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OSA Publishing > Biomedical Optics Express > Volume 1 > Issue 2 > Page 441
Christoph Hitzenberger, Editor-in-Chief
Issues in Progress
Feature Issues
Algorithmic depth compensation improves quantification and noise suppression in functional diffuse optical tomography
Fenghua Tian, Haijing Niu, Sabin Khadka, Zi-Jing Lin, and Hanli Liu
Fenghua Tian, Haijing Niu, Sabin Khadka, Zi-Jing Lin, and Hanli Liu*
Department of Bioengineering, the University of Texas at Arlington, 501 West First Street, Arlington, TX 76019, USA
*Corresponding author: hanli@uta.edu
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F Tian
H Niu
S Khadka
Z Lin
Vol. 1,
Issue 2,
•https://doi.org/10.1364/BOE.1.000441
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Fenghua Tian, Haijing Niu, Sabin Khadka, Zi-Jing Lin, and Hanli Liu, "Algorithmic depth compensation improves quantification and noise suppression in functional diffuse optical tomography," Biomed. Opt. Express 1, 441-452 (2010)
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Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography (BOE)
Quantification and normalization of noise variance with sparsity regularization to enhance diffuse optical tomography (BOE)
Quantification in time-domain diffuse optical tomography using Mellin-Laplace transforms (BOE)
Table of Contents Category
Image Reconstruction and Inverse Problems
The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Diffuse optical tomography
Functional imaging
Reconstruction algorithms
Previously assigned OCIS codes
Image reconstruction techniques (170.3010)
Tomography (170.6960)
Functional monitoring and imaging (170.2655)
Original Manuscript: June 8, 2010
Revised Manuscript: July 26, 2010
Manuscript Accepted: July 26, 2010
Biomedical Optics Express Optical Imaging and Spectroscopy (2010)
4. Discussion and conclusion
Equations (7)
Accurate depth localization and quantitative recovery of a regional activation are the major challenges in functional diffuse optical tomography (DOT). The photon density drops severely with increased depth, for which conventional DOT reconstruction yields poor depth localization and quantitative recovery. Recently we have developed a depth compensation algorithm (DCA) to improve the depth localization in DOT. In this paper, we present an approach based on the depth-compensated reconstruction to improve the quantification in DOT by forming a spatial prior. Simulative experiments are conducted to demonstrate the usefulness of this approach. Moreover, noise suppression is a key to success in DOT which also affects the depth localization and quantification. We present quantitative analysis and comparison on noise suppression in DOT with and without depth compensation. The study reveals that appropriate combination of depth-compensated reconstruction with the spatial prior can provide accurate depth localization and improved quantification at variable noise levels.
©2010 Optical Society of America
In the past decade, diffuse optical tomography (DOT) has become a promising technique to image functional brain activities [1,2] while being non-invasive, portable and biologically safe. Given the size and contour of the human head, reflectance geometry is widely used with the sources and detectors placed on the same side of the head and a few centimeters apart. However, the photon density in reflectance geometry drops rapidly with increased depth in tissue [3], i.e., deep absorption perturbation has low contribution to the intensity change measured on the surface. It follows that the sensitivity matrix, A, is ill-posed along depth. In conventional DOT reconstruction algorithm using Tikhonov regularization [2,4], the ill-posed A matrix results in a significant error in depth localization [2]. Since the magnitude of reflectance measurement is highly sensitive to the depth of local absorption perturbation, it becomes unrealistic to accurately quantify the local absorption perturbation without knowing its actual depth.
Recently we have developed a depth compensation algorithm (DCA) [5] to improve the accuracy of depth localization in three-dimensional (3D) DOT. It introduces a weight matrix, M, to counterbalance the severe sensitivity decay of A matrix along depth. The original A matrix is replaced by a depth-compensated matrix, AM, in reconstruction. The improved accuracy in depth localization by the depth compensation algorithm has been demonstrated in both laboratory phantom experiments and in vivo human brain study [6]. Thus, it is expected that this algorithm will also improve the quantification in DOT with a reasonable accuracy. However, this improvement cannot be achieved directly. It is because the matrix AM used in depth-compensated reconstruction is not in compliance with the actual measurement. Although the reconstructed image with depth compensation can reflect the actual location and size of local absorption perturbation, the quantity of the image does not reflect the actual quantity of the absorption perturbation. Thus, it is necessary to further develop an approach, which is based on the depth-compensated reconstruction, to improve the quantification in DOT. In this paper, we present such an approach which improves the quantification of local absorption perturbation by forming a spatial constraint or prior in reconstructed image. Simulative experiments are conducted to confirm the usefulness of this approach.
Moreover, in real measurement the data will inevitably contain noise which can be instrumental, physiological [2] or both. Mathematically, a parameter α is used in Tikhonov regularization to stabilize and optimize the solution of an ill-posed inverse problem [4] which suppresses the noise. Noise suppression is a key to success in DOT. However, it also affects the depth localization and quantification. For example, a set of real measurement data with a higher noise level will require a bigger regularization parameter in reconstruction, which may suppress more subtle but true signals that are likely to stem from deep tissues and consequently results in a bigger localization error in depth. In this paper, thus, we will also offer comparative analysis on noise suppression in DOT without and with the use of depth compensation.
2.1 Reconstruction and depth compensation
The measurement of absorption perturbation in a turbid medium is based on the modified Beer-Lambert law [7]. For a 3D medium, it can be generalized as [2]:
(1)ΔOD(λ,t)=∑j=1NvoxΔμa,j(λ,t)Lj(λ)
where ΔOD(λ,t) is the change in optical density (OD) attained from a source-detector pair (measurement) at wavelength λ and time t, Δμa,j is the absorption perturbation in the jth voxel of the medium, and Lj is the effective path length of detected photons through the jth voxel.
In DOT, multiple measurements are used to reconstruct an image of absorption perturbation. Thus, Eq. (1) can be written in a matrix/vector form as (hereafter we will omit variables λ and t):
(2)y=Ax
where y denotes the vector of ΔODi (i = 1 ... Nmeas) from all the measurements, x denotes the vector of Δµa,j (j = 1 ... Nvox) from all the voxels in the 3D medium, and A is a Nmeas × Nvox sensitivity matrix of Li,j and can be derived from the photon diffusion equation using the Rytov approximation [4]. In conventional DOT, the inverse solution of Eq. (2) is given based on Tikhonov regularization [2]:
(3)x^DOT=AT(AAT+αsmaxI)−1y
where I is the identity matrix, s max is the maximum eigen-value of AA T, and α is the regularization parameter to stabilize the solution and to suppress the noise existing in a real measurement.
However, in matrix A the elements from superficial layers are significantly greater in magnitude than those from deeper layers due to severe attenuation in photon density with increased depth. In depth-compensated reconstruction, a weight matrix, M, is created appropriately [5] and multiplied to matrix A in order to counterbalance the severe decay along depth. Specifically, the maximum singular value of layered sub-matrix A k (k = 1 ... Nlayer), denoted by smax,k for the kth depth, is inversely arranged to form matrix M:
(4)M=[smax,NlayerIsmax,Nlayer−1I...smax,1I]γ
where power γ is the parameter to adjust the weight of M. An optimal value of γ exists and has been determined empirically [5,6] with a range of 1.0 to 1.6. Then the depth-compensated matrix A# = AM is used to replace the actual sensitivity matrix A in Eq. (3), leading to
(5)x^DCA=(AM)T[(AM)(AM)T+αs′maxI]−1y
where s' max is the maximum eigen-value of (AM)(AM)T.
2.2 Quantification approach based on depth-compensated reconstruction
We have developed an approach in order to quantify the absorption perturbation based on the reconstructed image with depth compensation. This approach has two steps: the first step is to determine a scaling parameter that will remove the artificial factor induced by matrix M; the second step is to define an appropriate region of interest (ROI) in the reconstructed image to form a spatial hard prior or constraint.
(1) Determination of scaling parameter: By introducing the weight matrix M, the depth compensation algorithm actually derives the solution from an artificial forward model y=AMx^DCA. Since the depth-compensated reconstruction has been demonstrated to retrieve the actual size and location of local absorption perturbation within experiment error [5,6], we assume the depth-compensated solution, x^DCA, can be applied to the original forward problem by introducing an scaling parameter K. Thus, Eq. (2) can be rewritten as:
(6)y=KAx^DCA
where y is attained from actual measurements, A is the original sensitivity matrix and can be obtained through the calculation of forward problem, x^DCAis the reconstructed image from Eq. (5), and only K is unknown and can be solved in principle. In this paper the optimal estimation of K is carried out by linear regression across all the measurements. After K value is determined, x^DCA'=Kx^DCA becomes a new solution of depth-compensated reconstruction with correct scale. In this paper, all the reconstructed images with depth compensation to be shown are from x^DCA'.
(2) Formation of spatial prior: A general problem in DOT is the poor spatial resolution, which is partially attributed to the diffuse nature of photons and partially attributed to the limited number of measurements as compared to the huge number of voxels to be quantified in the medium. As a result, the outline of the absorber is blurred, and the recovered quantities of absorption perturbation are reduced in the reconstructed DOT image. A widely used approach to improve such a problem is to combine DOT with a spatial prior or constraint of abnormal structures that can be obtained from other high-resolution imaging modalities, such as CT and MRI [8,9]. However, independent usage of DOT as a stand-alone unit will be reduced by using this approach.
Since the depth compensation algorithm is able to determine the accurate location and size of local absorption perturbation, it may allow us to form a spatial prior directly from the reconstructed image, rather than from other imaging modalities, to quantify the values of local absorption perturbation with reasonable accuracy. Specifically, we first identify a ROI in the reconstructed images by applying a half-maximum threshold (or a half-minimum threshold for globally negative absorption perturbation). The identified ROI is then used to form a spatial hard prior with two assumptions: (1) the absorption perturbation in the medium originates from only the identified ROI and is negligible beyond this ROI, and (2) the absorption perturbation is homogeneous within the ROI, denoted by Δμa_ROI. Based on these assumptions, Eq. (2) after the spatial prior is applied can be rewritten as:
(7)y=Δμa_ROI∑j∈ROILi,j
In Eq. (7) the only unknown parameter is Δμa_ROI. The optimal estimation of Δμa_ROI is then determined via linear regression across all measurements.
2.3 Simulative experiments
Three simulative experiments were conducted to validate our new quantification approach using a Photon Migration Imaging (PMI) toolbox available online [10]. The homogeneous medium had absorption and reduced scattering coefficients of 0.1 and 10 cm−1, respectively. A 5 × 5 optode array (13 sources and 12 detectors) with an interval of 1.4 cm was arranged on the surface of medium, producing 132 measurements when the 1st to 4th nearest source-detector pairs were used (separation ranged from 1.4 to 5.0 cm). No noise was added to the data during the calculation of forward problem. In accordance with the coordinate system shown in Figs. 1(a) to 1(c), images of absorption perturbation were reconstructed in a 3D volume under the probe with x = −3 to 3 cm, y = −3 to 3 cm and z = −0.4 to −3 cm. The voxel size was 0.1 × 0.1 × 0.1 cm3. In each experiment, one (or two) cylindrical absorber(s) was (were) embedded into the medium with one circular side facing up to mimic the absorption perturbation due to brain activation(s). The absorber(s) had an identical diameter of 1.6 cm and thickness of 0.8 cm. The reduced scattering coefficient of the absorber(s) was the same as the background medium.
Fig. 1 Setups of simulative experiments: (a) experiment I, (b) experiment II, and (c) experiment III.
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In experiment I, one cylindrical absorber was located right under the center of the optode array at a depth of z = −2 cm, as shown in Fig. 1(a). The absorption coefficient of the absorber was 0.3 cm−1 (i.e., the actual perturbation due to the presence of the absorber, Δμa, is 0.2 cm−1).
In experiment II, two cylindrical absorbers with different absorption coefficients were used to mimic brain activations occurring simultaneously at two different locations. As shown in Fig. 1(b), two absorbers were located along one diagonal direction (y = x) of the optode array with a separation of 4.2 cm in lateral span (x-y plane). Both of them were located at a depth of z = −2 cm. One absorber had an absorption coefficient of 0.2 cm−1 and the other one was 0.3 cm−1.
In experiment III, two cylindrical absorbers with same absorption coefficient were located at two different depths. As shown in Fig. 1(c), two absorbers were located along one diagonal direction (y = x) of the optode array with a separation of 4.2 cm in lateral span. One absorber was at a depth of z = −2.2 cm and the other one was at z = −1.8 cm. Both absorbers had the same absorption coefficient of 0.3 cm−1.
For all the simulative experiments, first images of the cylindrical absorber(s) were reconstructed with depth compensation. As mentioned in Section 2.1, the optimal value of γ ranges from 1.0 to 1.6. In this study a medium γ value of 1.3 was used. Although no noise was added in the forward simulation, we used α = 10−3 in regularization which was a representative value we had used in tissue-like phantom experiments. To evaluate the quantification accuracy of the reconstructed image, we used two parameters: the first parameter was the maximum absorption perturbation, Δμa_max, in the scale-corrected image (i.e., x^DCA') without any spatial prior. The second parameter was the quantified absorption perturbation with spatial prior, Δμa_ROI.
Second, similar image reconstruction (α = 10−3) and evaluation process was repeated using the conventional DOT reconstruction algorithm [2] without depth compensation. In this way, a comparison between the results with and without depth compensation was made for each experiment.
2.4 Noise suppression
The regularization parameter, α, seen in Eqs. (3) and (5) is used to suppress the noise existing in real measurements. The optimal α value for DOT is usually determined by an L-curve algorithm [11,12]. In general, a bigger α value has to be used for data with a higher noise level to suppress the noise more severely. However, a bigger α simultaneously suppresses more subtle but true signals that are likely to stem from deep tissues and consequently results in a bigger localization error in depth. When the depth compensation algorithm is applied, this suppression becomes more complex since another parameter, γ, is introduced to adjust the power of depth compensation. These two parameters may have interaction or crosstalk. Based on the setup of simulative experiment I, which is shown in Fig. 1(a), we investigated the potential interaction between α and γ in the following three aspects:
Noise interference: Two data sets were generated when calculating the forward problem: the first data set was noise-free and the second one was added with 1% random noise. For both data sets, images of the absorber were reconstructed with and without depth compensation, respectively. In each reconstructed image the location of voxel with maximum absorption perturbation was identified to represent the central location of the absorber. Since the regularization parameter α was responsible for noise suppression, we altered the α value in reconstruction from 1 to 10−6 at a logarithm step size of 10−1 (1, 10−1, 10−2, ..., 10−6) and then compared the results from data with and without random noise. In this way, the sensitivity of respective reconstruction algorithm (with or without depth compensation) to noise can be seen clearly.
Depth localization: For depth-compensated reconstruction, we varied the γ value from 1.1, 1.3 to 1.5 simultaneously while modifying α values to investigate if γ had any cross effect with α on depth localization. It is noteworthy that the conventional DOT algorithm without depth compensation is equivalent to the case of γ = 0 when depth compensation is applied. For each pair of α and γ, the location of the reconstructed absorber was again represented by the location of voxel with maximum value of absorption perturbation.
Quantification: Using the data set with 1% random noise, values of Δμa_max and Δμa_ROI of the absorber were also quantified from the reconstructed images at variable α and γ values, and then compared between reconstruction algorithms with and without depth compensation.
3.1 Reconstruction and quantification
Experiment I: Figs. 2(a) to 2(c) show depth cross sections (y-z plane, x = 0) of the actual absorber, the reconstructed DOT images without and with depth compensation from the simulation setup shown in Fig. 1(a). The reconstructed image without depth compensation has a significant localization error in depth. Its maximum value, Δμa_max, is 0.059 cm−1 and located at about z = −1.4 cm, as seen in Fig. 1(b). By applying the half-maximum threshold, the recovered absorption perturbation within ROI, Δμa_ROI, remains to be 0.059 cm−1, which is about 30% of the actual value. After using depth compensation but without spatial prior, the reconstructed image recovers Δμa_max to be 0.070 cm−1 and locates at z = −1.9 cm, as shown in Fig. 1(c). If half-maximum thresholding is utilized to define ROI as a spatial prior, the recovered absorption perturbation with depth-compensated reconstruction is dramatically improved to be 0.122 cm−1, about 61% of the actual value. It is mainly because the identified ROI in reconstructed image with depth compensation can retrieve the location and size of the actual absorber very well, as illustrated by the dash circle in Fig. 3(c) .
Fig. 2 For experiment I, a depth cross section (y-z plane, x = 0) of the (a) actual absorber, (b) reconstructed DOT image without depth compensation, and (c) reconstructed DOT image with depth compensation. The dash circles in (b) and (c) mark the ROIs with half-maximum threshold.
Fig. 3 For experiment II: Figs. 3(a), 3(c), 3(e) are depth cross sections along the diagonal plane at y = x, which is marked by the dash line in (b), of the (a) actual and reconstructed absorbers (c) without and (e) with depth compensation being utilized. Figures 3(b), 3(d), 3(f) are lateral cross sections in the x-y plane of the (b) actual and recovered absorbers (d) without and (f) with depth compensation applied, respectively.
Experiment II: Figs. 3(a) and 3(b) show a depth cross section along the diagonal plane at y = x [shown by the dashed line in Fig. 3(b)] and a lateral cross section in the x-y plane at z = −2.0 cm for the two simulated absorbers [see Fig. 1(b)], respectively. Both absorbers are at z = −2.0 cm. The actual Δμa values of the two absorbers are 0.1 and 0.2 cm−1, respectively. Correspondingly, Figs. 3(c) and 3(d) show the depth and lateral cross sections of the reconstructed DOT images without depth compensation. Both of the absorbers are untruthfully projected to a depth of z = −1.4 cm. Therefore, z = −1.4 cm was chosen to plot the lateral cross section, as shown in Fig. 3(d). Without either depth compensation or spatial prior applied, the Δμa_max values for the two absorbers are 0.024 and 0.048 cm−1, about 24% of the actual values. In this case since the two absorbers can be clearly separated in the reconstructed images, two individual ROIs are identified in two sub regions using their respective half-maxima. The determined Δμa_ROI values for the two absorbers are 0.022 and 0.048 cm−1, as low as the Δμa_max values.
At last, Figs. 3(e) and 3(f) show the depth and lateral cross sections of the reconstructed DOT images with depth compensation. The two absorbers are projected to z = −1.9 cm, which is very close to their actual locations. Therefore, the x-y lateral cross section is drawn at z = −1.9 cm, as given in Fig. 3(f). With depth compensation being applied but without ROI-based spatial prior, the recovered Δμa_max values for the two absorbers are 0.027 and 0.058 cm−1, having a recovery rate of 27-29% of the actual values. Then, after two sub ROIs (described above) are appropriately identified, the reconstructed values of Δμa_ROI for the two absorbers are improved to be 0.047 and 0.104 cm−1, with a recovery rate of 47-52%.
