tissue, respectively. In this work, we assume
that air is -1000 HU and tissue is 0 HU.
$\alpha(I(\mathbf{x}))=\frac{HU_{tissue}-I(\mathbf{x})
}{HU_{tissue}-HU_{air}}$ and
$\beta(I(\mathbf{x}))=\frac{I(\mathbf{x})-HU_{air}
}{HU_{tissue}-HU_{air}}$ are introduced for notational simplicity.
Notice that $\alpha(I(\mathbf{x}))+\beta(I(\mathbf{x}))=1$.

Given~(\ref{eq:TissueVolume}), we can then define the SSTVD cost:
\begin{eqnarray}
  C_{\text{SSTVD}}
  &=&\int_\mathbf{\Omega}\left[V_2(\mathbf{x})-V_1(\mathbf{h(x)})\right]^2d\mathbf{x}\\
  &=&\int_\mathbf{\Omega}\left[ \nu_2(\mathbf{x})\beta(I_2(\mathbf{x})) - \nu_1(\mathbf{h(x)})\beta(I_1(\mathbf{h(x)})) \right]^2 d\mathbf{x}
 \label{eq:sstvd_cost_1},
\end{eqnarray}
 The notation $I_1(\mathbf{h(x)})$ is interpreted as the
image $I_1(\mathbf{x})$ deformed by the transformation
$\mathbf{h(x)}$ and is computed using trilinear interpolation. The
deformed volume element $\nu _1(\mathbf{h(x)})$ is  calculated using
the Jacobian $J(\mathbf{x})$ times the volume element $\nu _2
(\mathbf{x})$, i.e., $\nu _1(\mathbf{h(x)})=J(\mathbf{x}) \nu _2
(\mathbf{x})$.

Figure~\ref{fig:airtissuevolume} shows an example of a cubic shaped
region under deformation $\mathbf{h}$ from template image to target
image. The region volumes are $\nu_1$ and $\nu_2$. The volumes can
be decomposed into the tissue volume fraction and air volume
fraction based on the mean voxel intensity within the cube. The
small white sub volumes inside the cubes represent the tissue volume
$V_1$ and $V_2$. Air volumes are represented by $V'_1$ and $V'_2$
(in blue). As the ratio of air to tissue decreases, the CT intensity
of a voxel increases. The mean cube voxel intensities for the
template, $I_1$, and target images, $I_2$, are functions of the
ratios of air to tissue volumes within the cubes.

\begin{figure}[tbp]
  \centering
  \includegraphics[width=0.8\textwidth]{airtissuevolume.eps}
  \caption{Example of a region under deformation
  $\mathbf{h(x)}$ from template image to target image. $V_1$ and
  $V_2$ are tissue volumes in the regions. $V'_1$ and
$V'_2$ are air volumes in the regions. Region volumes $\nu_1 = V_1 +
V'_1$ and $\nu_2 = V_2 + V'_2$.}
  \label{fig:airtissuevolume}
\end{figure}

As the blood vessels branch to smaller and smaller diameters, the
raw grayscale information from vessel voxels provide very little
contribution to guide the intensity-based registration. To better
utilize the information of blood vessel locations, we use the
vesselness measure based on the eigenvalues of the Hessian matrix of
image intensity. Frangi's vesselness
function~\cite{Frangi98multiscalevessel} is defined as
\begin{equation}
  F(\lambda)=\left\{\begin{array}{ll}
    (1-e^{-\frac{R_A^2}{2\alpha^2}})\cdot e^{\frac{-R_B^2}{2\beta^2}} \cdot
    (1-e^{-\frac{S^2}{2\gamma^2}})& \text{if}~\lambda_2<0~\text{and}~\lambda_3<0\\
    0& \text{otherwise}
  \end{array}\right.
\label{eq:fangi_fcn}
\end{equation}
with
\begin{equation}
  R_A=\frac{|\lambda_2|}{|\lambda_3|}, ~ ~ ~ ~ ~
  R_B=\frac{|\lambda_1|}{\sqrt{|\lambda_2\lambda_3|}}, ~ ~ ~ ~ ~
  S=\sqrt{\lambda_1^2+\lambda_2^2+\lambda_3^2},
\label{eq:fangi_ra}
\end{equation}
where $R_A$ distinguishes between plate-like and tubular structures,
$R_B$ accounts for the deviation from a blob-like structure, and $S$
differentiates between tubular structure and noise. The vesselness
function has been previously widely used in vessel segmentations in
lungs~\cite{shikata2004, Shikata2009} and in
retinas~\cite{joshi2010a}. $\alpha$, $\beta$, $\gamma$ control the
sensitivity of the vesselness measure. The vesselness measure is
rescaled to [0, 1] and can be considered as a probability-like
estimate of vesselness features. For this study, $\alpha=0.5$,
$\beta=0.5$, and $\gamma=5$ and the weighting constants in the total
cost were set as $\rho=1$ and $\chi=0.2$. These parameters are
similar to those used in our previous
work~\cite{cao:762309,caowbir2010}.

The transformation $\mathbf{h(x)}$ is a cubic B-splines transform:
\begin{equation}
  \mathbf{h(x)}=\mathbf{x}+\sum_{i\in G}\phi_i \beta^{(3)}(\mathbf{x}-\mathbf{x}_i)
  \label{eq:param},
\end{equation}
where $\mathbf{\phi}_i$ describes the displacements of the control
nodes and $\beta^{(3)}(\mathbf{x})$ is a three-dimensional tensor
product of basis functions of cubic B-Spline. A spatial
multiresolution procedure from coarse to fine is used in the
registration in order to improve speed, accuracy and robustness. The
total cost in Equation~\ref{eq:total_cost} is optimized using a
limited-memory, quasi-Newton minimization method with bounds
(L-BFGS-B)~\cite{lbfgsb} algorithm. The B-Splines coefficients are
constrained so that the transformation maintains the topology using
the sufficient conditions that guarantee the local injectivity of
functions parameterized by uniform cubic B-Splines proposed by Choi
and Lee~\cite{Choi00injectivityconditions}.

\subsection{Regional Ventilation Measures from Image Registration}
\label{sect:meth_regvent} After we obtain the optimal warping
function $\mathbf{h(x)}$, we can calculate the regional ventilation,
which is equal to the difference in local air volume change per unit
time. The commonly-used ventilation measure is the specific
ventilation sV which takes the initial air volume into account. The
sV is equal to the specific air volume change sVol per unit time. Or
in other words, in a unit time,
\begin{equation}
\label{eq:sVol} sV=
sVol=\frac{V'_1(\mathbf{h(x)})-V'_2(\mathbf{x})}{V'_2(\mathbf{x})}.
\end{equation}
Three different approaches for estimating~(\ref{eq:sVol}) are
described below:

\paragraph{Specific air volume change by specific volume change (SAJ):}
The SAJ regional ventilation measure is based on the assumption that
local volume change is due to air volume change alone, and thus, any
regional volume change is due only to local air volume change.  Or,
equivalently, the SAJ measure assumes that there is no tissue volume
within the template or target volumes.
Figure~\ref{fig:airtissuevolume_saj} illustrates such an assumption.
Compared with the general condition in
Figure~\ref{fig:airtissuevolume}, the region volume now is pure air
volume, or equivalently,  $\nu_1=V'_1$ and $\nu_2=V'_2$. In this
case, the specific air volume change is equal to specific volume
change. Since the Jacobian tells us the local volume expansion (or
contraction), the regional ventilation can be measured by:
\begin{equation}
\label{eq:SAJ} SAJ=\frac{\nu _1(\mathbf{h(x)})-\nu
_2(\mathbf{x})}{\nu _2(\mathbf{x})}
=J(\mathbf{x})-1.
\end{equation}

Previously, SAJ has been used as an index of the regional function
and was compared with Xe-CT estimates of regional lung
function~\cite{Reinhardt2008752}. Regional lung expansion, as
estimated from the Jacobian of the image registration
transformations, was well correlated with xenon CT specific
ventilation~\cite{Reinhardt2008752,ding2009} (linear regression,
average $r^2=0.73$).

\begin{figure}[tbp]
  \centering
  \includegraphics[width=0.8\textwidth]{airtissuevolume_saj.eps}
  \caption{Example of a given region under deformation
  $\mathbf{h(x)}$ from template image to target image, with the
  assumption of no tissue volume ($V_1 = V_2 = 0$). $V'_1$ and
$V'_2$ are air volumes.}
  \label{fig:airtissuevolume_saj}
\end{figure}

\paragraph{Specific air volume change by corrected Jacobian (SACJ):}
Starting with~(\ref{eq:SAJ}) and expressing the air volumes
$V'_1(\mathbf{h(x)})$ and $V'_2(\mathbf{x})$ using the air-tissue
mixture model (\ref{eq:TissueVolume}) and~(\ref{eq:AirVolume}), we
obtain the corrected Jacobian measure of region air volume change,
SACJ,
\begin{eqnarray}
SACJ&=&\frac{V'_1(\mathbf{h(x)})-V'_2(\mathbf{x})}{V'_2(\mathbf{x})}\\
&=&\frac{V'_1(\mathbf{h(x)})}{V'_2(\mathbf{x})}-1\\
&=&\frac{\nu_1(\mathbf{h(x)})\alpha(I_1(\mathbf{h(x)}))}{\nu_2(\mathbf{x})\alpha(I_2(\mathbf{x}))}-1
\end{eqnarray}

As $\nu_1(\mathbf{h(x)})=J(\mathbf{x}) \nu_2 (\mathbf{x})$, the
specific air volume change is then

\begin{eqnarray}
SACJ
&=&J(\mathbf{x})\frac{\alpha(I_1(\mathbf{h(x)}))}{\alpha(I_2(\mathbf{x}))}-1\\
\label{eq:General_SACJ}
&=&J(\mathbf{x})\frac{HU_{tissue}-I_1(\mathbf{h(x)})}{HU_{tissue}-I_2(\mathbf{x})}-1
\end{eqnarray}
If we assume that pure air is -1000 HU and pure tissue is 0 HU, then
specific air volume change is
\begin{eqnarray}
\label{eq:SACJ}
SACJ&=&J(\mathbf{x})\frac{I_1(\mathbf{h(x)})}{I_2(\mathbf{x})}-1.
\end{eqnarray}
Compared to Equation~\ref{eq:SAJ}, the term
$\frac{I_1(\mathbf{h(x)})}{I_2(\mathbf{x})}$ is a correction factor
that depends on the voxel intensities in the template and target
images.  The SACJ measure is illustrated in
Figure~\ref{fig:airtissuevolume}, and represents the most general
case of where there is both tissue volume and air volume change
within the region.

