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Home > Journals > Journal of Medical Imaging > Volume 2 > Issue 1 > Article
24 March 2015 Automated brain computed tomographic densitometry of early ischemic changes in acute stroke
Berend C. Stoel,	Henk A. Marquering,	Marius Staring,	Ludo F. Beenen,	Cornelis H. Slump,	Yvo B. Roos,	Charles B. Majoie
Berend C. Stoel,1 Henk A. Marquering,2 Marius Staring,1 Ludo F. Beenen,3 Cornelis H. Slump,4 Yvo B. Roos,3 Charles B. Majoie3
1Leiden Univ. Medical Ctr. (The Netherlands)
2Academisch Medisch Centrum (Netherlands)
3Academisch Medisch Centrum (The Netherlands)
4Univ. Twente (The Netherlands)
J. of Medical Imaging, 2(1), 014004 (2015). https://doi.org/10.1117/1.JMI.2.1.014004
2 Material and Methods
2.1 Patient Data
2.2 Reference Standard and Clinical Data
2.3 Automated ASPECTS Method
2.4 ASPECTS Labeling
2.4.1 Optimization of the registration
2.5 Brain Densitometry
2.6 EIC Detection
2.7 Validation and Statistical Analysis
3.1 Method Optimization
3.2 Confounder Analysis
3.3 Associations with Clinical Parameters
3.4 Interobserver Agreement
The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This met hod was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS (R=−0.399 and −0.277, respectively) and with follow-up mRS (R=−0.256 and −0.272), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences <1 point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring.
Approximately 70% of acute strokes are caused by hypoxic (ischemic) brain damage as a result of severe cerebral blood circulation failure.1 The reduced blood flow can lead to infarction of the brain tissue, which can be detected on noncontrast computed tomography (NCCT).2 Ischemic tissue on NCCT is characterized by changes in brain parenchyma that reflect either decreased x-ray attenuation (e.g., loss of definition of the lentiform nucleus) or tissue swelling (e.g., hemispheric sulcal effacement and effacement of the lateral ventricle).3 These changes in brain parenchyma in the acute time window within 4.5 h after symptom onset, i.e., early ischemic changes (EICs), correlate with stroke severity and clinical outcome.4–6
To visually identify and quantify subtle EICs in the middle cerebral artery (MCA) territory, the Alberta Stroke Program Early CT score (ASPECTS) scoring method has been introduced,6 standardizing the detection and reporting of the extent of ischemic hypodensity. The ASPECTS value is calculated for 10 regions of interest at two standardized levels of the MCA territory, one including the basal ganglia and the cortical MCA areas at the level of the basal ganglia and below (M1 to M3, lentiform and caudate nuclei and internal capsule) and one above these structures, the supraganglionic level (M4 to M6). An unaffected MCA territory is assigned a total score of 10. For each area with signs of EIC, i.e., parenchymal hypodensity, one point is subtracted from that initial score.
The reliability of visual EICs detection on NCCT is influenced by several factors, including the time window between CT scanning and symptom onset, the vascular territory of the infarct, the experience of the CT reader,7 availability of clinical history,8 viewing window width, and level settings.9,10 Even among experienced clinicians, there is considerable lack of agreement in recognizing and quantifying early radiologic signs of acute stroke on CT.11–14 Hence, concern has arisen about the reliability of the detection of EICs on CT, while this is the basis for timely treatment, vital for stroke outcome.15 Therefore, objective and automated methods are needed to recognize and quantify ischemic brain damage in an early stage, not only for daily clinical practice but also for large-scale clinical trials on the treatment of ischemic stroke.
The quantitative nature of CT allows for a quantitative analysis of density changes as a sign of EICs. Since the density of ischemic brain tissue decreases with time after stroke onset,16 studies have used brain CT densitometry to identify EICs in acute ischemic stroke. Bendszus et al.17 described a postprocessing method for the detection of acute MCA territory infarcts. By subtraction of density histograms of the entire hemispheres on CT scans, they showed a significant increase in acute infarct detection rate by human observers. Maldjian et al.18 identified potential areas of acute ischemia on CT scans by comparing density histograms of the lentiform nucleus and insula with histograms of the structures on the contralateral brain side. ASPECTS areas were not used in these studies, which limits their applicability in clinical practice. Furthermore, no data were reported on the relation with clinical outcome.