Experiment III: Fig. 4(a) shows a depth cross section (along diagonal plane at y = x) of the two simulated absorbers. The actual absorbers are located at z = −2.2 and −1.8 cm with the same Δμa value of 0.2 cm−1. Correspondingly, Fig. 4(b) shows the depth cross section of the reconstructed absorber without using depth compensation. Both absorbers are untruthfully projected to z = −1.4 cm, regardless of their actual depths. Without either depth compensation or ROI-based spatial prior applied, the recovered Δμa_max values for the two absorbers are 0.024 and 0.053 cm−1, largely different from one another and from the single true value (0.3 cm−1). After two separate ROIs are identified in two sub regions, using the same method as Experiment II, the determined Δμa_ROI values are 0.020 and 0.055 cm−1 for the respective absorbers, as low and wrong as the Δμa_max values.
Fig. 4 For experiment III: a depth cross section along the diagonal plane at y = x of the (a) actual absorbers, (b) reconstructed DOT image without depth compensation, and (c) reconstructed DOT image with depth compensation.
In contrast, Fig. 4(c) shows the depth cross section of the reconstructed DOT image when depth compensation is applied. Two absorbers are projected to z = −2.1 and −1.7 cm, being very close to their actual depths. With depth compensation but without spatial prior, the Δμa_max values for the two absorbers are 0.020 and 0.052 cm−1. Then after two spatial priors are applied in two sub regions, the recovered Δμa_ROI values of the respective absorbers are improved to be 0.075 and 0.091 cm−1, with recovery rate of 38-46% of the actual values. It is also noteworthy that the Δμa_ROI values become less different between the two absorbers.
Overall, the recovered absorption perturbations from experiments I to III are listed in Table 1 , where Δμa is the actual value of absorption perturbation, Δμa_max and Δμa_ROI are the recovered values without and with the use of ROI-based spatial prior, respectively.
Table 1. Actual absorption perturbation, Δµa, maximum absorption perturbation recovered without ROI, Δµa_max, and absorption perturbation recovered with ROI, Δµa_ROI, for each absorber in experiments I to III. Unit: cm−1
The results seen in all three simulated experiments demonstrate clearly that the depth-compensated reconstruction with a spatial prior is critically useful to recover the absorption perturbation. In contrast, a spatial prior without depth-compensated reconstruction brings little improvement for the recovery of absorption perturbation, which is the case of conventional DOT imaging. Therefore, the success of our approach to improve the quantification in DOT requires the combination of the spatial prior with depth-compensated reconstruction.
Given the setup in Experiment I, the simulated data sets with and without 1% random noise are used to illustrate the results in this sub-section.
Noise interference: Figs. 5(a) and 5(b) show the reconstructed depths of the absorber at variable α and γ values, using the data without and with noise, respectively. It is clearly seen in Fig. 5(b) that if 1% random noise is added in the data, for the two reconstruction algorithms with and without depth compensation, the recovered depths of absorber are stable/consistent only when α is 10−3 and bigger (marked by the dashed line in that figure). Thus, two reconstruction algorithms have approximately same sensitivity to noise.
Fig. 5 Reconstructed depths of the absorber, using the setup of Experiment I in Fig. 1(a), with variable α and γ values when data were (a) noise-free and (b) with 1% random noise. Conventional DOT without depth compensation is equivalent to γ = 0. In (b) the reconstructed depths become divergent roughly after α = 10−3, which is attributed to the 1% random noise. The expected depth of the absorber is at z = −2 cm.
Depth localization: Figs. 5(a) and 5(b) also illuminate how the depth localization is affected when both α and γ vary. For all α values which can stabilize the inverse solutions (between 1 and 10−6 for noise-free data, and between 1 and 10−3 for data with 1% random noise), with the use of conventional DOT without depth compensation (γ = 0), a smaller α value results in a better accuracy in depth, approaching to, but never reaching the actual depth at z = −2 cm. When a big α-value is used (which in principle should be used for more noisy data), the reconstructed image without depth compensation has a very significant depth error as the absorber is untruthfully projected toward the surface. When depth-compensated reconstruction is applied, the reconstructed depth of absorber is relatively stable, within ± 3 mm of its actual depth, rather independent of either α or γ (between 1.1 and 1.5). The overall results in Fig. 5 demonstrate that when depth compensation is used in DOT reconstruction, any value of γ in the range of 1.1 to 1.5 is able to provide reliable depth localization with a 3-mm possible deviation for data at variable noise levels, even at a high noise level which may mandate the regularization parameter α to be a large value of 0.1 or 1.
Quantification: Using the data set with 1% random noise, values of Δμa_max and Δμa_ROI of the absorber were also quantified at variable α and γ values. Figures 6(a) and 6(b) show the quantified Δµa_max and Δµa_ROI values of the absorber within α ≥ 10−3 since the reconstruction becomes unstable when α < 10−3 [see Fig. 5(b)]. Note that each of these two figures includes the data obtained both without (γ = 0) and with depth compensation. In addition, Fig. 6(c) shows the volumes of the identified ROI, VROI (in cm3), for respective cases and within α ≥ 10−3. Without the ROI-based spatial prior, Fig. 6(a) plots Δµa_max values at four different γ values, none of which can recover the actual absorption perturbation in a reasonable range. The best recovery rate is ~28% when α = 10−3.
Fig. 6 Quantified (a) Δµa_max, (b) Δµa_ROI and (c) VROI values of the absorber, with the given setup in Experiment I, when α and γ values are varied. The expected Δµa value is 0.2 cm−1. The expected volume of the absorber is 1.7 cm3. Conventional DOT without depth compensation is equivalent to γ = 0.
On the other hand, when both depth-compensated reconstruction and ROI-based spatial prior are used for the respective cases, Fig. 6(b) shows that the reconstructed values of Δµa_ROI are in a range of 25-64% recovery with respect to the actual value of the absorber (Δµa = 0.2 cm−1) while both α and γ values are varied. Specifically, a smaller α value seems to lead to an improved Δµa_ROI, as expected. At α = 10−3, for example, the Δµa_ROI values are 52% and 62% recovered with γ = 1.1 and 1.5, respectively. In contrast, Δµa_ROI shows a mere 5-27% recovery rate when depth-compensated reconstruction is not used (γ = 0), even though the ROI-based spatial prior are formed appropriately.
Furthermore, Fig. 6(c) shows the dependence of ROI volumes, VROI, on α for all cases (without and with depth compensation in reconstruction), illustrating a decreasing pattern as α values become smaller. This trend can account for the increasing trend in Δµa_ROI with reduced α value, as seen in Fig. 6(b). At α = 10−3, the values of VROI approach the actual volume of the absorber, which is marked by the dash line in Fig. 6(c), for variable γ values.
Accurate quantification of a local absorption perturbation has been a major challenge in the field of DOT for functional brain imaging. When multi-channel data are collected in DOT to reconstruct an image of the brain activation, the accuracy in quantification relies greatly on the accuracy in localization of the reconstructed object, or the activated regional volume in the brain, particularly along the direction of depth. Unfortunately, the conventional reconstruction algorithm in DOT is not able to compensate the severe attenuation in sensitivity with increased depth, resulting in a significant depth error.
An algorithmic depth compensation has been developed and demonstrated to be able to accurately localize the absorption perturbation in our previous reports [5,6]. Consequently, this algorithm has a potential to improve the accuracy in quantification. In this paper, we have presented an approach based on the depth compensation algorithm that can be used to improve the quantification of absorption perturbation. The fundamental idea of this approach is to generate a spatial prior by half-maximum thresholding so that the reconstructed image can recover the object closely to its actual size and location. Three simulative experiments have been conducted to validate the approach using absorbers with variable absorption contrasts as well as at different locations and depths. When the depth compensation algorithm is utilized without a spatial prior, the reconstructed maximum values, Δµa_max, of absorption perturbations in each experiment are comparable to those obtained without using any depth compensation. But, none of them can recover well the true values of absorption perturbation. Then after defining an appropriate ROI by half-maximum thresholding in the reconstructed image with depth compensation, the accuracy in quantification is improved greatly in each of the three experiments. More significant improvement has been observed for deeper absorbers, as demonstrated in experiment III (Fig. 4). The results given throughout all three experiments are reasonable and expected since the spatial priors with depth-compensated reconstruction are relatively close to the actual locations and sizes of the absorber(s) in each experiment. Since the spatial prior utilized in this approach also depends on the spatial resolution of reconstructed image, we expect that the accuracy of quantification using this approach may vary somewhat for different probe geometries. Such expectation needs to be studied in future.
In addition to the simulative experiments described above, we have also conducted an Intralipid phantom experiment to further validate our quantification approach. The setup for phantom experiment was the same as simulative experiment I, which is shown in Fig. 1(a). The optical properties of the phantom were measured with a dual-channel tissue oximeter (Model 96208, ISS INC., Champaign, IL) at 750 and 830 nm. The data was acquired with a high-density DOT system at 750 and 850 nm [13], using the probe geometry shown in Fig. 1(a). Since the tissue oximeter and DOT system shared the same wavelength at 750 nm, we analyzed the data at this wavelength only. Specifically, the phantom had absorption and reduced scattering coefficients of 0.08 and 11.1 cm−1 at 750 nm, respectively. The cylindrical absorber had approximately the same reduced scattering coefficient as the phantom, and a higher absorption coefficient of 0.23 cm−1 at 750 nm. The absorber was embedded 2 cm into the phantom (i.e., z = −2 cm), which introduced an absorption perturbation of 0.15 cm−1. Figures 7(a) and 7(b) show depth cross sections (y-z plane, x = 0) of the reconstructed DOT images without and with depth compensation, respectively. The cylindrical absorber is projected to z = −1.4 cm in reconstructed image without depth compensation and at z = −1.7 cm when depth compensation algorithm is applied. In both images, the Δμa_max values are much lower than the actual value (0.15 cm−1). After a ROI is identified with half-maximum threshold to form a spatial prior, the Δμa_ROI value derived from the reconstructed image without depth compensation is 0.025 cm−1, merely 17% of the actual value. The Δμa_ROI value derived from the reconstructed image with depth compensation is improved to 0.042 cm−1, about 28% of the actual value. Within the experimental error, the depth-compensated reconstruction with the spatial prior significantly improves the recovery rate of absorption perturbation, although it is still far from being ideal.
Fig. 7 For phantom experiment, a depth cross section (y-z plane, x = 0) of the reconstructed DOT image (a) without depth compensation, and (b) with depth compensation. The cylindrical absorber was located at z = −2.0 cm which had an actual Δµa of 0.15 cm−1.
The noise suppression also plays a role in DOT reconstruction, with and without depth compensation, which affects the size and location of object in reconstructed image. Using the setup in Experiment I as an example, a comprehensive comparison on the noise suppression has been conducted to investigate the (1) deviations of the reconstructed object depths and (2) recovery rates of absorption perturbations that were derived from reconstructed images without and with depth compensation while values of α and γ were varied. The reconstruction outputs from the data without and with 1% random noise illustrate that the two reconstruction algorithms, i.e., reconstruction with and without depth compensation, have approximately same sensitivity to noise. For all α values which can stabilize the inverse solutions, however, the recovered DOT images without applying depth compensation cannot exhibit the correct depth (z = −2 cm) of the absorber, regardless of any α value used. On the other hand, if depth compensation is employed, the reconstructed depths are much closer to the expected value (z = −2 cm) without any restriction in α values for noise-free data and with α larger than 10−3 for the data having 1% random noise.
More importantly, the investigation on inter-parameter effects between α and γ on depth localization reveals that conventional DOT reconstruction is more restricted by the noise suppression procedure. For example, when a big α value is employed for more noisy data, conventional DOT algorithm (γ = 0) has a very significant depth error. On the other hand, when depth compensation algorithm is combined in the DOT reconstruction, the recovered depth of the absorber is relatively stable across a broad range of α values when an optimal γ range is selected (between 1.1 and 1.5). It is a strong support that the depth compensation algorithm can provide reliable and accurate depth localization for data at variable noise levels. This finding is practically important with the consideration of the physiological noises to be encountered during in vivo human brain measurements [2]. Furthermore, the accurate localization of the absorption perturbation based on the depth compensation algorithm leads to appropriate selection of ROIs, which consequently improves the accuracy in quantification that cannot be achieved by the conventional DOT reconstruction without depth compensation.
Overall, in this study, we have presented an approach based on our recently developed depth compensation algorithm to quantify the absorption perturbation in DOT with reasonable recovery rates. By applying this approach, quantification of absorption perturbation can be recovered with a best rate of 64% in simulative experiments. Moreover, the comprehensive comparison on the noise suppression demonstrates that the combination of the depth compensation algorithm with ROI-based spatial prior offers a reliable and accurate depth localization and improved quantification in DOT at variable noise levels. Moreover, while our approach targets primarily on the application of functional brain imaging, the improvements in quantification potentially can be extended to other applications of DOT, e.g., the detection of cancer where reflectance geometry is used.
This work was supported in part by National Institute of Health (NIH) funding from NINDS (4R33NS052850-03).
1. A. Villringer and B. Chance, "Non-invasive optical spectroscopy and imaging of human brain function," Trends Neurosci. 20(10), 435–442 (1997). [CrossRef] [PubMed]
2. D. A. Boas, A. M. Dale, and M. A. Franceschini, "Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy," Neuroimage 23(Suppl 1), S275–S288 (2004). [CrossRef] [PubMed]
3. C. K. Lee, C. W. Sun, P. L. Lee, H. C. Lee, C. Yang, C. P. Jiang, Y. P. Tong, T. C. Yeh, and J. C. Hsieh, "Study of photon migration with various source-detector separations in near-infrared spectroscopic brain imaging based on three-dimensional Monte Carlo modeling," Opt. Express 13(21), 8339–8348 (2005). [CrossRef] [PubMed]
4. R. Arridge, "Optical tomography in medical imaging," Inverse Probl. 15(2), R41–R93 (1999). [CrossRef]
5. H. Niu, F. Tian, Z. J. Lin, and H. Liu, "Development of a compensation algorithm for accurate depth localization in diffuse optical tomography," Opt. Lett. 35(3), 429–431 (2010). [CrossRef] [PubMed]
6. H. Niu, Z. Lin, F. Tian, S. Dhamne, and H. Liu, "Comprehensive Investigation of three-dimensional diffuse optical tomography with depth compensation algorithm," J. Biomed. Opt. in press.
7. M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, "Methods of quantitating cerebral near infrared spectroscopy data," Adv. Exp. Med. Biol. 222, 183–189 (1988). [PubMed]
8. H. Dehghani, B. W. Pogue, J. Shudong, B. Brooksby, and K. D. Paulsen, "Three-dimensional optical tomography: resolution in small-object imaging," Appl. Opt. 42(16), 3117–3128 (2003). [CrossRef] [PubMed]
9. G. Boverman, E. L. Miller, A. Li, Q. Zhang, T. Chaves, D. H. Brooks, and D. A. Boas, "Quantitative spectroscopic diffuse optical tomography of the breast guided by imperfect a priori structural information," Phys. Med. Biol. 50(17), 3941–3956 (2005). [CrossRef] [PubMed]
10. E. R. Hom, http://www.nmr.mgh.harvard.edu/PMI/toolbox/index.html
11. L. Wu, "A parameter choice method for Tikhonov regularization," Electron. Trans. Numer. Anal. 16, 107–128 (2003).
12. P. C. Hansen and D. O'Leary, "The use of the L-curve in the regularization of discrete ill-posed problems," SIAM J. Sci. Comput. (USA) 14(6), 1487–1503 (1993). [CrossRef]
13. B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, "Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography," Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007). [CrossRef] [PubMed]
Article Order
A. Villringer and B. Chance, "Non-invasive optical spectroscopy and imaging of human brain function," Trends Neurosci. 20(10), 435–442 (1997).
[Crossref] [PubMed]
D. A. Boas, A. M. Dale, and M. A. Franceschini, "Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy," Neuroimage 23(Suppl 1), S275–S288 (2004).
C. K. Lee, C. W. Sun, P. L. Lee, H. C. Lee, C. Yang, C. P. Jiang, Y. P. Tong, T. C. Yeh, and J. C. Hsieh, "Study of photon migration with various source-detector separations in near-infrared spectroscopic brain imaging based on three-dimensional Monte Carlo modeling," Opt. Express 13(21), 8339–8348 (2005).
R. Arridge, "Optical tomography in medical imaging," Inverse Probl. 15(2), R41–R93 (1999).
[Crossref]
H. Niu, F. Tian, Z. J. Lin, and H. Liu, "Development of a compensation algorithm for accurate depth localization in diffuse optical tomography," Opt. Lett. 35(3), 429–431 (2010).
H. Niu, Z. Lin, F. Tian, S. Dhamne, and H. Liu, "Comprehensive Investigation of three-dimensional diffuse optical tomography with depth compensation algorithm," J. Biomed. Opt.in press.
M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee, "Methods of quantitating cerebral near infrared spectroscopy data," Adv. Exp. Med. Biol. 222, 183–189 (1988).
H. Dehghani, B. W. Pogue, J. Shudong, B. Brooksby, and K. D. Paulsen, "Three-dimensional optical tomography: resolution in small-object imaging," Appl. Opt. 42(16), 3117–3128 (2003).
G. Boverman, E. L. Miller, A. Li, Q. Zhang, T. Chaves, D. H. Brooks, and D. A. Boas, "Quantitative spectroscopic diffuse optical tomography of the breast guided by imperfect a priori structural information," Phys. Med. Biol. 50(17), 3941–3956 (2005).
E. R. Hom, http://www.nmr.mgh.harvard.edu/PMI/toolbox/index.html
L. Wu, "A parameter choice method for Tikhonov regularization," Electron. Trans. Numer. Anal. 16, 107–128 (2003).
P. C. Hansen and D. O'Leary, "The use of the L-curve in the regularization of discrete ill-posed problems," SIAM J. Sci. Comput. (USA) 14(6), 1487–1503 (1993).
B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, "Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography," Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
Arridge, R.
Boas, D. A.
Boverman, G.
Brooks, D. H.
Brooksby, B.
Chance, B.
Chaves, T.
Cope, M.
Culver, J. P.
Dale, A. M.
Dehghani, H.
Delpy, D. T.
Dhamne, S.
Franceschini, M. A.
Hansen, P. C.
Hsieh, J. C.
Jiang, C. P.
Lee, C. K.
Lee, H. C.
Lee, P. L.
Lin, Z.
Lin, Z. J.
Miller, E. L.
Niu, H.
O'Leary, D.
Paulsen, K. D.
Pogue, B. W.
Reynolds, E. O.
Schlaggar, B. L.
Shudong, J.
Sun, C. W.
Tian, F.
Tong, Y. P.
van der Zee, P.
Villringer, A.
White, B. R.
Wray, S.
Wu, L.
Wyatt, J.
Yang, C.
Yeh, T. C.
Zeff, B. W.
Adv. Exp. Med. Biol. (1)
Appl. Opt. (1)
Electron. Trans. Numer. Anal. (1)
Inverse Probl. (1)
J. Biomed. Opt. (1)
Neuroimage (1)
Opt. Express (1)
Opt. Lett. (1)
Phys. Med. Biol. (1)
Proc. Natl. Acad. Sci. U.S.A. (1)
SIAM J. Sci. Comput. (USA) (1)
Trends Neurosci. (1)
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Fig. 6 Quantified (a) Δµa_max , (b) Δµa_ROI and (c) VROI values of the absorber, with the given setup in Experiment I, when α and γ values are varied. The expected Δµa value is 0.2 cm−1. The expected volume of the absorber is 1.7 cm3. Conventional DOT without depth compensation is equivalent to γ = 0.
Table 1 Actual absorption perturbation, Δµa , maximum absorption perturbation recovered without ROI, Δµa_max , and absorption perturbation recovered with ROI, Δµa_ROI , for each absorber in experiments I to III. Unit: cm−1
Equations on this page are rendered with MathJax. Learn more.
(1) Δ O D ( λ , t ) = ∑ j = 1 N v o x Δ μ a , j ( λ , t ) L j ( λ )
(2) y = A x
(3) x ^ D O T = A T ( A A T + α s max I ) − 1 y
(4) M = [ s max , N l a y e r I s max , N l a y e r − 1 I ... s max , 1 I ] γ
(5) x ^ D C A = ( A M ) T [ ( A M ) ( A M ) T + α s ′ max I ] − 1 y
(6) y = K A x ^ D C A
(7) y = Δ μ a _ R O I ∑ j ∈ R O I L i , j
Actual absorption perturbation, Δµa , maximum absorption perturbation recovered without ROI, Δµa_max , and absorption perturbation recovered with ROI, Δµa_ROI , for each absorber in experiments I to III. Unit: cm−1
DOT without depth compensation
DOT with depth compensation
Experiment Absorber Δµa Δµa_max Δµa_ROI Δµa_max Δµa_ROI
I 1 0.2 0.059 0.059 0.070 0.122
II 1 0.1 0.024 0.022 0.027 0.047
2 0.2 0.048 0.048 0.058 0.104
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The Rocky and Bullwinkle Show
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redpajama_set_name: RedPajamaCommonCrawl