\paragraph{Specific air volume change by intensity change (SAI):}
The intensity-based measure of regional air volume change SAI can be
derived from the SACJ by assuming that tissue volume is preserved
during deformation, or equivalently, that the tissue volume
difference
${\Delta}V(\mathbf{x})=V_1(\mathbf{h(x)})-V_2(\mathbf{x})=0$. Under
this assumption, $V_1(\mathbf{h(x)})=V_2(\mathbf{x})$ and we have
\begin{equation}
\nu_1(\mathbf{h(x)})\beta(I_1(\mathbf{h(x)}))=\nu_2(\mathbf{x})\beta(I_2(\mathbf{x})),
\end{equation}
and
\begin{equation}
\nu_1(\mathbf{h(x)})=\nu_2(\mathbf{x})\frac{\beta(I_2(\mathbf{x}))}{\beta(I_1(\mathbf{h(x)}))},
\end{equation} Since $\nu_1(\mathbf{h(x)})=J(\mathbf{x}) \nu _2 (\mathbf{x})$, with
above equation, we have
\begin{eqnarray}
\label{eq:SAI-assumption} J(\mathbf{x})
&=&\frac{\beta(I_2(\mathbf{x}))}{\beta(I_1(\mathbf{h(x)}))}\\
&=&\frac{I_2(\mathbf{x})-HU_{air}}{I_1(\mathbf{h(x)})-HU_{air}}.
\end{eqnarray}

\noindent Substituting the above equation into
equation~\ref{eq:General_SACJ}, yields
\begin{equation}
SAI
=\frac{I_2(\mathbf{x})-HU_{air}}{I_1(\mathbf{h(x)})-HU_{air}}\frac{HU_{tissue}-I_1(\mathbf{h(x)})}{HU_{tissue}-I_2(\mathbf{x})}-1\\
\end{equation}

\begin{equation}
\label{eq:General_SAI}
=\frac{I_2(\mathbf{x})HU_{tissue}+HU_{air}I_1(\mathbf{h(x)})-I_1(\mathbf{h(x)})HU_{tissue}-HU_{air}I_2(\mathbf{x})}{(I_1(\mathbf{h(x)})-HU_{air})(HU_{tissue}-I_2(\mathbf{x}))}
\end{equation}

\noindent Finally, if we assume that pure air is -1000 HU and pure
tissue is 0 HU, then
\begin{equation}
\label{eq:SAI} SAI
=1000\frac{I_1(\mathbf{h(x)})-I_2(\mathbf{x})}{I_2(\mathbf{x})(I_1(\mathbf{h(x)})+1000)}
\end{equation}

\noindent which is exactly the result from Simon~\cite{Simon2000},
Guerrero et al.~\cite{Guerrero2005630}, and Fuld et
al.~\cite{Fuld04012008}.

Figure~\ref{fig:airtissuevolume_sai} illustrates the assumption with
no tissue volume change in SAI\@. In
Figure~\ref{fig:airtissuevolume_sai} as the region volume changes
from $\nu_1$ to $\nu_2$, the tissue volume inside the cube remains
the same ($V_1=V_2$).
\begin{figure}[tbp]
  \centering
  \includegraphics[width=0.8\textwidth]{airtissuevolume_sai.eps}
  \caption{Example of a given voxel under deformation
  $\mathbf{h(x)}$ from template image to target image, with the
  assumption of no tissue volume change. Notice the tissue volume
  $V_1=V_2$ under this assumption. $V'_1$ and
$V'_2$ are air volumes.}
  \label{fig:airtissuevolume_sai}
\end{figure}

\paragraph{Difference of specific air volume change (DSA) and difference of tissue volume (DT):}
To investigate the relationship between the measurements of specific
air volume changes and the tissue volume change, we can calculate
the difference between equation~(\ref{eq:General_SACJ}) and
equation~(\ref{eq:General_SAI}) and define the difference of
specific air volume change (DSA) between SACJ and SAI, and the
difference of tissue volume (DT) as:

\begin{equation}
DSA = |SACJ - SAI|
\end{equation}

\begin{eqnarray}
DT &=& |V_1(\mathbf{h(x)})-V_2(\mathbf{x})|\\
&=& |\nu_1(\mathbf{h(x)})\beta(I_1(\mathbf{h(x)}))-\nu_2(\mathbf{x})\beta(I_2(\mathbf{x}))|\\
&=& |J(\mathbf{x})\nu_2(\mathbf{x})\beta(I_1(\mathbf{h(x)}))-\nu_2(\mathbf{x})\beta(I_2(\mathbf{x}))|\\
&=&
|\nu_2(\mathbf{x})\frac{J(\mathbf{x})(I_1(\mathbf{h(x)})-HU_{air})-(I_2(\mathbf{x})-HU_{air})}{HU_{tissue}-HU_{air}}|
\end{eqnarray}

\noindent Again, if we assume that air is -1000 HU and tissue is 0
HU, then the tissue volume difference is:
\begin{equation}
DT
=|\nu_2(\mathbf{x})\frac{J(\mathbf{x})(I_1(\mathbf{h(x)})+1000)-(I_2(\mathbf{x})+1000)}{1000}|
\end{equation}

\subsection{Computational Setup}
Processing starts by identifying the lung regions in all images
using the Pulmonary Workstation 2.0 (VIDA Diagnostics, Inc., Iowa
City, IA). The Xe-CT estimate of sV is computed in the coordinates
of the \eeinit{} using Pulmonary Analysis Software Suite 11.0
(PASS)~\cite{guo2008} at the original image size of 0.5 mm $\times$
0.5 mm $\times$ 2.4 mm voxels. Overlapping 1 $\times$ 8 regions of
interest (ROI) are defined in the lung region on each 2D slice. All
images, including the \ee{}, \ei{}, \eeinit{} and their
corresponding lung region masks or sV map, are then resampled to a
voxel size of 1 mm $\times$ 1 mm $\times$ 1 mm. After preprocessing,
\ei{} is registered to \ee{} using the TVP registration for measuring the
regional ventilation from these two phases in a 4DCT scan. The
resulting transformation is used to estimate the SAJ, SACJ and SAI.
Then \eeinit{} is registered to \ee{} using TVP registration as well to map the
sV to the same coordinate system as that of the SAJ, SACJ and SAI.
Due to the fact that the denominator of equation
(\ref{eq:General_SACJ}) and (\ref{eq:General_SAI}) may become zero,
we eliminated from consideration any points that have the absolute
value of the denominator become less than $0.001$. For the TVP registration, the
multiresolution strategy is used in the processing and it proceeds
from low to high image resolution starting at one-eighth the spatial
resolution and increases by a factor of two until the full
resolution is reached. Meanwhile, a hierarchy of B-spline grid
spaces from large to small is used. The finest B-spline grid space
used in the experiments is 4~mm. The images and image grid space are
refined alternatively.

\subsection{Assessment of image registration accuracy}
A semi-automatic landmark system is used for landmark selection and
matching~\cite{murphy2008}. This system first uses an automatic
landmark detection algorithm to find the landmarks in the \ee{}
image. The algorithm automatically detects ``distinctive'' points in
the target image as the landmarks based on a distinctiveness value
$D(p)$. Around each point $p$, 45 points, $q_1, \dots, q_{45}$ are
uniformly distributed on a spherical surface. A region of interest
$ROI(q_i)$ is compared with the corresponding region of interest
$ROI(p)$ around the original point, and then combined with its
gradient value to calculate the distinctiveness value $D(p)$.

The same system is then applied to guide the observer to match
landmarks in the target image with corresponding landmarks in the
template image. Each landmark-pair manually annotated by the
observer is added to a thin-plate-spline to warp the template image.
The system utilizes the warped image to estimate where the anatomic
match will be located for a new landmark point presented to the
observer, therefore the observer can start the matching from a
system estimated location. Thus, as the warped image becomes more
accurate by the new added landmarks, the task of the observer
becomes easier.

For each animal, after 200 anatomic landmarks are identified in the
\ee{} image, the observer marks the locations of the voxels corresponding
to the anatomic locations of the landmarks in the \ei{} image. For each
landmark, the actual landmark position is compared to the
registration-derived estimate of landmark position and the error is
calculated. With the evaluated accuracy of transformation from the
lung image registration algorithm, the resulting regional
ventilation measures estimated using the transformation can be then
compared to Xe-CT estimated ventilation.

\subsection{Compare Registration Regional Ventilation \\Measures to Xe-CT Estimated Ventilation}
In our previous work~\cite{Reinhardt2008752,ding2009}, regional lung
expansion, as estimated from the Jacobian of the image registration
transformations, was compared with Xe-CT-based sV.  The
analysis was conducted by evaluating Jacobian value between a pair
of lung volumes from static (multiple airway pressures) and dynamic
image data sets, and comparing the Jacobian along the y
(ventral-dorsal) axis.  While the correlation between the
Jacobian value and sV reflect the fact that regional expansion
estimated from image registration can be used as an index as
regional lung function, the spatial resolution of the analysis
method employed were likely not sufficient to distinguish the
differences between regional ventilation measures as we have
described in Section~\ref{sect:meth_regvent}. Therefore, to locally
compare the regional ventilation measures, the corresponding region
of Xe-CT image \eeinit{} in the \ee{} is divided into approximately
100 non-overlapping cubes with size of 20 mm $\times$ 20 mm $\times$
20 mm. We compare the average regional ventilation measures (SAJ,
SACJ and SAI) to the corresponding average sV measurement from Xe-CT
images within each cube. The correlation coefficients between any
two estimates (SAJ-sV, SACJ-sV or SAI-sV) are calculated by linear
regression. To compare two correlation coefficients, the Fisher
Z-transform of the $r$ values is used and the level of significance
is determined~\cite{papoulis1990}. The relationship between the
specific air volume change and difference of tissue volume is also
studied in four animals by linear regression analysis.