Kosior et al.19 computed an automated atlas-based topographical scoring of acute stroke based on the ASPECTS regions based on MRI. They obtained an auto-ASPECTS from the overlap of ischemic lesions detected on apparent diffusion coefficient maps in MRI data, using atlas-based segmentation of the MCA supply territories. They adopted a segmentation method for artery supply territories in the brain20 using the Fast Talairach Transform (FTT).21 The FTT method was optimized and validated for MR images, but an extension to CT images is not available.
Therefore, we developed an automated brain CT densitometric method to replicate the manual ASPECTS based on the so-called brain density shift (BDS) between contralateral brain areas, thereby systematically quantifying subtle EICs. We hypothesized that this automated ASPECTS quantification correlates with stroke severity and clinical outcome. We designed and optimized the auto-ASPECTS method using a training set of images. Subsequently, the method was validated against manual ASPECTS determined by consensus, and stroke severity at presentation (National Institutes of Health Stroke Scale, NIHSS) and clinical outcome after 7 to 10 days (NIHSS) and 3 months (modified Rankin Scale, mRS) in a separate test set. The agreement between automated and consensus scoring was compared with the interobserver agreement for the global scoring data and per ASPECTS region.
As part of a prospective randomized multicenter controlled trial22 on acute ischemic stroke, patients were included retrospectively for this study during the period of July 2008 until April 2011. Out of these 73 patients, we randomly selected a training set of 10 patients to optimize the automated method and this optimal method was subsequently evaluated in a separate population of 63 patients (see Table 1). All patients came from densely populated areas and, therefore, had short times from symptom onset to scan. All image data were anonymized to ensure blind assessments. The study protocol was approved by the medical ethics committee and all patients or their legal representatives provided written informed consent.
Patient characteristics in the training and test image sets.
Test set
Follow-up mRS≤2
Follow-up mRS>2
Number of patients 10 24 39 63
Male/female 6/4 12/12 23/16 35/28
Age (years), mean±standard deviation 66.6±17 68.0±13 65.8±13 66.6±13
Symptom onset-to-scan time (h), first to third quartile 0∶46−2∶36 1∶00–2∶59 0∶51–2∶49 0∶58–2∶52
All images were acquired with a 64-slice CT scanner (Sensation 64, Siemens Medical Solutions, Forchheim, Germany) with syngo CT 2007s software, using a tube voltage of 120 kVp and a current of 323 mA with a pitch factor of 0.85 and 28.8 mm collimation. Axial slices were reconstructed with a thickness of 1.0 mm and a 0.64±0.12 mm (mean±SD) increment, using a moderately soft kernel (Siemens "H31s") and a 512×512 matrix containing the whole brain. The in-plane resolution was 0.46±0.06 mm (mean±SD).
Reference Standard and Clinical Data
Baseline NCCT scans were independently inspected by two experienced radiologists with more than 10 years of experience (C.B.M. and L.F.B.) for signs of EIC occurring in the ASPECTS regions of interest. Additional findings, such as a high degree of atrophy, old ischemic lesions, or scanning artifacts, were also recorded. EICs were defined as parenchymal x-ray hypoattenuation as indicated by a region of abnormally low attenuation relative to other parts of the same structure or the contralateral hemisphere. The observers were blinded for clinical data except for the symptom side. Viewing settings were adjusted based on standard preset viewing window width and center level of 70/35 and 35/30 Hounsfield unit (HU). After individual scoring, discrepancies between both observers were resolved by consensus reading.
Stroke severity was assessed by NIHSS at presentation by the neurologist on duty and clinical outcome was assessed by NIHSS after 7 to 10 days and by mRS at 3 months after symptom onset.23,24
Automated ASPECTS Method
A schematic overview of the proposed method is shown in Fig. 1 divided into three main steps: ASPECTS labeling, brain densitometry, and EIC detection. During ASPECTS Labeling [Figs. 1(a)–(c)], the ASPECTS regions of interest are defined automatically in the NCCT data. Through brain densitometry [Figs. 1(d) and 1(e)], the density histograms are compared between contralateral sides, yielding BDSs in each ASPECTS region. In the final step, EICs are detected based on these BDSs resulting in an auto-ASPECTS score [Fig. 1(f)]. The overall method was implemented in the image processing and development environment MeVisLab (version 2.2.1).25
Flow chart of the automated brain densitometry method based on ASPECTS. (a) The spatial transformation T(x) matching the atlas image with the patient image is computed. (b) The atlas-label image, Latlas(x), is transformed by T(x) to produce a labeled image Lpat(x). (c) The atlas is applied as a mask over the patient image. (d) The density distribution is calculated for each ASPECTS region in the left and right hemisphere; HL and HR, respectively. (e) For each ASPECTS region, i, the brain density shift (BDSi) is calculated between contralateral sides. (f) The EIC are detected by thresholding the BDS values, while accounting for irrelevant defects.