A brand is the sum of its parts.
I work on those parts.
I'm an independent designer who teams up with established businesses, startups, and creative agencies to help build brands.
I've spent my career crafting logo identities to print materials to websites and everything in between for brands large and small, new and old, rich and poor, online and off. Whether you're a fledgling startup looking for a foundation or an established company with a couple missing pieces, I pride myself on the ability to plug in. Regardless, expect a strategic, creative, and people-centered approach.
I've got extensive experience designing identities that are simple, versatile, and unique. Whether you're a startup, non-profit, small business or corporation, a logo is an important, foundational touchpoint.
A collection of logo marks.
I'm Partner in a web company called Web Concentrate. We spend our days building simple and complex marketing and eCommerce websites that solve business problems.
I'll work with you to establish a unique, strategic vision that unifies all of your brand's touchpoints. Bring me into to play a part in the effort or run the whole show for you.
Image from a campaign and website for Daiya Foods.
Digital has taken over, but print is not dead. Modern business still has needs for tactile, real life design that ends up in a customer's hands.
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text:
Joseph Hnylka
(954) 262-6191 jh511@nova.edu
J.D., The University of San Diego School of Law, 1988
Joe Hnylka, pronounced "Nell-kuh," hails from Cleveland but lived in California and practiced law there for many years. Licensed to practice in both Ohio and California, Hnylka earned a bachelor's degree from the Ohio State University in 1983, where he made the dean's list, competed in debate and took part in a study abroad program that took him to Oxford University in England for studies. He earned a law degree in 1988 from the University of San Diego School of Law, where he served as executive editor of the San Diego Law Review from 1987-1988. He worked as a civil litigator in private practice for two California law firms until 1992 when he began his teaching career serving as full-time instructor and as an adjunct professor at USD Law School until 1996. From 1996 until 2000, Hnylka taught legal research and writing, appellate advocacy, and the American legal system for foreign attorneys at the University of the Pacific, McGeorge School of Law. He was an assistant professor at Gonzaga University in Washington until May of 2008.
Professor Hnylka's scholarship interests are varied, although his primary interests are Jurisprudence, Legal Research and Writing, Remedies, and Civil Procedure. His most recent article is titled: Continuing To Litigate After You Have Won: Courts Defy Article III to Avoid Mooting TCPA Class Actions, Despite Defendants' Rule 68 Offers of Complete Relief. It was published in volume 64 of Drake Law Review in Spring 2016. In addition, Professor Hnylka is a frequenter presenter at conferences. Most recently, he made a presentation at the Legal Writing Institute's 2016 National Biennial Conference held in Portland, OR. The presentation was titled: Incorporating Social Justice into the LRW Classroom. Professor Hnylka was recently selected to serve on the New Member Outreach Committee of ALWD (Association of Legal Writing Directors) for 2017-2018.
Legal Research & Writing
Governmental Tort Liability
Teaching Pedagogy
View Resume