\section{Results} \label{sec:results}
\subsection{Registration Accuracy}
For each animal, approximately 200 automatically identified
landmarks within the lungs are used to compute registration
accuracy. The landmarks are widely distributed throughout the lung
regions. Figure~\ref{fig:xe3d} shows an example of the distribution
of the landmarks in animal D for both the \ee{} and \ei{} images.
The coordinate of each landmark location is recorded for each image
data set before and after registration for all four animals.
Figure~\ref{fig:lmkerror} shows the landmark distance before and
after registration for four animals. The grey boxes show the
magnitude of respiratory motion during the tidal breathing. For all
four animals, before registration, the average landmark distance is
6.6 mm with a minimum distance of 1.0 mm, maximum distance of 14.6
mm, and standard deviation of 2.42 mm. After registration, the
average landmark distance is 0.4 mm with a minimum distance of 0.1
mm, a maximum distance of 1.6 mm, and a standard deviation 0.29 mm.
The trends for all animals are consistent and the results
demonstrate that the registrations produced good anatomic
correspondences. All registrations were examined and it was
confirmed that all Jacobian values had positive values.

Figure~\ref{fig:xe3d}(a) shows the location of the \eeinit{} (Xe-CT)
slab overlaid on the \ee{} image. Figure~\ref{fig:xe-0in-error}
shows an example of the image registration result from the \eeinit{}
image to the \ee{} image. The first row shows the misalignment
between the images before image registration. Though the images were
acquired without moving the animal between the scans, there is still
non-rigid deformation between scans as shown in
Fig~\ref{fig:xe-0in-error}(d), as the black and white regions
represent the large intensity difference between
Fig.~\ref{fig:xe-0in-error}(a) and (b).  In addition, the
slice thicknesses were quite different which causes partial volume
artifacts. After image registration, the \eeinit{} image is
aligned to the \ee{} image, and the resulting difference image
(shown in Fig.~\ref{fig:xe-0in-error}(e)) is near zero. The
transformation from the \eeinit{} to the \ee{} image allows us to
map the Xe-CT sV into the coordinate system of \ee{} image. Note
that since the regions outside the lung are not included in the
registration process, the mediastinum and other body tissues are not
aligned. Also note that the dorsal region of the lung shows a
intensity difference after registration. This is due mainly to the
gradual progression of atelectasis and tissue edema during the
course of the experiment.

\begin{figure}[htbp]
  \centering
  \begin{tabular}{cc}
  \includegraphics[width=0.52\textwidth]{AS70080_0IN_Sagittal3D.eps}
  &
  \includegraphics[width=0.52\textwidth]{AS70080_100IN_Sagittal3D.eps}\\
  (a)&(b)\\
  \end{tabular}
  \caption{3D view of the landmarks in: (a) \ee{} with \eeinit{} and (b) \ei{}. The dark region below the carina in (a) is the \eeinit{} and the spheres are the automatically defined landmarks.}
  \label{fig:xe3d}
\end{figure}

\begin{figure}[htbp]
  \centering
  \begin{tabular}{c}
    \includegraphics[width=0.8\textwidth]{regvent-lmkerror.eps}
  \end{tabular}
  \caption{Landmarks distances of the registration pair \ei{} to \ee{} for all four animals.  Boxplot lower extreme is first quartile,
  boxplot upper extreme is third quartile.  Median is shown with solid horizontal
line.   Whiskers show either the minimum (maximum) value or extend
1.5 times the first to third quartile range beyond the lower (upper)
extreme of the box, whichever is smaller (larger). Outliers are
marked with circles.}
  \label{fig:lmkerror}
\end{figure}

\begin{figure}[p]
  \centering
  \begin{tabular}{cc}
    \includegraphics[width=0.38\textwidth]{AS70080_0IN_t180.eps}\\
    (a)\\
    \includegraphics[width=0.38\textwidth]{AS70080_XE_BeforeReg_t180.eps}
    &
    \includegraphics[width=0.38\textwidth]{AS70080_XE_AfterReg_t180.eps}
    \\
    (b)&(c)\\
    \includegraphics[width=0.38\textwidth]{AS70080_XE_To_0IN_BeforeReg_Diff_t180.eps}
    &
    \includegraphics[width=0.38\textwidth]{AS70080_XE_To_0IN_AfterReg_Diff_t180.eps}
    \\
    (d)&(e)\\
  \end{tabular}
  \caption{Visualization of the result of the transformation that
  maps the Xe-CT estimated ventilation sV to the \ee{} coordinate system:
  (a) \ee{}, (b) \eeinit{}, (c) deformed \eeinit{} after registration,
  (d) intensity difference between \ee{} and \eeinit{}
  before registration, (e) intensity difference
  between \ee{} and \eeinit{} after
registration.}
   \label{fig:xe-0in-error}
\end{figure}

\subsection{Registration Estimated Ventilation Compared \\to Xe-CT Estimated Ventilation}
Figure~\ref{fig:vent}(a) shows a comparison between the
registration-derived indices of ventilation and the Xe-CT estimated
sV in cube-shaped regions of interest for animal D. The
corresponding Xe-CT regions in the \ee{} are divided into about 100
cubes. Figure~\ref{fig:vent}(b) is the Xe-CT estimate of sV.
Figure~\ref{fig:vent}(c), (d), (e) are the corresponding
registration ventilation measures SAJ, SACJ and SAI\@. The regions
with edema are excluded from the comparison.
Figure~\ref{fig:vent}(b) to (d) all show noticeable similar gradient
in the ventral-dorsal direction. Notice that the color scales are
different in each map and are set based on the range of values from
the appropriate plot in Fig.~\ref{fig:regvent-xevent-cuberoi}.

Figure~\ref{fig:regvent-xevent-cuberoi} shows scatter plots
comparing the registration ventilation measures and the Xe-CT
ventilation sV in all four animals. The SACJ
column shows the strongest correlation with the sV (average
$r^2=0.82$). The SAJ, which is directly related to Jacobian as
$\mathrm{SAJ}=\mathrm{J}-1$, also shows good correlation with the sV
(average $r^2=0.75$). The intensity-based measure SAI shows the
lowest correlation with the sV (average $r^2=0.58$).

Table~\ref{tbl:pvalue-cuberoi-sacj} shows the results of comparing
the $r$ values from SACJ vs.\ sV and SAI vs.\ sV. All four animals show
that the correlation coefficient from SACJ vs.\ sV is significantly
stronger than it from SAI vs.\ sV. Similarly,
table~\ref{tbl:pvalue-cuberoi-saj} shows the results of comparing
the r values from SAJ vs.\ sV and SAI vs.\ sV. The registration
ventilation measure SAJ also shows a significantly stronger
correlation with sV than SAI\@. Comparing the r values from SACJ vs.\ sV and SAJ vs.\ sV, only animal B and C show that the SACJ has significantly stronger correlation with sV than SAJ.

To analyze the effect of the size of the region of interest, the
corresponding region of Xe-CT image \eeinit{} in the \ee{} is
divided into about 30 slabs along the ventral-dorsal direction with
size of 150 mm $\times$ 8 mm $\times$ 40 mm as similarly in our
previous work~\cite{Reinhardt2008752,ding2009}.
Figure~\ref{fig:regvent-xevent-slabroi} shows the scatter plots
between the registration ventilation measures and the Xe-CT
ventilation sV similar as Fig.~\ref{fig:regvent-xevent-slabroi} but
in larger ROIs. The SACJ column shows the strongest correlation with
the sV (average $r^2=0.92$). Both the SAJ and SAI show good
correlation with sV as well (average $r^2=0.88$ and $r^2=0.87$).
However, though the average $r^2$ value still show the SACJ has the
highest correlation with Xe-CT based sV,
Table~\ref{tbl:pvalue-slabroi-sacj} and~\ref{tbl:pvalue-slabroi-saj}
show that with larger averaging
region as defined slabs, there is no significant difference between
the correlation coefficients from SACJ vs.\ sV and SAI vs.\ sV, between SAJ vs.\ sV and SAI vs.\ sV, or SACJ vs.\ sV and SAJ vs.\ sV as in Table~\ref{tbl:pvalue-slabroi-sacj_vs_saj}.

Figure~\ref{fig:sair-tissue} shows the scatter plots between DSA
(the absolute difference of the value between the SACJ and SAI) and
the DT (the absolute difference of the tissue volume) with linear
regression in all four animals (average $r^2=0.86$). From the
equation (\ref{eq:General_SACJ}) and (\ref{eq:General_SAI}), we know
that the SAI measurement assumes no tissue volume change in a given
region being registered, which may not be valid assumption in all
lung regions. Figure~\ref{fig:sair-tissue} shows that as the tissue
volume change increases, the difference between the measures of
regional ventilation from SACJ and SAI increases linearly in all
four animals. It indicates that the lower correlation of SAI with sV
compared with SACJ with sV may be caused by the tissue volume change
between two volumes.
\begin{figure}[p]
  \centering
  \begin{tabular}{cc}
    \includegraphics[width=0.35\textwidth]{AS70080_Cube.eps}\\
    (a)\\
    \includegraphics[width=0.35\textwidth]{AS70080_SV.eps}
    &
    \includegraphics[width=0.35\textwidth]{AS70080_SAJ.eps}\\
    (b)&(c)\\
    \includegraphics[width=0.35\textwidth]{AS70080_SACJ.eps}
    &
    \includegraphics[width=0.35\textwidth]{AS70080_SAI.eps}\\
    (d)&(e)\\
  \end{tabular}
  \caption{Comparison of the regional ventilation measures for animal D.
           (a): \ee{} with color coded cubes showing the sample region.
           (b), (c), (d) and (e): color map of the sV, SAJ, SACJ and SAI.
           Note that the color scales are different for (b)-(e), and are set
           based on the range of values from the appropriate plot in Fig.~\ref{fig:regvent-xevent-cuberoi}. The results were similar for the other three animals}
   \label{fig:vent}
\end{figure}