In the following subsections, the different steps are presented in more detail along with the optimization.
ASPECTS Labeling
The voxels are labeled according to their ASPECTS regions using an atlas-based segmentation method, see Figs. 1(a)–1(c). The atlas is composed of two image volumes: an atlas-intensity image, Iatlas(x), containing the original intensity values, and an atlas-label image, Latlas(x), which contains the regions of interest with ASPECTS labels. The atlas-intensity image is registered to the patient's CT image data, Ipat(x), yielding a spatial transformation, T. Subsequently, the same transformation is applied to Latlas(x) to obtain the labeled patient CT data, Lpat(x).
To account for anatomical differences in the ventricular system26 (including asymmetry of the lateral ventricles), two atlases with different ventricular shapes were generated (each containing only one CT scan). An experienced neuroradiologist (L.F.B.) selected two typical CT scans; one with a "nondilated" and one with a "moderately dilated" ventricular system.
ASPECTS regions of interest as described by Barber et al.6 were delineated manually for both image data sets using a freeform editing tool by a trained observer. All ASPECTS regions consist of an eight-slice volume. Delineation was checked for accuracy by an experienced neuroradiologist (L.F.B.).
Image data were registered using the open source software package Elastix,27 version 4.3. Elastix deforms the atlas-intensity image by a coordinate transformation to optimally agree with the patient's image data. First, the atlas images were roughly aligned to the patient image by a rigid transformation. Subsequently, an affine registration with four resolution levels was used to compensate for remaining translation, rotation and scaling differences. Finally, to achieve the most detailed alignment, a nonrigid registration was applied starting with a coarse B-spline grid, which was refined at four subsequent resolutions. For each of these steps, we used an iterative adaptive stochastic gradient descent method29 with a maximum of 3000 iterations. This maximum was set conservatively without optimizing for speed to ensure convergence in all cases. We used a brain mask for focusing the registration in obtaining similarities within the brain, such as the ventricular system. This brain mask was generated automatically by segmentation of the intracranial structures.30 After registration, the ASPECTS labeling of the patient image data was obtained by transformation of the two atlas-label images. After visual inspection of the registered atlases, the one that agreed best with the patient data was selected.
Optimization of the registration
The registration was optimized by assessing its accuracy and precision while varying registration settings. The alignment of contours of the lateral ventricles and the outer surface of the brain was used as accuracy measure. The brain contour was generated from the brain mask, detected as described above,30 and the contours of the anterior horn and body of the lateral ventricles were determined by region growing. If needed, contours were adjusted manually. It should be noted that the manual adjustment was only needed during optimization of the algorithm. Normal analysis was automated without manual interaction. The registration error at a certain location was defined as the shortest distance between the ventricle and brain contours. The mean error and the variation in the errors were used to determine the accuracy and precision for different transform parameters.
We optimized the nonrigid (B-spline) registration with respect to the use of a brain mask and grid size using six different spacings: 5, 8, 10, 20, 30 and 40 mm. The efficacy of the usage of a brain mask was assessed by comparing the accuracy of registering: (1) without a brain mask; (2) using a brain mask covering brain tissue only; and (3) using a morphologically dilated brain mask (9×9×1 kernel size).
Brain Densitometry
For each ASPECTS region, the contralateral histograms of the density values were computed [Figs. 1(d)–1(e)]. The histograms were normalized (area under the curve is 1) to compensate for differences in volume between contralateral ASPECTS regions of interest.
We examined seven different measures for assessing the BDSs by comparing the density histograms of the left and right hemisphere, HL and HR. These measures were either represented by a difference in distribution features (first, second, and third quartiles, average density, and the peak density value) or by a shift in the entire histograms based on minimizing overlap or maximizing cross-correlation. As the affected side is known in clinical practice by functional symptoms, this information was also used in the automated assessment. The histograms and BDS measures of the ASPECTS regions were calculated in a MeVisLab-based software package (see Fig. 2).