Note: In the UK, the term of 'Learning disability' is used instead of 'Intellectual disability'.
Welsh theatre company Hijinx has launched a casting agency for learning-disabled actors, in an attempt to give them more exposure in the industry.
Hijinx, which stages work with and trains actors with learning disabilities, is creating the online agency with digital innovation funding from the Arts Council of Wales and Nesta.
Based on a similar model to Spotlight, the agency is the result of a project exploring how better to support learning-disabled actors in Wales.
Over the past four years, Hijinx has grown to encompass five training academies for learning-disabled actors, at four locations spread across Wales. It now works with about 60 actors with learning disabilities, who will be represented on the casting site.
Vanessa Morse, Hijinx's marketing manager, said: "Our job is to pioneer training for learning-disabled actors but also promote them to the industry and get work for them. Learning-disabled people have a low profile, so how are we going to promote them to the industry?
Each actor's page on the site will be accompanied by an introduction to themselves, including information such as the types of work for which they are suitable.
This entry was posted in Intellectual Disability and tagged Hijinx Actors, Intellectual disability.


text:
Police find 4 guitars stolen from Nils Lofgren…
NewsCrime and Public Safety
Police find 4 guitars stolen from Nils Lofgren in Dallas
PUBLISHED: April 2, 2018 at 12:00 a.m. | UPDATED: September 12, 2018 at 12:00 a.m.
DALLAS (AP) — Dallas police have recovered four guitars stolen from Nils Lofgren before the longtime member of the E Street Band was due to perform in the city.
Dallas police tweeted Monday that they also have made an arrest in the theft. Cpl. Debra Webb provided no further details.
Lofgren tweeted his gratitude that his instruments have been recovered, saying: "Looks like my guitars are coming back to me."
Police haven"t said if a harp that was also stolen has been found.
The instruments were left in a van parked outside a Holiday Inn Thursday. The theft was discovered Friday morning.
The veteran rocker used borrowed instruments when he performed solo at the Kessler Theater in Dallas on Friday night.
The E Street Band has been Bruce Springsteen"s backing band since 1972.