\begin{figure}[p]
  \centering
  \begin{tabular}{ccc}
    \includegraphics[width=0.28\textwidth]{AS70077_SAJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70077_SACJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70077_SAI_vs_SV_min.eps}\\
    ~&Animal A&~\\
    \includegraphics[width=0.28\textwidth]{AS70078_SAJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70078_SACJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70078_SAI_vs_SV_min.eps}\\
    ~&Animal B&~\\
    \includegraphics[width=0.28\textwidth]{AS70079_SAJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70079_SACJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70079_SAI_vs_SV_min.eps}\\
    ~&Animal C&~\\
    \includegraphics[width=0.28\textwidth]{AS70080_SAJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70080_SACJ_vs_SV_min.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70080_SAI_vs_SV_min.eps}\\
    ~&Animal D&~\\
  \end{tabular}
  \caption{Small cube ROIs with size 20 mm $\times$ 20 mm $\times$ 20
    mm results for registration estimated ventilation measures
    compared to the Xe-CT estimated ventilation sV in scatter plot
    with linear regression in four animals. The first column is the
    SAJ vs.\ sV. The second column is the SACJ vs.\ sV. The third column
    is the SAI vs.\  sV.}
  \label{fig:regvent-xevent-cuberoi}
\end{figure}

\begin{figure}[p]
  \centering
  \begin{tabular}{ccc}
    \includegraphics[width=0.28\textwidth]{AS70077_SAJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70077_SACJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70077_SAI_vs_SV_min_slabroi.eps}\\
    ~&Animal A&~\\
    \includegraphics[width=0.28\textwidth]{AS70078_SAJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70078_SACJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70078_SAI_vs_SV_min_slabroi.eps}\\
    ~&Animal B&~\\
    \includegraphics[width=0.28\textwidth]{AS70079_SAJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70079_SACJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70079_SAI_vs_SV_min_slabroi.eps}\\
    ~&Animal C&~\\
    \includegraphics[width=0.28\textwidth]{AS70080_SAJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70080_SACJ_vs_SV_min_slabroi.eps}
    &
    \includegraphics[width=0.28\textwidth]{AS70080_SAI_vs_SV_min_slabroi.eps}\\
    ~&Animal D&~\\
  \end{tabular}

  \caption{Large slab ROIs with size 150 mm $\times$ 8 mm $\times$ 40
    mm results for registration estimated ventilation measures
    compared to the Xe-CT estimated ventilation sV in scatter plot
    with linear regression in four animals. The first column is the
    SAJ vs.\ sV. The second column is the SACJ vs.\ sV. The third column
    is the SAI vs.\  sV.}
  \label{fig:regvent-xevent-slabroi}
\end{figure}

\begin{table}
\centering

\begin{tabular}{ccccc}
\toprule
\multicolumn{ 1}{c}{Animal} & Correlation  & Correlation with sV & Number of  & \multicolumn{ 1}{c}{SACJ vs.\ SAI} \\

\multicolumn{ 1}{c}{} &  pair &  ($r$ value) &    samples & \multicolumn{ 1}{c}{$p$ value} \\
\midrule
\multicolumn{ 1}{c}{A} &      SACJ vs.\ sV &       0.88 & \multicolumn{ 1}{c}{83} & \multicolumn{ 1}{c}{$p<=0.0001$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.65 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{B} &      SACJ vs.\ sV &       0.93 & \multicolumn{ 1}{c}{119} & \multicolumn{ 1}{c}{$p<=1.18e^{-6}$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.77 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{C} &      SACJ vs.\ sV &       0.89 & \multicolumn{ 1}{c}{86} & \multicolumn{ 1}{c}{$p<=0.0075$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.78 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{D} &      SACJ vs.\ sV &       0.92 & \multicolumn{ 1}{c}{110} & \multicolumn{ 1}{c}{$p<=0.0017$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.83 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\
 \bottomrule
\end{tabular}
\caption{Comparison of ventilation measures between SACJ and
  SAI in small cube ROIs with size 20 mm $\times$ 20 mm
  $\times$ 20 mm.}
\label{tbl:pvalue-cuberoi-sacj}
\end{table}

\begin{table}
\centering

\begin{tabular}{ccccc}
\toprule
\multicolumn{ 1}{c}{Animal} & Correlation & Correlation with sV & Number of  & \multicolumn{ 1}{c}{SAJ vs.\ SAI} \\

\multicolumn{ 1}{c}{} &  pair &  ($r$ value) &    samples & \multicolumn{ 1}{c}{$p$ value} \\
\midrule
\multicolumn{ 1}{c}{A} &      SAJ vs.\ sV &       0.86 & \multicolumn{ 1}{c}{83} & \multicolumn{ 1}{c}{$p<=0.0005$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.65 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{B} &      SAJ vs.\ sV &       0.89 & \multicolumn{ 1}{c}{119} & \multicolumn{ 1}{c}{$p<=0.002$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.77 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{C} &      SAJ vs.\ sV &       0.78 & \multicolumn{ 1}{c}{86} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.78 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{D} &      SAJ vs.\ sV &       0.92 & \multicolumn{ 1}{c}{110} & \multicolumn{ 1}{c}{$p<=0.0017$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.83 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\
\bottomrule
\end{tabular}
\caption{Comparison of ventilation measures between SAJ and
SAI in small cube ROIs with size 20 mm $\times$ 20 mm
$\times$ 20 mm.}
\label{tbl:pvalue-cuberoi-saj}
\end{table}

\begin{table}
\centering
\begin{tabular}{ccccc}
\toprule
\multicolumn{ 1}{c}{Animal} & Correlation  & Correlation with sV & Number of  & \multicolumn{ 1}{c}{SACJ vs.\ SAJ} \\

\multicolumn{ 1}{c}{} &  pair &  ($r$ value) &    samples & \multicolumn{ 1}{c}{$p$ value} \\
\midrule
\multicolumn{ 1}{c}{A} &      SACJ vs.\ sV &       0.88 & \multicolumn{ 1}{c}{83} & \multicolumn{ 1}{c}{$p<=0.302$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.86 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{B} &      SACJ vs.\ sV &       0.93 & \multicolumn{ 1}{c}{119} & \multicolumn{ 1}{c}{$p<=0.035$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.89 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{C} &      SACJ vs.\ sV &       0.89 & \multicolumn{ 1}{c}{86} & \multicolumn{ 1}{c}{$p<=0.007$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.78 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{D} &      SACJ vs.\ sV &       0.92 & \multicolumn{ 1}{c}{110} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.92 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\
 \bottomrule
\end{tabular}
\caption{Comparison of ventilation measures between SACJ and SAJ in small cube ROIs with size 20 mm $\times$ 20 mm
  $\times$ 20 mm.}
\label{tbl:pvalue-cuberoi-sacj_vs_saj}
\end{table}

\begin{table}
\centering

\begin{tabular}{ccccc}
\toprule
\multicolumn{ 1}{c}{Animal} & Correlation & Correlation with sV & Number of  & \multicolumn{ 1}{c}{SACJ vs.\ SAI} \\

\multicolumn{ 1}{c}{} &  pair &  ($r$ value) &    samples & \multicolumn{ 1}{c}{$p$ value} \\
\midrule
\multicolumn{ 1}{c}{A} &      SACJ vs.\ sV &       0.95 & \multicolumn{ 1}{c}{17} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.95 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{B} &      SACJ vs.\ sV &       0.99 & \multicolumn{ 1}{c}{23} & \multicolumn{ 1}{c}{$p<=0.005$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.95 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{C} &      SACJ vs.\ sV &       0.94 & \multicolumn{ 1}{c}{23} & \multicolumn{ 1}{c}{$p<=0.15$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.89 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{D} &      SACJ vs.\ sV &       0.95 & \multicolumn{ 1}{c}{25} & \multicolumn{ 1}{c}{$p<=0.28$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.93 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\
\bottomrule
\end{tabular}
\caption{Comparison of ventilation measures between SACJ and
SAI in large slab ROIs with size 150 mm $\times$ 8 mm
$\times$ 40 mm.}
\label{tbl:pvalue-slabroi-sacj}
\end{table}

\begin{table}
\centering

\begin{tabular}{ccccc}
\toprule
\multicolumn{ 1}{c}{Animal} & Correlation  & Correlation with sV & Number of  & \multicolumn{ 1}{c}{SAJ vs.\ SAI} \\

\multicolumn{ 1}{c}{} &  pair &  ($r$ value) &    samples & \multicolumn{ 1}{c}{$p$ value} \\
\midrule
\multicolumn{ 1}{c}{A} &      SAJ vs.\ sV  &       0.95 & \multicolumn{ 1}{c}{17} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.95 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{B} &      SAJ  vs.\ sV &       0.99 & \multicolumn{ 1}{c}{23} & \multicolumn{ 1}{c}{$p<=0.005$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.95 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{C} &      SAJ  vs.\ sV &       0.94 & \multicolumn{ 1}{c}{23} & \multicolumn{ 1}{c}{$p<=0.16$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.89 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{D} &      SAJ  vs.\ sV &       0.95 & \multicolumn{ 1}{c}{25} & \multicolumn{ 1}{c}{$p<=0.28$} \\

\multicolumn{ 1}{c}{} &        SAI vs.\ sV &       0.93 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\
\bottomrule
\end{tabular}
\caption{Comparison of ventilation measures between SAJ and
SAI in large slab ROIs with size 150 mm $\times$ 8 mm
$\times$ 40 mm.}
\label{tbl:pvalue-slabroi-saj}
\end{table}

\begin{table}
\centering

\begin{tabular}{ccccc}
\toprule
\multicolumn{ 1}{c}{Animal} & Correlation & Correlation with sV & Number of  & \multicolumn{ 1}{c}{SACJ vs.\ SAI} \\

\multicolumn{ 1}{c}{} &  pair &  ($r$ value) &    samples & \multicolumn{ 1}{c}{$p$ value} \\
\midrule
\multicolumn{ 1}{c}{A} &      SACJ vs.\ sV &       0.95 & \multicolumn{ 1}{c}{17} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.95 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{B} &      SACJ vs.\ sV &       0.99 & \multicolumn{ 1}{c}{23} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.99 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{C} &      SACJ vs.\ sV &       0.94 & \multicolumn{ 1}{c}{23} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.94 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\*[10pt]