Capture of the graphical interface of the automated brain densitometry method based on the ASPECTS regions. The top row represents the different BDS measures per ASPECTS regions. The bottom rows are visualizations of the density distributions of the ASPECTS regions. From left to right, top to bottom: caudate nucleus; anterior and posterior part of internal capsule (for the ultimate quantification, these two regions were merged into one region, conform manual ASPECTS); lentiform nucleus; insular ribbon; M1; M2; M3; M4; M5, and M6. Red (gray) curves represent histograms of the left hemisphere and yellow (white) curves of the right hemisphere. A leftward shift of the yellow curve towards the lower density values indicates a hypodensity in the right hemisphere. The internal capsula area was divided in an anterior and a posterior part, to ensure detection of EICs when only a single part of the capsula was affected.
EIC Detection
Before detecting EICs, we first optimized each measure and evaluated which BDS measure would be the best basis for this detection. This best method was used for the EIC detection and is described in more detail below.
The ability of the different measures of BDS to detect affected ASPECTS regions of interest was optimized regarding their threshold parameters [i.e., DA, DO, D in Eq. (3)] by receiver operating characteristic (ROC) curve analysis using the consensus ASPECTS reading as the reference standard. After optimization, we selected the best method for detecting EICs in an ASPECTS region (i), with the largest area under the ROC curve (see results section). This method is based on the overlap Oi(Δd) between the left and right histogram (HL,i and HR,i, respectively), after applying a density shift, Δd:
Eq. (1)
Oi(Δd)=1−12∑d|HL,i(d)−HR,i(d+Δd)|.
The value Δd between −40 and 40 HU for which Oi(Δd) was largest was defined as the BSDi
BDSi=argmax−40≤Δd≤40Oi(Δd).
In some cases, brain densitometry could be affected by cerebrospinal fluid between the gyri in extensive atrophic brains or old ischemic scars which may be included in the ASPECTS regions, or by imaging artifacts. Therefore, additional findings (including imaging artifacts) were detected separately based on the dissimilarity of the contralateral histograms after applying the density shift by a large difference in the first quartile (BDSq1,i). Subsequently, the results were used to exclude these areas automatically. The ultimate detection of an EICi in the i'th ASPECTS region was thus determined by
EICi={ 0(max−40≤Δd≤40Oi(Δd)<DA∧|BDSq1,i|>DO)∨|BDSi|≤D1|BDSi|>D,
where BDSq1,i is the density shift in the first quartile and optimal thresholds for defect detection DA=84.5% and DO=3 HU; and for EIC detection, D=1.5 HU.
Finally, the automated ASPECTS score was calculated by following the ASPECTS definition:
auto_ASPECTS=10−∑i=110EICi.
Validation and Statistical Analysis
The best method, optimized in the training set, was subsequently validated using a separate test set of 63 patients (see Table 1). In a confounder analysis, correlations were evaluated between variables that should not be associated (for example, between pixel size and BDS). The Spearman's correlation coefficient was used to assess the association between ASPECTS (automated and manual) and the clinical outcome parameters. The variability between automated and consensus ASPECTS and the variability between observers was studied by Bland–Altman analysis. The agreement between ASPECTS measurements was evaluated by first dichotomizing all scores using a threshold of 7 (one of the common cut-off points for treatment6,31), followed by constructing confusion matrices and computing the percent agreement between observers, and between automated and consensus ASPECTS.
Similarly, the agreement between observers and between automated and consensus ASPECTS was also evaluated in detecting EICs in different regions by calculating the percent agreement for each region separately. A McNemar test was conducted in these paired data to determine the significance of the differences in agreement.
A p-value below 0.05 was considered to indicate statistical significance.
Method Optimization
The accuracy and precision of the different registration strategies are represented in Fig. 3. The use of a brain mask improved the ventricle registration accuracy considerably (from 6 to 2 mm), at the expense of a decreased accuracy of brain contour alignment. By dilating the brain mask, the registration accuracy of the brain contours improved with only a minor decrease in accuracy in registering the ventricles. Using B-spline grid sizes of 5, 8, and 10 mm gave comparable accuracies. Therefore, we choose a spacing of 10 mm (to allow for smooth deformations) in combination with a dilated mask in the following experiments.