Natan is proud to announce that the application process for Natan's 2013 grants is now open! We've made several changes to our grant areas for the 2013 Grants, all of which are explained below.
Natan is now accepting initial Letters of Inquiry (LOI) from new applicants in two program areas: Emerging Models of Jewish Connection in North America and Jewish Peoplehood. All of the necessary information (and a new FAQ page) can be found on the Grants pages of our website. Organizations may apply for grants of up to $50,000; LOIs are due by noon ET on Monday, April 23, 2012. Following a multi-stage application process, grantees will be notified of their awards by January 31, 2013.
Jewish Peoplehood grants support innovative organizations that build connections between young Jews across national, ethnic, denominational, and ideological borders; that foster a sense of mutual responsibility between Jews; that emphasize the Jewish people's collective responsibility to heal the world; and/or that grapple with the special role that Israel plays in contemporary Jews' identity.
Natan's program areas are constantly evolving, reflecting the evolution of Natan members' aggregate philanthropic interests and the changing needs of the Jewish community.
For 2013, Natan will no longer be making grants in two program areas: Advancing Inclusiveness in the North American Jewish Community and New Media. We found over the course of last year's grant review process that all of our grant committees were integrating the values and principles of these committees – including traditionally marginalized populations in Jewish life, and using cutting-edge new technologies to strengthen and enrich Jewish experiences – into their grantmaking, and thus we are explicitly making these principles a part of our grantmaking across all of our grantmaking, rather than running separate grant processes for them.
Natan's longstanding Economic Development in Israel committee will be undergoing a strategic planning process. We will be exploring potential new partnerships for this grant area, inviting select organizations to apply for new grants, considering renewals from our existing grantees, and conducting an internal conversation about our goals for this program area.
Our DC Small Grants Initiative will also be on a brief hiatus for the first half of 2012, as we explore several new strategic options for this program area.
More than just a grant program, Natan's partnership with ROI is a way of strategically integrating two networks of dedicated young people who are transforming the Jewish world for the 21st century. By bringing Natan's network of young philanthropists together with ROI's network of young entrepreneurs and innovators, and by creating a dedicated source of funding for ROI members (beyond ROI's existing micro-grant program), both Natan and ROI hope to strengthen and enhance each other's' work while collectively having an even greater impact on the Jewish world than either group could have on its own.
The Natan Grants for ROI Entrepreneurs application process will launch at the ROI Summit in Jerusalem in June, 2012.
Please be in touch with Natan's Program Officer, Jackie Fishman (jackie@natan.org), with any questions.