\multicolumn{ 1}{c}{D} &      SACJ vs.\ sV &       0.95 & \multicolumn{ 1}{c}{25} & \multicolumn{ 1}{c}{$p<=0.5$} \\

\multicolumn{ 1}{c}{} &        SAJ vs.\ sV &       0.95 & \multicolumn{ 1}{c}{} & \multicolumn{ 1}{c}{} \\
\bottomrule
\end{tabular}
\caption{Comparison of ventilation measures between SACJ and
SAJ in large slab ROIs with size 150 mm $\times$ 8 mm
$\times$ 40 mm.}
\label{tbl:pvalue-slabroi-sacj_vs_saj}
\end{table}

\begin{figure}[p]
  \centering
  \begin{tabular}{cc}
    \includegraphics[width=0.5\textwidth]{AS70077_sAirDiff_vs_TissueDiff.eps}
    &
    \includegraphics[width=0.5\textwidth]{AS70078_sAirDiff_vs_TissueDiff.eps}\\
    (a)&(b)\\
    \includegraphics[width=0.5\textwidth]{AS70079_sAirDiff_vs_TissueDiff.eps}
    &
    \includegraphics[width=0.5\textwidth]{AS70080_sAirDiff_vs_TissueDiff.eps}\\
    (c)&(d)\\
  \end{tabular}
  \caption{Linear regression analysis between DSA and DT. (a) to (d): DSA (the absolute difference of the value between the SACJ and SAI) compared to DT (the absolute difference of the tissue volume) in animals A, B, C and D.}
   \label{fig:sair-tissue}
\end{figure}

\section{Summary and Conclusions}
\label{sec:discussion}   We described three measures to estimate
regional lung tissue ventilation from tissue volume and vesselness
preserving image registration of CT images.  These measures have
been compared with each other, and compared to Xe-CT estimates of
specific ventilation.  We examined the assumption of constant tissue
volume between registered lung regions, and demonstrated that the
difference between two of the registration derived measures (SACJ
and SAI) may be explained by differences in tissue volume between
the lung regions being compared with registration.

The tissue volume and vesselness preserving non-linear registration
algorithm was used to match the \ei{} image to the \ee{} image to
produce the registration deformation field and estimates of regional
ventilation. It was used to register the \eeinit{} image to the
\ee{} image for comparing the three ventilation measures to the
Xe-CT based sV. About 200 anatomical landmarks were identified and
annotated in each data set to evaluate registration accuracy. The
average landmark error is on the order of 1 mm after registration.

The ventilation measures SAJ, SACJ, and SAI were derived using a
simple model of a lung region, containing a mixture of air and
tissue, which deforms during inspiration or exhalation.   The SAJ
measure, which is a linear function of the Jacobian of the
registration displacement field, measures regional ventilation based
on the assumption that the lung region contains only air (i.e., no
tissue volume).   The SACJ is the most general form of the
three measures and is based on model where both the air and tissue
volumes can change during inspiration and exhalation. Finally, the
SAI measure is computed based on intensity change alone, and assumes
that the region may have a tissue volume that is non-zero, but this
volume does not change during inspiration and exhalation.
Thus, the SAJ measure relies solely on the volume change information
computed from the Jacobian of the deformation field and the SAI
measure relies solely on the change in region intensity as measured
by the CT\@. The SACJ measure uses a more complete model of local
ventilation, and combines the geometric information from the
Jacobian with the density information calculated from the change in
region intensity.

The three registration-based ventilation measures and the Xe-CT sV
measurement were averaged and compared in cubic-shaped regions of
interest. In 20 mm $\times$ 20 mm $\times$ 20 mm ROIs, the SACJ
shows significantly higher correlation with Xe-CT sV than the
SAI in all four animals. By studying the difference between
the SACJ and SAI measures and the tissue volume difference
estimated by the CT intensity change, we showed that the difference
between SACJ and SAI may be explained by the constant tissue volume
assumption implicit in the SAI model~(\ref{eq:SAI-assumption}).
From Fig.~\ref{fig:sair-tissue}, we see that the difference between
the SACJ and SAI measures is approximately linearly-related to the
estimated tissue volume change.
Tables~\ref{tbl:pvalue-cuberoi-sacj} and~\ref{tbl:pvalue-cuberoi-saj}
show that the both the SACJ and
SAJ have significantly better correlation with sV than the SAI\@.
This is consistent with the findings by Kabus et
al.~\cite{kabus2008workshop} who showed that the Jacobian-based
measure of ventilation has less error than the intensity-based ventilation
measure, using the segmented total lung volume as a global
comparison. Though all the regional ventilation measures and Xe-CT
based sV from the linear regression analysis in
Fig.~\ref{fig:regvent-xevent-slabroi} show high correlations,
Tables~\ref{tbl:pvalue-slabroi-sacj} and~\ref{tbl:pvalue-slabroi-saj}
show that there is no significant
difference in the correlation with sV between the Jacobian-based
measures and intensity-based measure. This result indicates that the
validation methods using global comparison such as segmented total
volume may not be able to distinguish the Jacobian-based measure and
the intensity-based measure.

The comparison of the ventilation measures was limited to the
resolution of 20 mm $\times$ 20 mm $\times$ ROIs. As the size of the
ROIs decreases, the correlation between the ventilation measures
with Xe-CT based sV decreases.  This may be due to the
underlying noise of the Xe-CT measurement of ventilation or the
decreased sensitivity of registration based measure to local
ventilation heterogeneity which is relative to the case.
Additional Xe-CT image analysis work including using
multi-compartment models, thinner slice, and inter-phase
registration to improve sV measurement are required to reduce the
noise in Xe-CT based sV measurement.

To compare with the intensity-based ventilation measure used in
previous work in Simon~\cite{Simon2000}, Guerrero et
al.~\cite{Guerrero2005630}, and Fuld et al.~\cite{Fuld04012008}, we
followed the assumption that $HU_{air}$ is -1000 HU and
$HU_{tissue}$ is 0 HU (equaling water~\cite{Hoffman1985}) in this
work. The ventilation measures were calculated under the assumption
that $HU_{air}$ is -1000 HU and $HU_{tissue}$ is 55 HU, which are
the values used by Yin et al.\ in~\cite{yin:4213}. Our analysis shows
that the correlation coefficients between any two estimates (SAJ-sV,
SACJ-sV or SAI-sV) change less than $1\%$ with two different
$HU_{tissue}$ values. However, it would be important to have
sensitivity analysis in the future to compare different ventilation
measures against intensity changes.

The image registration algorithm used to find the transformation
from \ei{} to \ee{} for measurement of regional ventilation produces
accurate registrations by minimizing the tissue volume and
vesselness measure difference between the template image and the
target image. It would be interesting to compare different image
registration algorithms and their effects on the registration-based
ventilation measures. For example, if two registration algorithms
achieve the similar landmark accuracy, the one does not preserve
tissue volume change may show even larger difference in the SACJ and
SAI measures than the results using TVP as described above.

In conclusion, with the same deformation field by the same image
registration algorithm, a significant difference between the
Jacobian/-corrected Jacobian-based ventilation measures and the
intensity-based ventilation measure is found in a regional level
using Xe-CT based ventilation measure sV. The ventilation measure by
corrected Jacobian SACJ gives best correlation with Xe-CT based sV
and the correlation is significantly higher than from the
ventilation by intensity SAI indicating the ventilation measure by
corrected Jacobian SACJ may be a better measure of regional lung
ventilation from image registration of 4DCT images.

\section{Acknowledgments}
The authors would like to thank Ms.\ K.\ Murphy and Dr.\ B. van
Ginneken for providing the software iX for generating and annotating
landmarks. This work was supported in part by NIH grant HL079406 and
EB004126.

\vspace{2in}

redpajama_set_name: RedPajamaArXiv


"Guidance Hub was established in 2015 to serve the community of Greater Manchester. Our objective is to build a vibrant, productive and prosperous society. Focusing on: education, spirituality and community."


What a bright eye-spy quilt this was! Whichever kid gets it will have hours of fun identifying all the animals, characters, etc. on it. Of course, being so bright and 'busy', and as it will likely be well loved by a child, I decided to keep the quilting design to a basic all-over 'open headband' motif, but in a matching bright fluoro orange thread!
Even the backing fabric was bright — red, with the names of animals printed on it.
The 'headband' swirl effect is wonderful. The texture is so "yummy"!


In the past few years, our firm absorbed and digested highly developed technologies equally at home and abroad. Meanwhile, our business staffs a group of experts devoted on the growth of Small Matt Paper Bag , Small Kraft Paper Bag , Small Paper Bag , The quick Delivery.
Our goal will be to satisfy our clients by offering golden provider, great cost and top quality for Small Matt Paper Bag , Small Kraft Paper Bag , Small Paper Bag , Our company will adhere to "Quality first perfection forever people-oriented technology innovation"business philosophy. Hard work to keep making progress innovation in the industry make every effort to first-class enterprise. We try our best to build the scientific management model to learn abundant specialist knowledge to develop advanced production equipment and production process to create the first-call quality solutions reasonable price high quality of service quick delivery to provide you create new value .


I'll post my address closer to the time so you can all send your cards, presents etc.
Traum wrote: 17th.. don't forget.
Visits must be planned 2 months in advance so I can tie them in with my horrendously hectic schedule.
I'd also recommend coming a day or 2 BEFORE my actual birthday, as there will be hoards of fans turning out on the day which may cause an inconvenience for you.


Black Women's Association is a non-for-profit organization engaged in mentoring and charitable works in the Elgin community.
Utilizing our talents and resources to support the needs of our community? and the suburban area.


View of Stirling town and castle. It is not known when a castle was first built on this site however the earliest known records with references to a castle here date to the early 12th century. Generations of Scottish Kings have added to and altered the stone castle at Stirling. James III built a great hall, with 5 fireplaces and a hammer beam roof, James IV built a Renaissance style Palace with rooms encircling an open court and James VI built a beautiful chapel for the christening of his son Henry in 1594.