Box plots of the residual registration errors in the training set of 10 patients, showing the effect of the use of brain masks. The left column shows the residual distances to the ventricle contours and the right column the distances to the brain contours.
Except for the measurements based on a shift in the histogram peaks, all measures had similar ROC areas under the curve, where BDS based on maximizing the histogram overlap with a threshold of 1.5 HU performed best with an area under the curve of 71.6%. This method detected the affected ASPECTS regions in the training set with a sensitivity of 75% and specificity of 74%.
The detection of additional findings (including artifacts) in the ASPECTS regions of various measures was also optimized by ROC analysis, where the visual classification of defects by the radiologists was considered the reference standard. Additional findings were detected best with a sensitivity of 78% and specificity of 81%.
One analysis, including image registration and quantification, takes approximately 15 min on a standard PC. The method has not been optimized for speed, however, and parallelization can significantly reduce analysis time.32
Confounder Analysis
In the auto-ASPECTS analyses, the atlas with the "moderately dilated" ventricular system was used in 10 patients and the atlas with the "nondilated" ventricular system in the remaining 53 patients. It was shown that mRS scores in the dilated group were 1.3 points higher than in the group with nondilated ventricular systems (95% confidence interval (95%CI): [0.1 to 2.5], p=0.03). However, this can be explained by the additional effect of age, since age is associated with mRS (Spearman's R=0.64, p=0.036) and with the enlargement of cerebral ventricles. Since the choice of atlas is based on ventricle dilation, the two groups differ in age by 11 years, 95%CI: [2.8 to 20.0], p=0.01, with an accompanying difference in mRS scores. Therefore, the choice of atlas was not a confounding factor but a correct adaptation to the patient's anatomy.
No other potentially confounding factors were found.
Associations with Clinical Parameters
The correlation between the auto-ASPECTS score and clinical parameters is presented in Table 2, together with the manual ASPECTS, scored separately and in consensus. All patients were included in a multicenter randomized open-label trial, treated with standard alteplase or alteplase with the early addition of intravenous aspirin, where ASPECTS scoring was not used as inclusion criterion. Since early administration of intravenous aspirin had no effect on the outcome, treatment was not considered a confounder in this study.22 Due to the standardization of the acquisition protocol, we used all trial data from one center with the same CT scanner. For both consensus and auto-ASPECTS, there was a weak but statistically significant correlation with baseline NIHSS. The consensus scoring correlated slightly better than the automated methods and individual observers. After 7 to 10 days, the correlation between NIHSS was maintained for the consensus and individual scores, but the automated methods did not correlate significantly. The correlations with mRS score at 3 months were lower than with (baseline and follow-up) NIHSS, with a slightly higher correlation for the automated method. The scores from the individual observers, however, did not correlate significantly with mRS.
Nonparametric (Spearman's rho) correlations among ASPECTS scores and clinical baseline and outcome parameters, in the test image set.
ASPECTS (n=63):
Baseline NIHSS
NIHSS after 7 to 10 days
mRS after 3 months
Auto-ASPECTS −0.277* −0.192ns −0.272*
Observer 1 −0.358** −0.285* −0.107ns
Observer 2 −0.315* −0.273* −0.091ns
Consensus −0.399** −0.367** −0.256*
Note: * Significant at the 0.05 level.; ** Significant at the 0.01 level.; nsnot significant.
Interobserver Agreement
Figure 4 shows the results of the Bland–Altman analysis. The interobserver differences and mean values did not follow a normal distribution, and an association was observed between the interobserver difference and the mean score (Spearman's R=0.67, p<0.01), despite the outlier with a seven- point difference [Fig. 4(b)]. Therefore, there was a significant bias between observers that was dependent on the magnitude of the score, and as a consequence, a reliable limit of agreement could not be calculated. However, the over-all agreement between automated and consensus was comparable to the agreement between observers, with a slightly higher variability between the observers. The average difference (SD) between consensus and auto-ASPECTS was −0.54 (1.20) points, and the average difference (SD) between observers was −0.81 (1.62) points. In 73% of the cases, the auto-ASPECTS deviated from the consensus score by one point or less; the same exact rate occurred for the interobserver agreement: in 73% of cases, the difference was one point or less.