Home» VideoScribe» Community» VideoScribe questions and answers» Can I make other fonts "draw" well?
Can I make other fonts "draw" well?
Imported fonts don't draw nicely like the fonts that are included with videoscribe. Is there a way to use a font of my choice and make it draw well?
1) type your text in illustrator or inkscape. or copy and paste it in. write a word or a sentence or a paragraph in a font of your choice.
2) convert the text to outlines with color fills (black or whatever color you want your text to be) and no strokes. This step prevents the videoscribe hand from trying to draw the outline of each letter.
3) on a new layer, use the pencil tool with a stroke big enough that you can manually trace (quickly and simply without too much precision) over each of the letters and they will be covered by the stroke. you don't have to be precise. You are basically just adding a path for the hand to follow that APPROXIMATES the motion of writing the letters. it can be one continuous curvy/squiggly line. try to cover each letter before moving to the next.
5) set the opacity of the new stroke to zero so it is transparent.
6) export the finished image as an SVG and play it in videoscribe.
It will be more work than just typing in the text into videoscribe, but you can make pretty much any font draw well. The faster the words draw in videoscribe, the less accurate your transparent stroke needs to be, and the less noticeable any inaccuracies will be.
Looks like your transparent stroke is only about 1 or 2 pixels wide. The transparent stroke needs to be a lot wider (at least as wide as the paintbrush stroke) to reveal the paintbrush stroke underneath.
That's just a guess since you didn't attach the SVG.


The 2nd Orchestra performing 'The Wonderful Adventures of Sindbad the Sailor' in the Royal Albert Hall during their performance there on 2nd February 2002.
Each year the Barnardo's organises an event at the Royal Albert Hall in London to help raise funds for the charity. In 2002 both BYPE and the 2nd Orchestra were invited to be part of this event, and played during the 2nd half of the concert. The first half of the concert was provided by two theatre & dance schools from Berkshire.
The concert was compered by Alex Lovell, seen here interviewing BYPE musical director Lee Stanley at the end of their performance. Alex is well-known to many children as Little Alex on Playhouse Disney on Sky TV.
BYPE played two samba pieces while the 2nd Orchestra played Walton's 'Orb & Sceptre' followed by Douglas Coombes' ''The Wonderful Adventures of Sindbad the Sailor", which was conducted by the composer himself, and narrated by Carole Lindsay-Douglas. The concert concluded with the Second orchestra performing Elgar's "Pomp & Circumstance March No.4 - Land of Hope & Glory".


Tommy Jordan is a rapper, podcaster and audio producer from Bellingham, Washington.
He is best known for his rap collaboration with French DJ Wasaru and as the voice of Special Agent Keith Curry on the "Lauren Proves Magic is Real!" Previous projects include the bands Urban Fantasy and Direwood, and he was the founding member of the artist collective Shepherd Boy Records.
He is also a proud father, husband and ray gun enthusiast.


I've just got a couple of things to tell you before we drop you off to terrorize play with the grandparents.
Worm, your grandparents are old, especially grandpa. Your screaming may make my ears bleed, but it may not be enough for pops to hear what you're saying. Since he's too stubborn to get a hearing aid, you may need to grab his ear and yell directly into it (kind of like how you speak to your sister).
Smushie, there is no dog bowl at the GP's house for you to drink out of. You may have to hydrate with baby formula instead. Also, the GP's don't have any dog toys for you to chew on. It may pose a problem, but I'm sure you will find some power cords or old house slippers to bite into.
Worm, I don't know how you're going to manage at bedtime. I think the old folks go to bed before you do. So if you would kindly tuck them in and sing a song for them, they may fall asleep faster. Grandma needs to have the TV on to watch in her sleep (I'm not sure how that works), so just turn the volume down on the 'mote. Try to put yourself to bed at a reasonable hour, possibly before midnight.
Smushie, there's a lot to explore in the grandparents' house. I suggest wearing a helmet for when you pull the doilies, table runners, and placemats down. A lot of grandma's knick knacks are delicate and your head may leave a dent in some of them. Plus, grandma thinks that she moved all of the decorations to a higher location. I'm somewhat concerned about this because even though you're almost two and a half feet tall, you're almost as tall as she is.
Worm, make sure Smush doesn't put anything in her mouth that isn't food. Also Wormie, don't push your sister into the walls, doors, floors, toilet, etc., basically don't push her into anything not resembling a couch. That's pretty much all I got. I want you, Worm, to love the GP's like you love eating gummy bunnies. Smushie, I want you to love them like you love…well…you love everything. So just keep the status quo!
I know it sounds like we'll be having more fun without you, than with you. Well….it's true!
I will miss you two for a while, but then I'll adapt lots and it will be very strange being without my babies for 3 whole days. (It's the longest I'll have ever been away from you.) It will be a good test for me before we send you off to boarding school to see what life will be like when you start going to elementary school. Be good to your grandparents and uncle Faldo, for we will be back to see them again at Christmas!
The Cheapest Playpen We Found! U-Haul – $3. Fits Two Children.