Je reçois des demandes de participation à la European Weta Cup par quelques équipages australiens et néo-zélandais.
Bien sûr, il serait nécessaire d'affréter le Wetas, un bateau sera disponible pour eux par moi, quelqu'un d'autre de la France serait en mesure et intéressé à la affrètement?
En outre, nous pensons à organiser une clinique Weta juste avant le championnat, pensez-vous que ce serait une bonne idée?
I am receiving requests for participation at the European Weta Cup by a couple of Australian and New Zealand crews. Of course it would be necessary to chartering the Wetas, one boat will be available for them by me, someone else from France would be able and interested to charter?
Besides, we are thinking to organize one Weta clinic just before the championship, do you think that it would be a good idea?
Hi Erwin, could you be more specific on the "weta clinic" idea?
As I wrote, it is only an idea for the time being.
In case one very experienced Weta NZ sailor (winner of the WMG with his wife) will participate to the championship, we may run with him a special course on tuning and sailing the Weta.


Should Colorado allow marijuana delivery to homes in your neighborhood?
DENVER — The Colorado legislature is debating a bill co-sponsored by Rep. Jonathan Singer, D-Longmont, that would legalize home delivery for medical marijuana starting in 2020 and recreational marijuana delivery in 2021.
Each Colorado city can choose to opt in or decide not to allow delivery under Singer's bill.
Denver7 is taking a 360 degree look at various viewpoints on this issue and what some see as pros and cons.
Medical marijuana user David LaRose says delivery would simplify his life. While in the U.S. Marine Corps, his head was smashed between a service door and the side of the airplane. He says he started having seizures shortly afterward.
"I was actually driving and had a seizure and got into a car accident," LaRose said.
Marijuana helps him have fewer seizures, he says. He is no longer allowed to drive.
There have been times during snowy weather that friends haven't been able to drive him to a dispensary. Bus service where he lives in Colorado Springs isn't convenient.
"To get to a dispensary it's a five-mile walk," he said.
LaRose suffers from PTSD and feels marijuana helps him ward off a seizure when he feels one coming on.
LaRose says it simply shouldn't be difficult to get medicine that he feels helps keep him out of the emergency room or urgent care.
"The delivery service would be absolutely perfect for me," he said.
Aurora Police Sgt. Scott Pendleton investigates the illegal side of marijuana. He worries gun-carrying criminals will intercept deliveries and go after the drugs or the cash that drivers carry.
"Inviting potential violent crime to your neighborhood or the house next door is certainly not something that's desirable," Pendleton said. "It creates yet another avenue for criminals and for people to be victimized with violent crime."
The dominant view among law enforcement is trepidation about making marijuana delivery legal.
Jason Thomas disagrees. Thomas was a member of law enforcement as a marshal in Holly, Colo. — along the Colorado-Kansas border about four hours from Denver.
He says people already deliver on the sly and a legal delivery system would make the deliveries regulated and tracked.
Thomas' view is that if people could get pot delivered to their door legally, they would stop buying it from illegal delivery drivers that exist underground.
"The fewer avenues that the black market has available to it, obviously the less demand that the black market will have," he said.
Denver businessman Tim Ruybal, of Dyspense Technologies, has built a whole software package to track, organize and run marijuana deliveries. He currently manages shipments of marijuana between dispensaries only, but wants to expand to the home delivery business if it becomes legal.
He also stands to earn cash if dispensaries that deliver marijuana use his software.
"Hundreds of millions of dollars," he said of the money he hopes to earn through home delivery.
His software tracks delivery vehicles by GPS. Every box carrying cash or marijuana also is tracked.
His software can send alerts if a driver is stopped for too long or goes on roads or highways where they shouldn't, for example, to a home that is too near a school.
Police will be able to see information if there's a crime. His delivery cars aren't marked.
Crime has not been a significant issue for him.
"There's not been any drivers that have been held up at gunpoint," Ruybal said.
Rep. Singer, who says he has never used marijuana, believes people have a right to get medicinal or recreational marijuana conveniently.
"We don't turn away people from their pharmacies. We allow people to get their medicine delivered to them," he said.
The plan is to have drivers carry only marijuana products that have been previously ordered, similar to how a pizza delivery driver only carries pizzas for customers who have placed an order.
Though all details haven't been worked out, he thinks the state can find a way to do it safely.
"I don't hear [skeptics] worry so much about Fed Ex trucks being knocked over everyday," he said.
California is way ahead of Colorado on the issue and has had marijuana delivery in place for years.
Depending on the individual city and its rules, some drivers leave a dispensary in the morning and stock their vehicle with marijuana they expect to deliver — based on past history for a region, the date and time.
In other places in California, drivers only carry marijuana to fulfill existing orders.
It's hard to know how marijuana delivery has affected crime overall.
The spokeswoman for the state's largest marijuana delivery company, Eaze, says crime has not been a significant factor for drivers or customers that are affiliated with.
"Less than .001% in the over 3 million deliveries we've made," said Sheena Shiravi, Eaze's Communications Director.
Rep. Singer says he expects Governor Jared Polis to sign the bill should it reach his desk. A spokeswoman for the Governor's Office says he is supportive of the concept, but is still reviewing the specifics.
The bill is in the House Finance Committee and must pass a vote there before lawmakers in the House and Senate can send it to the governor.
The legislative session is set to end May 3.
Viewers have shared their opinions after we announced our story on Facebook.
Marijuana customers using the drug while driving concerned at least one person.
Editor's note: Rep. Singer represents Longmont, which is in Boulder County. A previous version of this story suggested Singer represents the City of Boulder.


left: Rich Froning Jr, right: Camille LeBlanc-Bazinet- male and female champions of the 2014 Crossfit Games.
First of all I want to make sure that I give the proper credit to the person who deserves it. The title of this post is a copy of an article made by blogger COACHARNOLD from Power and Grace Performance. I just came across what I consider a really good article that applies to my life and wanted to share it with all of you.
I think these are the kind of questions that any believer should ask of himself/herself of anything they do whether it is a hobby, their job, or their personal relationships. Well I encourage you to continue and read the original post so you can think about these questions and hopefully gain some clarity on the answers, gain some perspective on how to remember he who holds us, who gives us the talents we have, and the ability to live the life that we live, and also hopefully as you read along you can know more of the challenges and the responsibilities of elite christian athletes.
For those of you who are athletes or want to be athletes like me, I hope that this article generates some interest in knowing how to live your life and how to perform at the top of your game but for the glory of God.
Love the thoughts and that article. Thanks, Andres.


Audi A4 transmission ECU failure will cause the PRNDS lights to flash and will prevent the gearbox from selecting gears. When using scan tools a range of fault codes may be found including: P0706 17090 - transmission range sensor (F125), P1757 18165 - supply voltage and P1793 18201 - transmission output speed sensor 2 (G196). Internal component malfunction is common but can be rectified if caught early enough.
great service sent audi ecu for test and repair,e mailed to say was faulty,priced repaired and returned in good time,cheers.


CRADLE OF FILTH: 'Evermore Darkly…' Artwork Unveiled?
French retailer Fnac has posted what appears to be the cover artwork for "Evermore Darkly…", the new mini-album from British extreme metallers CRADLE OF FILTH. Due on October 10 via Peaceville Records, the follow-up to last year's triumphant "Darkly, Darkly, Venus Aversa" album will arrive as a CD/DVD package including new tracks and rarities, with a lengthy on-the-road documentary and ten songs filmed live at this year's Graspop Metal Meeting in Dessel, Belgium, as well as the video for the song "Lilith Immaculate".
The "Evermore Darkly…" CD features brand new musical beast "Thank Your Lucky Stars", plus a special remix of "Forgive Me Father", courtesy of ANTHRAX guitarist Rob Caggiano, an extended rendition of "Lilith Immaculate", as well as tempered demo versions of three "Darkly, Darkly…" songs. The mini-album closes with a special taster from CRADLE OF FILTH's impending orchestral project in the form of a haunting, classic reworking of fan favorite "Summer Dying Fast".
The "Evermore Darkly…" DVD contains footage from summer 2011 when CRADLE OF FILTH embarked on a rapid spat of tours, accompanied by two eager filmmakers. The resulting documentary, "You Can't Polish A Turd, But You Can Roll It In Glitter", combined with performance and interview footage culminates in a forty five minute film of the not so glamorous reality of life in a touring band at work and play, as they trawl their way across Europe over eight performances. The DVD includes an interview with ghoul-guide Dani Filth, interspersed and peppered with live footage and backstage shenanigans, culminating in the entire hour long set from this year's Graspop Metal Meeting appearance.
To read our interview with CRADLE OF FILTH, please go HERE.


text:
Finally – album dei Blackstreet del 1999
Finally – singolo di Fergie del 2008
 Finally – singolo dei Mr. Belt & Wezol del 2015
Finally – raccolta di Namie Amuro del 2017

redpajama_set_name: RedPajamaWikipedia


text:
This terraform module defines the resources needed for running
[Cloud Resource Library](https://github.com/DataBiosphere/terra-cloud-resource-lib) (CRL) integration tests.

For more information, check out the [MC-Terra deployment doc](https://docs.dsp-devops.broadinstitute.org/mc-terra/mcterra-deployment)
and our [Terraform best practices](https://docs.dsp-devops.broadinstitute.org/best-practices-guides/terraform).

This documentation is generated with [terraform-docs](https://github.com/segmentio/terraform-docs)
`terraform-docs markdown --no-sort . > README.md`

## Requirements

No requirements.