Bland-Altman plots of (a) auto-ASPECTS versus consensus-ASPECTS and (b) differences between observers. The size of the circles and the value inside indicate the number of cases with the same result.
The agreement among the different ASPECT measurements after dichotomizing with a threshold 7 is presented in Table 3. The percent agreement between the individual observers was 83%, whereas the percent agreement between automated and consensus scoring was 89%. These percent agreements did not differ significantly, however, between the two comparisons (McNemar test).
Confusion matrices between observers and between automated and consensus ASPECTS (equal to or less than 7 versus higher than 7), in the test image set.
Agreement:
Observer 1
Observer 2 ≤7 1 10 Auto-ASPECTS ≤7 4 6
>7 1 51 >7 1 52
The agreement in detecting EIC's in the individual ASPECTS regions is shown in Fig. 5, between observers and between the automated method and consensus ASPECTS. Overall, the automated method found more EICs than the consensus scoring (87 versus 52, out of 629 regions, that were free of artifacts) and observer 2 found more EICs than observer 1 (82 versus 31, out of 629 regions). The percent agreement between observers ranged between 67% and 97%, whereas the percent agreement between automated method and consensus ASPECTS ranged between 73% and 89%. In the regions M2, M3, and M6, the interobserver agreement was significantly higher than for the automated method and consensus scoring (McNemar test). Of the 52 manually detected ASPECTS regions, five EICs were missed by the automated method due to the exclusion of these regions due to additional findings such as high degree of atrophy, old ischemic lesions, or scanning artifacts.
Percent agreement between observers (cylinders) and between automated and consensus scoring (blocks), for each ASPECTS region. The lower part of the columns represents the percentage agreement on positive EIC findings (i.e., the percentage of cases where both methods indicate a positive EIC finding), the upper part represents the negative agreement (% agreement of a negative EIC finding). From left to right: caudate nucleus (C); internal capsule (IC); lentiform nucleus (L); insular ribbon (I); M1; M2; M3;M4, M5 and M6.
We have designed, optimized, and validated an automated method for brain densitometry in ASPECTS regions of interest to detect and quantify EICs on NCCT images, using atlas-based segmentation to automatically delineate the ASPECTS regions. In the validation against clinical parameters, we found variable associations. Both manual and automated ASPECTS correlated with baseline stroke severity (NIHSS). The individual manual scores still correlated with NIHSS after 7 to 10 days, where the automated method did not. On the other hand, the manual ASPECTS scoring by the individual observers did not correlate with clinical dependency scores (mRS) after 3 months, where the automated method did correlate. The poor and absent correlations could be due to the early or reversible stage of the ischemic lesion, however, determining the cause of this difference was beyond the scope of this study.
Except for selecting the optimal atlas (normal versus enlarged ventricles), the method is fully automated. For this reason, no test-retest was performed as the remainder of the method is completely observer independent. The atlas-based segmentation method for classifying voxels into specific ASPECTS regions was optimized by minimizing the residual registration errors of the brain and ventricle contours. From these experiments, it became clear that brain masks are needed to focus the image registration to brain tissue only. However, although this improved the alignment of the ventricles, the accuracy of registering the outer contours of the brain deteriorated due to over-focusing. Therefore, a dilated brain mask was proposed in which the gradient between the skull and brain tissue is included in the registration, thereby improving the registration quality of the outer contours without significantly degrading the registration of ventricle contours.
B-spline grid spacing, which determines the smoothness of the deformation, did not influence the accuracy as much as the usage of masks, since sizes between 5 and 10 mm gave comparable results. Therefore, we choose a spacing of 10 mm to allow for the smoothest possible deformations.
This optimized segmentation method obtained an accuracy between 0.45 mm (at the brain surface) and 1.8 mm (at the ventricle). For comparison, Pexman et al.33 reported at least a 10 mm difference in the size of each "M" area between the minimum and maximum chosen by human observers. Despite this variability, they still found an excellent interobserver agreement of ASPECTS scoring. Therefore, the accuracy of our segmentation method for labeling the ASPECTS regions may be sufficient for clinical use.
Signs of EICs were indicated by a BDS between the histograms of contralateral ASPECTS regions. To optimize this BDS method, a comparison with manual consensus scoring was made and we obtained an optimal method with a threshold of 1.5 HU. A sensitivity of 75% and specificity of 74% was obtained.