text:
WhatsApp is testing a new compression method for images
The compression will result in better legibility of text in images
appocalypse newslatest tech newstechnologywhatsapp
By Divij Sonak On Sep 17, 2017
WhatsApp is known for continuing to push incremental updates and under the hood features to its app to improve performance and stability. One of these updates though could go a long way in improving the quality of the text you see on images shared across the app and by quality we mean visibility-wise and not necessarily content-wise.
WhatsApp Beta info site WABetaInfo is reporting that WhatsApp is testing a new method of compressing images that goes a little soft on the text embedded in them. According to the report, WhatsApp will scan images shared to determine if there's any textual content in them and if it finds some, it will compress the images in a different way to ensure the text doesn't get unnecessarily compressed. This could result in text in images retaining its sharpness while still leading to a reduced file-size as compared to what would be in the absence of compression.
The feature is being tested in beta version 2.17.339 of the app on Android with it being disabled on iOS for now, although it should make it to Apple's platform in the near future. WhatsApp will continue to use its older method to compress images without text in them. For some users, this may well be another privacy concern in that if WhatsApp can make out text from images, it shouldn't be significantly harder to make out the content of the text either. But that is a discussion for another time; for now we await the feature's public roll out which is uncertain at this point as to when will it happen.
Source WABetaInfo
WhatsApp has launched Verified Business Accounts
HBO releases a 5-minute preview of Game of Thrones: Conquest and Rebellion
Google Services Down – Docs, Gmail, Hangouts, YouTube
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iPhone 12 Pro And Pro Max Are Apple's Smartphones For Pro Enthusiasts
iPhone 12 5G Launched By Apple, Starting $699


New York – As the fourth-quarter launch date of DirecTV Now nears, AT&T Entertainment Group senior vice president, strategy and business development Tony Goncalves said the over-the-top service won't be riding the skinny bundle bandwagon like some of its competitors.
DirecTV Now is expected to offer more than 100 channels of live and on demand programming, targeted at younger viewers. But at the Next TV Summit here Wednesday (Oct. 20) during NYC Television & Video Week, Goncalves said while skinny bundles may work for niche offerings, that's not the case for a broader service like DirecTV Now.
Goncalves also addressed some pay TV execlutives like Comcast chairman and CEO Brian Roberts, who has said he doesn't believe there is a business model for OTT.


On this week's show we discuss the attack against U.S. Ambassador to South Korea Mark Lippert with Korea Chair Victor Cha. Dr. Cha explains what the attack tells us about the ROK-U.S. alliance and how to understand the South Korean public reaction to the stabbing. Then Korea Chair Research Associate Marie DuMond interviews Ambassador Kim Young-mok, the president of Korea's International Cooperation Agency (KOICA), on South Korea's aid priorities and where KOICA can cooperate with the United States. We also cover the region's news, profile Australia's Gillian Triggs in the featured leaderboard, and share our One to Watch.
Hosted by Colm Quinn. Audio edited by Samuel Ellis. Produced by Jeffrey Bean.


The 2019 USATF Indoor Championships are this weekend in Staten Island. And while the distance fields aren't quite as strong as they would be in a year in which there is a World Indoor team to make, rest assured that we are not skipping writing this preview to write a preview about the Boston University Last Chance Invitational instead. This is still a national championship, after all, and it demands our respect.
Because there are obvious favorites in many of the events, we're not going to do our usual detailed event-by-event breakdown of each race. You don't need us to tell you that Ajee' Wilson and Shelby Houlihan are good at running. Instead, let's hit on the biggest storylines you should be watching for this weekend in Staten Island.
If you look at the entries for the men's 600, men's 1000, and women's 600, it's pretty obvious who is going to win those races at USAs. Donavan Brazier, fresh off his 1:44.41 American 800 record at Millrose, will be heavily favored in the 600. Olympic bronze medalist Clayton Murphy, who has run 1:45 and 3:53 this year, is the clear pick in the men's 1000. And barring some sort of disaster — such as the faceplant that derailed her at this meet in 2015 — American indoor/outdoor 800m record holder Ajee' Wilson should win the women's 1000.
We don't want to just hand these athletes a national title. All will have to survive their prelims on Saturday, and in an indoor race — particularly the 600 — one tactical mistake can cost you dearly. Erik Sowinski, running the 600, doesn't have Brazier's top-end speed, but he's a strong tactical runner, especially indoors, where he has won three national titles across the 600 and 800. World Indoor silver medalist Drew Windle (600) and 2017 USA outdoor 1500 champ Robby Andrews (1000) have both accomplished big things in their careers, and though neither has been in great form this year, they have to at least be mentioned at a national championship. Ce'Aira Brown has run 1:59 and 2:39 (1k) this year and won't be a pushover.
If there's an upset, great. But Brazier, Murphy, and Wilson should win their finals on Sunday, which means we need another reason to get excited. And we have one: the clock. If there is a benefit to racing oddball distances like the 600 and 1000 (and it's here we point out that it's absurd that both of these events are being run at USAs this year, dividing the talent pool and depriving us of a Brazier vs. Murphy showdown), it is that American and world records are often on the table. Let's examine each star's record chances this weekend.
Of the three events, Saruni's 600 world record is the most vulnerable. He set it just last year in Albuquerque, seven weeks before running 1:45.15 to win the NCAA title. Brazier has run 1:44.41 this year — ranking him #5 all-time indoors — and he's definitely more of a 600/800 than 800/1000 guy. Remember, this is a guy who split 45.92 in the 4×400 in high school and ran 1:14.98 in a 600 time trial (outdoors) during his freshman fall at Texas A&M (of course, we guess we should also have you remember he went sub-4:10 in the mile in HS). On January 19 of this year, he ran 1:15.46, but that came just three weeks after he resumed track workouts coming back from an Achilles injury. He's had another month of training since then, which means 1:14.79 is well within his reach. And given that the safest way for Brazier to win is to avoid traffic and take the race out hard, we would not be surprised at all to see the WR go down.
Murphy is one of the biggest 800/1500 talents the United States has ever produced, so it would be fitting if he could claim the American record in this discipline. As mentioned above, he's already run 1:45.92 (on a flat track) and 3:53.30 this year, and considering the lifetime PRs of the American record holder (the late David Torrence) were 1:45.14 and 3:52.01 (both outdoors), 2:16 certainly seems attainable for Murphy in the right race.
Using the NCAA's conversion table, 1:45.92 on a flat track is worth 1:44.43 on a banked track. And LRC stats guru John Kellogg says that 1:44.43 for 800 is worth 2:16.23 for 1000 (1:45.92 is equivalent to 2:18.24, per JK). JK's algorithm doesn't deem his 3:53.30 mile at Millrose quite as impressive, converting it to 1:45.60 for 800 (banked track), but remember that Murphy could have run faster in that race had he not gone out hard chasing Yomif Kejelcha.
More context: Murphy ran 2:18.64 in his last crack at the 1000, back in Seattle on January 12. He's definitely fitter now, but two seconds is a lot to make up. In his last attempt at the distance outdoors, in 2017, he ran 2:17.17 in a failed bid at Rick Wohlhuter's 2:13.9 American outdoor record.
The biggest issue for Murphy is that this is a championship final, which means that if he's going to attack the record, he'll have to do it on his own. But if he commits to getting after it, the American record could fall. The world record of 2:14.20 would require a monster effort and some terrific pacemaking; it's not going down this weekend.
Wilson has run over a second faster this indoor season at 800 meters (an American record of 1:58.60) than AR holder Jen Toomey ever ran, indoors or outdoors (her pb is 1:59.64). And in the 1500, Wilson's PR of 4:05.18 is superior to Toomey's 4:06.24. That gives Wilson a very good shot at the American record, should she choose to chase it (JK converts her 1:58.60 to 2:34.79 for 1k).
The world record is the more interesting proposition, but we don't think it will fall. Maria Mutola, the 2000 Olympic champion, was a total stud, especially indoors, where she won seven world titles in the 800 and ran an indoor PR of 1:56.36. Last year, Caster Semenya took a shot at a fast 1000 outdoors in Berlin and her time (2:30.70) was barely faster than Mutola's indoor WR. So it's hard to imagine Wilson running just as fast without any competition in February.
LRC predictions: Brazier gets the WR and Wilson gets the AR, but Murphy falls just short of the AR.
2) Can Shelby Houlihan make it 10 straight US titles?
Houlihan is entered in both the mile (Saturday) and 2-mile (Sunday) at USA Indoors and it would be a shock if she did not win both of them. She has faster PRs, a better kick, and a bigger engine (14:34 American record in the 5k) than anyone else in the field; outside of a fall, illness, injury, or act of God, it's hard to envision any scenario in which Houlihan loses.
The more interesting question: when is the next time anyone beats Houlihan at USAs? Houlihan, who turned 26 earlier this month, is in her prime, and last year's win over Jenny Simpson (now 32 years old) at USAs felt like a changing of the guard. That brought an end to four straight US outdoor 1500 titles for Simpson, and Houlihan could easily reel off a similar streak in that event. Maybe a collegian — Colorado's Dani Jones? — rises up to challenge her, but Houlihan is at such a high level right now — we ranked her #3 in the world at 1500 last year — that she would have to decline for anyone else in the US to have a chance.
Things can change quickly in this sport, so we don't feel comfortable predicting beyond a couple of years. But our bet is that Houlihan's next defeat at USAs comes at some point after the 2020 Olympic Trials.
One more note on the women's mile/2-mile: both races will be run as two-section finals. Houlihan, though she has no seed time for either race, will be in the "fast" section of both.
Houlihan's Bowerman TC teammate Colleen Quigley was not as fortunate. Quigley, like Houlihan, did not have a qualifying time and Monday said that if there was not a second section, she would not have been accepted into the meet at all. But because enough women hit the auto standard to necessitate two sections, USATF has the prerogative to fill the second section, which is how Quigley got a bid.
Given that Quigley is the top seed in the mile, and the mile comes first (it's on Saturday, the 2-mile is on Sunday), Quigley could simply scratch the 2-mile. But if she chooses to run, she will do so in the slow section.
The biggest impact that the two-section finals will have is that the runners in the "fast" section cannot afford to dawdle, otherwise they'd risk being displaced by athletes from the "slow" section.
Yes, we'd love to see Paul Chelimo, Matthew Centrowitz, and Evan Jager in these races. But none of those men — who have combined to win 19 US titles on the track — will be in action this weekend as Chelimo is making his half marathon debut in New York next month while Centro and Jager have been conspicuously absent from all racing this year. That means that some younger athletes have a chance to shine in Staten Island.
In fact, of the 34 men entered across the mile and 2-mile as of Thursday (12 in the mile, 23 in the 2-mile; Eric Avila is entered in both), only one has ever won a US title on the track: 33-year-old Will Leer, who claimed the mile and 3k crowns in 2013. Considering Leer hasn't made a US final in almost five years, we don't expect him to add to his total this weekend.
So who can win? Sunday's mile should come down to Craig Engels of the Nike Oregon Project and World Championship finalist Johnny Gregorek of NJ*NY Track Club. Gregorek edged Engels by .02 at USA Outdoors two years ago for the final spot at Worlds, and we're sure Engels would like nothing more than to earn his first national title by returning the favor. He heads in with the edge, having defeated Gregorek head to head at Millrose, by a pretty significant margin (1.34 seconds). But in a championship race, things could be different.
One of those two guys is likely your winner, but keep an eye on Oregon alum Sam Prakel, who beat both Engels and Gregorek at USA Outdoors last year to finish 7th. He was last at Millrose in 3:59, but that was due in large part to his decision to run super-aggressively early on (57.71 at 409, 1:26.84 at 609).
In the 2-mile on Saturday, the three names that jump out are Izaic Yorks, Drew Hunter, and Sean McGorty, all of whom are 24 or younger. Yorks was the US runner-up at 1500 outdoors last year but has only raced twice indoors; a 7:55 3k in Seattle on January 12, and a 3:58.6 split on the anchor to help his Brooks Beasts squad claim the 4 x mile world record on January 26. Hunter was last seen finishing 9th at USA XC on February 2 — a strong performance for a 1500/5k guy in his first ever 10k — while McGorty looked great in running 13:21 to knock out the Worlds standard for 5k at BU on February 8.
That's not a ton to go off, but we'll take McGorty, the 2018 NCAA outdoor 5k champ, FTW as he looked very smooth in the 5k in Boston. If he hasn't dropped Yorks with 100 to go, however, he could be in trouble against the 1500 specialist. We should also note that, because there are 23 guys entered, the 2-mile will be run as two sections. And because Hunter doesn't have a qualifying time, he may end up having to run in the slow section, which would make it even tougher for him to win.
One last thing: it's a joke that there are going to be two-section finals in the 2-mile. Either a) Put everyone in one heat with some sort of massive three-turn stagger or b) Do what the NCAA does and say you'll accept a maximum of 16 people per event.