## Providers

| Name | Version |
|------|---------|
| google.target | n/a |
| google | n/a |

## Inputs

| Name | Description | Type | Default | Required |
|------|-------------|------|---------|:--------:|
| google\_project | The google project id to create for CRL to run integration tests within. | `string` | n/a | yes |
| folder\_id | What folder to create google\_project and a test folder under. | `string` | n/a | yes |
| billing\_account\_id | What billing account to assign to the project. | `string` | n/a | yes |
| enable\_billing\_user | Whether to set the CRL test SA as a billing user on the billing account. | `bool` | `true` | no |

## Outputs

| Name | Description |
|------|-------------|
| test\_resource\_container\_folder\_id | The folder id of the folder the admin service account has permissions to edit. |
| service\_account\_email\_admin | Admin Google service account email |
| service\_account\_email\_user | User Google service account email |

redpajama_set_name: RedPajamaGithub


text:
Q: How to extract values of a column and create a new column with the result I have a data frame that looks like below, I want to be able to extract all values in the column 'Holiday' that says 'N' and create av new column 'NonHoliday'
My_data
        Date Holiday
        1     Y
        2     N
        3     N
        4     Y
        5     Y

My code
My_data['Holiday'] = My_data['Holiday'] == 'N'

gives an error message, I wanted something like this
Date NonHoliday
2     N
3     N

A: You can use:
new_df = My_data.loc[My_data['Holiday'] == 'N']

or:
new_df = My_data[My_data['Holiday'] == 'N']

And then:
new_df.columns = ['Date', 'NonHoliday']

redpajama_set_name: RedPajamaStackExchange


The seat grabber: Never mind if there are senior citizens or pregnant women waiting to board, this dude jumps in and rushes to take his seat. And then proceeds to look through anyone who might need the seat more than him.
The door hogger: This commuter cannot be lured away from the entrance to the Metro coaches. Others boarding or alighting have to make their way around him.
The laptop lugger: This has been a cause for much friction on the Delhi Metro and is catching on in Bangalore, too. Laptop-carrying commuters refuse to put their bags the floor, occupying space, hitting fellow commuters every time they move and causing tempers to flare.
The loud talker: You feel like telling them… 'Sshh…keep it down, please.' They speak so loud on their mobiles, you can follow the entire conversation and learn the intimate details of their problems with the boss, their dinner plans and their opinions about the Indo-Pakistan cricket match.
The tourist: They've come from out of town and the Metro is a novelty. The problem arises when they want to be photographed hanging from the straps, or posing next to the door in a fully-packed compartment. Metro coaches when they are full are not a good setting for selfies either.


The Global 8000, able to fly farther than any other business jet, will feature a three-zone 2,236 cu.ft. cabin and a range of 7,900 nmi at Mach 0.85. It will be powered by the next-generation GE TechX 16,500 lbf thrust engines to deliver significant efficiency and emissions advantages, including reduced NOx emissions and an eight per cent overall fuel efficiency when compared to the industry leading Global Express XRS aircraft. It will connect Sydney to Los Angeles, Hong Kong to New York and Mumbai to New York non-stop with eight passengers. The Global 8000 jet will reach a high-speed cruise of Mach 0.90. Entry into service is scheduled for 2017.


Sehr geehrter Kunde, nach langer Zeit freuen wir uns, Ihnen den neuen und verbesserten Tembo Shop präsentieren zu können. Die neuen Features sind beispielsweise eine optimierte Ausgabe für den Tablet PC als auch eine optimierte Ausgabe für Ihr Mobiltelefon.
Es gibt viele Innovationen die Ihren Einkauf einfacher und sicherer machen. Sie können sich nun wieder neu als Kunde registrieren oder einfach als Gast in unserem Shop bestellen.
Dear customer, it's finally the New Tembo store is finished after a long time we are glad to present you the new better Tembo store. The new features are, for example an optimized output for the Tablet PC and also optimized output for your mobile phone.
There are many innovations make a shopping easier. Furthermore, we have decided old customer data are not therefore taken only once every customer to re-register or do not. For those who do not wish to register for which there is again the opportunity to order as a guest so will the personal information be used only for an order and then deleted automatically.
P. S. there is a smooth transition so all orders made in the old shop are also processed.


text:
NFL Anthem Protesters Made Things Worse With One Sick Threat To Trump
The cancer of anti-American anthem protests is spreading.
NFL executives are folding to their America hating players' demands.
And it could lead to one massive threat to Donald Trump.
The NFL announced it would support one of the pro-cop killer Black Lives Matters top wish-list items.
Now the NFL will support legislation in Congress to flood the streets with drug-using thugs and other criminals by supporting earlier prison releases and softer sentences.
"The National Football League seeks to double down on politics despite the bad hand of anthem kneelers resulting in fans becoming boo-birds.
The league recently formally endorsed the bipartisan Sentencing Reform and Corrections Act.
Senators Chuck Grassley (R-Iowa) and Dick Durbin (D-Ill.) introduced the legislation, thwarted in earlier incarnations, earlier this month. A diverse lot including Senators Mike Lee (R-Utah), Dianne Feinstein (D-Calif.), and Cory Booker (D-NJ) support the measure.
"We felt that this was an issue over the last months, as we have continued to work with our players on issues of equality and on issues of criminal justice reform, that was surfaced for us, and we thought it was appropriate to lend our support to it," Joe Lockhart, the former spokesman for President Bill Clinton who now performs a similar role for NFL Commissioner Roger Goodell, informed reporters in acknowledging the league's support for the bill on a media conference call.
The bill changes life sentences for three-time drug felons to 25-year terms, amends the Controlled Substances Act to impose a mandatory minimum of five years rather than ten, and seeks alternatives to prison for nonviolent offenders, such as home confinement. It tasks the attorney general with creating "a strategic plan for the expansion of recidivism reduction programming and productive activities."
The bill applies to the small percentage of inmates in federal prisons and not to those sentenced in state courts."
This will likely not be the last demand made by players.
Now that they see the league's weakness, they are likely to only increase their demands.
And it could lead to even greater anti-Trump activism.
The players claimed Trump's statement that the owners should fire players who kneel for the anthem spurred the league-wide protests in week 4.
One player even wore an "everyone against Trump" t-shirt.
The league felt backed into a corner.
Fans are furious about NFL players not standing for the anthem.
Some stadiums are empty on Sundays and ratings are down 7.5% from last year.
But a faction of the players want to use the NFL as a political platform.
In order to try and talk the players off the ledge, the league caved to their demands to support left-wing political causes.
This won't stop with prison reform.
Now that the players have sensed they can push the league around and get their way, their demands for the NFL to jump into political debates and issues will increase.
And it could wind up with the NFL taking even more anti-Trump stances.
Tags home President Bill Clinton
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redpajama_set_name: RedPajamaCommonCrawl


The official Japanese Pokémon Youtube channel has updates with a new trailer for Pokémon the Movie: Mewtwo Strikes Back Evolution. The recent trailer features a closer look at the film's setting and showcases another look at the characters. The trailer also takes a look at Mewtwo's armored form, which looks quite different compared to his original design.
Pokémon the Movie: Mewtwo Strikes Back Evolution will be released in Japanese theaters on July 12, 2019.
After seeing this trailer it actually makes me believe this film might be pretty lame. The human character animation looks worse in this trailer than it did in the previous trailers and it doesn't help some specific scenes don't have the same life as it had in the original film. I also don't like the redesign of Mewtwo's armored form since it looks like a mess.


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2020 Census Program Management /
Processing the Count /
Disclosure Avoidance Modernization /
Understanding Differential Privacy
Within Decennial Census of Population and Housing
Guidance for Data Users
Information for Respondents
Census confidentiality protections—what we call "disclosure avoidance"—have evolved over time to keep pace with emerging threats. Since the 1990 Census we've added "noise"—or variations from the actual count—to the collected data. For 2020 Census data we're applying noise using a newer protection framework based on "differential privacy." Learn more here about why and how we're modernizing our protections and how you can engage in the process.
Why We're Modernizing Census Disclosure Avoidance
How Census Disclosure Avoidance Methods Have Evolved Over Time
How Differential Privacy Works: The Basics
Technical Background Information
Why We're Modernizing Census Disclosure Avoidance
What law requires the Census Bureau to protect against disclosure of my Census responses?
Why do we need a different disclosure avoidance system in 2020 than we used in 2010?
What harms can arise if the basic demographic data collected in the decennial census are exposed?
Could external attackers know whether they've correctly reidentified individuals in Census data even if they don't have access to confidential census records?
Reconstruction / Re-identification research
Simulated Reconstruction Re-identification Attack on the 2010 Census
This webinar examines the simulated re-identification attack that the Census Bureau performed on the published 2010 Census data.
Random Samplings Blog
Census Bureau Adopts Cutting Edge Privacy Protections for 2020 Census
The Census Bureau takes seriously its legal and professional obligation to safeguard the information it gathers from the public.
The Balancing Act of Producing Accurate and Confidential Statistics
Several months ago, Dr. John Abowd, our chief scientist at the Census Bureau, blogged about the need for disclosure avoidance measures and the considerations that went into our decision to implement differential privacy.
Research Matters Blog
Protecting the Confidentiality of America�s Statistics: Ensuring Confidentiality and Fitness-for-Use
In my last blog, I described the Census Bureau's work to modernize how we protect respondent data in the statistics we publish.
Adopting Modern Disclosure Avoidance Methods at the Census Bureau
The U.S. Census Bureau's commitment to data stewardship—protecting respondent privacy and confidentiality at every stage of the data lifecyle—is grounded in law that is straightforward, robust, and strong.
Challenges Facing the Disclosure Review Board
Scientists from the Census Bureau describe the importance of research in survey design, methodology, modeling, record linkage, confidentiality protection, etc.
A History of Census Privacy Protections
Learn how the laws and protections have changed from 1790 to the 2020 Census—based on safeguards to protect identifiable information.
Disclosure Avoidance Techniques Used for the 1970 through 2010 Decennial Censuses of Population and Housing
Developing the new Disclosure Avoidance System
Demonstration data (Privacy-Protected Microdata Files) and quality metrics
Development timeline [<1.0 MB]
What kinds of disclosure avoidance systems has the Census Bureau used in the past?
Disclosure Avoidance for the 2020 Census: An Introduction
In this handbook, the U.S. Census Bureau's Disclosure Avoidance System is described in the context of the 2020 Census Redistricting Data Summary File.
Protecting Privacy in Census Bureau Statistics
Protecting Privacy with Math
Differential Privacy 101
This introductory webinar will explain the Census Bureau's decision to modernize the methods we use to protect respondent confidentiality in data we release.
Research into Alternatives to Differential Privacy
Census Bureau researchers present preliminary findings from experiments designed to illustrate techniques used to protect privacy.
Comparing Differential Privacy With Older Disclosure Avoidance Methods
Differential privacy, first developed in 2006, is a framework for measuring the precise disclosure risk associated with each release of confidential data.
Protecting the Confidentiality of 2020 Census Redistricting Data
By law, we must ensure that we don't release information that could identify your information in the statistics we publish.
Redistricting Data: What to Expect and When
Since releasing the apportionment results in April, we've had several teams working hard on the next set of 2020 Census data — the redistricting data.
Modernizing Privacy Protections for the 2020 Census: Next Steps
The world has evolved quite a bit since 1990, the last time we changed the way we protect individual responses in published census statistics.
Why didn't the 2010 Census use differential privacy?
Differential Privacy 201 and the TopDown Algorithm
This webinar provides an examination of how the Census Bureau is using framework of differential privacy to safeguard respondent data for the 2020 Census.
Misc. Background Resources
Census Bureau Declarations for Alabama v. Commerce II Litigation - Part I [<1.0 MB]
Census Bureau Declarations for Alabama v. Commerce II Litigation - Part II [<1.0 MB]
Consistency of Data Products and Formal Privacy Methods for the 2020 Census
JASON was asked to study the impact of these privacy mechanisms on consistency for two data products produced from the 2020 census data.
Assessment of 2020 Census Data Quality Processes
This JASON report assesses data quality processes, procedures, and plans and provides insights and recommendations on areas for improvement.
Abowd, J.M., Benedetto, G.L., et al. (2020) The Modernization of Statistical Disclosure Limitation at the U.S. Census Bureau. Census Bureau Working Paper.
Dinur, I. and Nissim, K. (2003) "Revealing Information while Preserving Privacy" PODS, June 9-12, San Diego, CA. https://doi.org/10.1145/773153.773173.
"U.S. Census Bureau Reidentification Studies," available at https://www.census.gov/library/working-papers/2019/adrm/2019-04-ReidentificationStudies.html.
Abowd, J. M., & Schmutte, I. M. (2019). An economic analysis of privacy protection and statistical accuracy as social choices. American Economic Review, 109(1), 171-202.
Garfinkel, S., Abowd, J. M., & Martindale, C. (2018). Understanding Database Reconstruction Attacks on Public Data: These attacks on statistical databases are no longer a theoretical danger. Queue, 16(5), 28-53.
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006, March). Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference (pp. 265-284). Springer, Berlin, Heidelberg.
Haney, S., Sexton, W., Machanavajjhala, A., Hay, M., Miklau, G., Tumult Labs, science@tmlt.io (2021) Differentially Private Algorithms for 2020 Census Detailed DHC Race & Ethnicity.
Page Last Revised - March 21, 2022