text:
Q: Angularjs: event preload background images I'm building a slider with AngularJs using a directive but now I have a problem. This is the code of my home.html

<slider-directive images="images" ng-show="$root.showSlider" ng-if="images"/>

Now I show the snippet the controller:
.controller("HomeController",["$scope","$rootScope", function($scope, $rootScope) {

            (function getData() {
                    //do async call that return data
                    $scope.images = data;
                }, function(err) {
                    console.log(err);
                });
            })();
        }
    ]);

And Now I can show the code of my directive:
.directive('sliderDirective', function($rootScope, $interval) {
            return {
                restrict: 'E',
                templateUrl: 'html/.../slider.html',
                replace: true,
                scope: {
                    images: '='
                },
                link: function(scope, element, attrs) {

                    scope.currentIndex = 0;

                    scope.next = function() {
                        scope.currentIndex < scope.images.length - 1 ? scope.currentIndex++ : scope.currentIndex = 0;
                    };

                    scope.prev = function() {
                        scope.currentIndex > 0 ? scope.currentIndex-- : scope.currentIndex = scope.images.length - 1;
                    };

                    scope.$watch('currentIndex', function() {
                        scope.images.forEach(function(image) {
                            image.visible = false; // make every image invisible
                        });

                        scope.images[scope.currentIndex].visible = true; // make the current image visible

                        $rootScope.showSlider = true;
                    });

                    $interval(function() {
                        scope.next();
                    }, 10000);
                }
            };
        });

Now my problem is understand when the images in background, that are in slider, are loaded so I can remove the loading and show the slider without bad effect. Is there a method that tell me when all images or the first image are loaded? thanks

A: You can simply attach 'load' event handler for the first image (or for all images - it depends from you). Have a look at this question:
AngularJS - Image "onload" event
SamBarnes created a simple and nice directive, which lets you easily attach load event handler and connect it with function in angular controller.
Edit
I present a sample solution with imageLoaded directive.
1. image-loaded directive
angular.module('your_module')
    .directive('imageLoaded', ['$parse', ImageLoadedDirective]);

function ImageLoadedDirective($parse) {
    return {
        restrict: 'A',
        scope: {
            imageLoaded: '&'
        },
        link: function (scope, elem, attrs) {
            elem.on('load', function (event) {
                scope.$apply(function () {
                    if (angular.isFunction(scope.imageLoaded)) {
                        scope.imageLoaded();
                    }
                });
            });
        }
    };
}

2. slider template (a simple ng-repeat with images)
...

...

3. slider directive
...

link: function(scope, element, attrs) {

    // Number of currently loaded images
    scope.loadedImagesCount = 0;

    // Function executed when image is loaded
    scope.imageLoadHandler = function() {

        // Increase counter
        scope.loadedImagesCount++;

        // Check if number of loaded images equals slider's images count
        if(scope.loadedImagesCount == scope.images.length) {

            // Do something, when all images are loaded
            alert('all loaded');
        }
    }

...

Explanation
I attached image-loaded directive to img element inside ng-repeat and used it to notify slider's controller when each of the images is loaded. In slider directive's scope I added a counter and a simple handler, which increments the counter and checks if all images have been loaded.

redpajama_set_name: RedPajamaStackExchange


Fourteen years ago yesterday I became a momma. Well, I suppose I might say that I began my mothering career nine months before that. I was unwed and terrified, but I faced my circumstance, made up my mind and dove in. I have never regretted my decision to carry that child in my body. (Often I'd regretted marrying her father the month before she was born, but how can I even regret that? I would not have the other children or life experiences I have today.) Two years later another daughter was born to me, and roughly two years later my first son. Within twelve years time I'd borne seven children; four girls and three boys. I will not bear any more.
When I knew I was going to be a mother I also committed to raise any children born to me. I would not send them to day-care so that I could work or have free time, I would not send them to public school for their education, I would even go easy on the baby-sitters. I have sacrificed and worked my tail off to stay at home with them, to give them my constant love and attention, to the best of my ability. Yep, some days I still feel like I am a total failure, but overall I see it is paying off and I certainly feel it's been rewarding. We have more joy and love than frustration and sorrow. They have a human for a momma, is all. They are little humans, too. But we are humans communicating and working together at this life thing. This is the greatest responsibility and the greatest privilege I will ever know.
My youngest is not quite three years old. If I reckon on him being out of the house by age twenty I am not yet halfway through this parenting thing. Actually, I hear it never really ends and I'm sure I will be mothering until I die, but I may be able to focus on some other things when my youngest goes off to find his way in the world, maybe even before. We'll see. Today I look at the fourteen year old beauty standing as tall as myself, eyes sparkling with wit, laughing the most contagious laughter you've ever heard, and I feel at once honored and intimidated. Together we have to ease her into adulthood and wean me. Then her sister after her, their brother after them, etc.. In my head I know how it works; the application is trickier. I try every day to be flexible and understanding, to make good decisions, trying to balance their freedoms and responsibilities for a smooth adjustment from dependent to independent; holding them close to letting them go. Maybe in another twelve years I'll have it down pat and the now-three-year-old will be a piece of cake, though I suspect these things never go the way you plan.
For fourteen years I have had the most beautiful lives so intricately entwined with mine. Seven distinct individuals that the universe saw fit to bring into existence through me. I do not mean this in a providential sense, do not mistake me. Believing, as I did once, that Yehovah himself selected me to be mother to these seven special people is not half as amazing to me as my current understanding of the chance of the impersonal universe. How wonderful to me that I should happen to exist at all, let alone exist so entwined with these other lives! Of all the star stuff in the universe, here we are. I look at any of my children and try to remember that they were once within my body, then my mind zips off in a dazzling regress and I see mankind evolving, our earth evolving, the universe evolving… stars exploding and being reborn, bright gases swirling… And here we are.