There's a phrase that I hear all the time now – "Gotta get my steps!" – that I'm pretty sure wasn't in the rotation a decade ago. The "steps" in question are, of course, part of a daily exercise regimen prompted by a wearable technology – the Fitbit or other. These slick wearable devices use biometric tracking to monitor our exercise, sleep patterns, heart rate, and other physical indicators to deliver the nudges we need for building a healthy lifestyle.
As this form of AI evolves, we can expect to see more and more personalization to tailor our specific daily routines, diet (both eating patterns and nutrient content), and a whole slew of other factors that affect our health. In much the same way as a personal trainer designs and adjusts a workout to fulfill our personal goals, so too will AI-powered activity trackers use the data we generate in our daily lives to help us live the healthiest lives possible.
Obviously, the goal isn't simply to live well at the moment but well into the future. One of the most exciting untapped potentials for AI biometrics, in concert with other domestic smart technology, is prevention. Your wearable might take note of uncharacteristic fatigue or lethargy, and consult with your smart fridge to find out if you've been hitting the junk food a little too hard.
Then it's added to and evaluated along with a roster of other possible warning signs, maybe nudging you to seek care earlier than you would have on your own. Such pre-emptive alerts would be an immense benefit for people who don't have immediate access to hospitals – who live in rural areas or have otherwise reduced mobility. And preventative care greatly reduces the strain on already overtaxed healthcare resources – early diagnosis of serious conditions such as cancer or dementia improves patient care and lowers costs.
While it might seem strange to some that we'd invite computers to assume such intimate roles – like monitoring our sleep and diet – in our day-to-day lives, the relationship we have with our smartphones is already borderline codependent. One of the most famous stories about how quickly we start to see computers as actual confidantes start way back in 1964 when Joseph Weizenbaum created a program called ELIZA. ELIZA was essentially a chat-bot therapist that reframed user's prompts as questions. As the story goes, Weizenbaum's secretary found ELIZA so comforting that she forced Lizenbaum to leave the room so that she could keep conversing with ELIZA in private!
AI already enables sophisticated chat-bots to serve in a variety of capacities, from customer-service reps for retailers to website moderators to (surprise!) even replacing our closest confidantes. Over time, as our AI-powered BFFs learn more about us –habits, tastes, even our unique communication styles – they can evolve to enrich our lives in the most personalized ways possible. According to Futurism.com, Replika is an AI app "programmed to ask meaningful questions about your life and to offer you emotional support without judgment. [Replika] learns about your interests and habits over time, even adopting your linguistic syntax and quirks much in the way a close friend might." As of early 2018, Replika had over a half million regular users, some of whom claim that they share secrets with the app that they wouldn't tell their family members or partners.
Your AI BFF might interpret a lack of movement as boredom and suggest a new game to download, or running path to check out, or movie to see. Even more remarkably, chat-bots such as Replika are designed to offer advice tailored to our attitudes and beliefs. Fight with the boss?
Ask the app to spitball – knowing you as well as they do, what course of action would they suggest?
Not only is this particular friend available 24/7, but could easily help reduce interpersonal strain and open new possibilities for maintaining open, honest relationships with the 'real' people in our lives.
And it doesn't stop there. We've already seen how AI can provide support as a personal trainer, a therapist, and a friend – but what about a dietician?
The assistance starts at the farm, where AI can track things that are important to you like ethical practices, agricultural efficiency, locally grown, nutrient-rich foods. Then, by recording and breaking down your specific food consumption habits, AI algorithms could recommend a variety of real-time bespoke meal choices.
Low on iodine? Grab some seaweed sushi. Not hot on the seaweed suggestion?
AI isn't gonna give you a bad time about it. Tuck into some fish and chips instead. Your nutrient intake will be recalibrated for the following few meals.
As we all well know, staying balanced isn't entirely under our own control. No matter how well we look after ourselves, the world around us can sometimes feel like a gauntlet of anxiety-inducing intrusions, hell-bent on ruining our good habits. Traffic jams, for example, are often cited as the principal cause of daily stress, and stress is a well-known risk factor for heart disease and an array of other afflictions.
But with AI as your new bestie, stress in traffic may be a thing of the past: how about "smart" traffic lights that communicate with your self-driving car?
You know, the one that can stop and start in near-perfect harmony with traffic while adjusting lighting and tunes to suit your exact mood… the only thing missing is the six-pack of beer (or is it!).
Whatever health and happiness looks like to you – exercise that fits snugly into the shape of your life, delicious and nutritious meals, stress free commutes, some backup when you're blue, or just getting in your steps – AI is on a course to help make it all happen in the most intimate and personalized ways possible.
Serial entrepreneur and founder of Calgary-based Ambyint Inc., Nav is one of the leading figures in oil and gas technology innovation and is a serial entrepreneur having attained a hard-won collection of tech startup hits in his history. Most notable amongst his startups is Calgary-based Ambyint Inc. - which delivered leading-edge IoT and Artificial Intelligence to the oil patch and brought self-running oil wells closer to reality. Currently in his sights is the strategic and disruptive power of Artificial Intelligence & Blockchain as a platform for an Artificial Intelligence Ecosystem via Imaginea.Ai, Nav's newly founded firm.


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PRCC Men's Basketball Alumni
Related Bios Hayden Sowers Dee Wallace
Chris Oney
Title: Head Coach
Email: coney@prcc.edu
Chris Oney is entering his fourth season as head men's basketball coach after taking over the position early in the 2016-17 season.
The Wildcats have experienced tremendous success since Oney has taken over as head coach. Oney has led the River to a MACJC Tournament title in 2018 and a Region XXIII Tournament title in 2019 along with the programs first birth in the NJCAA Tournament.
Oney spent four seasons at Pearl River as an assistant coach, coming to PRCC after two years at Grambling State.
He is a 1994 graduate of McCall High School in Tallulah, Louisiana, where he was district MVP for four seasons. He spent a year at Texas A&M before transferring to Ole Miss, where he played from 1995 through 1998. He earned a bachelor's degree from Ole Miss in 1998 and a masters in education from Louisiana College in 2008.
Oney began his coaching career at Nicholls State, where he spent one season. He spent four seasons as an assistant at Louisiana-Monroe before getting his first head coaching job at Carroll High in Monroe, Louisiana. In his two seasons there, his teams were 60-15.
He moved on to a two-year stint as head coach at Grambling Lab High School, where his teams were 43-12, before taking on a position as an assistant at Grambling State.
Oney and his wife, Christen, have two children, Camryn and Caleb.
Years School Title
2000-01 Nicholls State Assistant Coach
2001-05 Louisiana-Monroe Assistant Coach
2005-07 Carroll High School (La.) Head Coach
2007-09 Grambling High School Head Coach
2009-11 Grambling State Assistant Coach
2011-15 PRCC Assistant Coach
2016- PRCC Head Coach


CMS issued an approval letter dated December 5, 2013 notifying Indiana Medicaid that the Value Based Purchasing (VBP) methodology for nursing facility reimbursement had been approved (click here to read the approval letter). The newly approved State Plan Amendment will be implemented retroactively to July 1, 2013. Meyers & Stauffer, Indiana Medicaid's rate setting contractor, is currently calculating each nursing facilities' VBP score based upon the prior year cost report and Schedule X of the cost report. Once calculations are complete, nursing facilities will begin to receive notice of the claims reprocessing process on and after July 1, 2013. IHCA has asked Indiana Medicaid to be mindful of potential cash-flow issues if claims reprocessing results in significant overpayments (an overpayment because the facility fared worse under the VBP methodology than it did under prior methodology). In past claims reprocessing events, Indiana Medicaid has been able to stagger implementation to minimize the impact of significant overpayments.
The VBP program has been in the works for 3 years and is now approved. To read more detail about the VBP program, click here. Changes to the VBP methodology in the future are likely to include satisfaction survey measures and MDS Quality Metrics, although how to include either of those types of data is not decided. IHCA continues to engage Indiana Medicaid and the Division of Aging on those issues. Any future change to the VBP methodology is not expected until July 1, 2015.