In the early stages of cerebral ischemia, there is a maximum change of only 1.6 to 4.4 HU within the first 2 to 2.5 h of ischemia.15,34–36 As our patient group was scanned between 0:58 and 2:52 h after symptom onset (first and third quartile), the optimal threshold of 1.5 HU is consistent with this reported range of density changes.
In a study on quantifying EICs by comparing density histograms from contralateral sides,18 the nucleus caudatus, lentiform nucleus, and insula were labeled by registration of the NCCT scan to the Talairach37 template. Unfortunately, no objective measure for the quality of the alignment was reported. Furthermore, this method is not based on ASPECTS regions, which makes it less applicable in clinical practice.
In the MRI atlas-based scoring method from Kosior et al.,19 the average errors in localization of the Talairach landmarks using FTT ranged between 0.08 and 1.49 mm, depending on the location of the landmark in the brain.21 The registration accuracy reported in our study fits well within this range.
In the study by Kosior et al.,19 the mean symptom onset-to-scan time was 11.2±11.7 h (average±standard deviation) for MRI scans, compared to a range of only [0∶58 to 2∶52] h for our NCCT scans (Table 1). As a longer onset-to-scan time increases the visibility of EICs, results from this study are not comparable to our data.
This study has a number of limitations. The method compares density distributions between contralateral sides. As such, it does not account for lesions, such as remote infarcts or encephalomalacia, on the contralateral side. The detection and correction of such lesions could improve accuracy.
Because the patients included in this study live in a densely populated area, onset-to-scan time is relative short. Especially for these patients, it is difficult to detect the (very) EICs and an automated method may have added value. As a result of this short onset-to-scan time, the ASPECTS scores were higher, resulting in a different ASPECTS distribution than in less-densely populated areas with a less extensive public infrastructure or less stroke awareness. Furthermore, since all patients in the population were part of a randomized trial, it was required that they were eligible for alteplase. Guidelines for management of patients with acute stroke suggest that hypodensity is the only meaningful imaging finding in patients eligible for standard recombinant tissue plasminogen activator treatment.38 Therefore, all patients were eligible for standard treatment because they all arrived within 4.5 h after symptom onset. However, because ASPECTS is mainly used in clinical research and not yet in clinical practice, we believe that the added value of this score is highest for the population arriving within 6 h after stroke onset for treatment effectiveness studies. The validation in patients with a longer onset-to-scan time is prudent, but was beyond the scope of this study. Such a validation should be performed with a more diverse patient population with a larger window of onset-to-scan times. Moreover, it is expected that the automated method has a higher accuracy for more severe strokes since the hypodensity increases with time, making the histogram shifts more apparent.
Although techniques have been described that increase visibility of EICs on NCCT scan,9,39–41 comparative data are limited since most methods are based on MR imaging, accuracy is not always reported and none have been correlated to clinical outcome. Although diffusion-weighted MRI is considered the gold standard for assessing EICs, MRI is rarely available in the emergency setting of patients with acute stroke and consequently it is not the standard care.42 Our method was, therefore, developed for NCCT, which is the standard initial imaging examination for acute stroke, since it is fast, inexpensive, widely and easily available, and provides whole brain coverage.43 A limitation of this study is, therefore, that no gold-standard (MRI) of the EICs was available. We could only compare the automated scoring with manual scoring, which had considerable observer variability. The variation of the manual ASPECTS limits the value of the reference measurement, however, accurately it resembles the expected variation in clinical practice. The variation in manual ASPECTS was a main motivation to initiate this study on an objective automated approach knowing that a direct comparison with observers cannot prove any improvements.
The automated ASPECTS method excludes regions where a small old ischemic scar is found (extreme hypoattenuation). Therefore, this rules out situations where new EICs occurs in proximity of an old infarct, which can be picked up by visual scoring. Out of 52 EICs detected by the consensus scoring, 5 were missed by the automated method. As this is a substantial limitation, new image analysis methods need to be developed focused on this particular situation.
Another possible limitation of our method is the fact that histogram analysis does not include local image information. As a result, small focal lesions may not contribute to a detectable shift in the histogram. Therefore, in order to improve the performance of the method in detecting focal lesions, spatial information needs to be included, requiring a different approach. These methods may, however, be less sensitive to subtle BDSs in diffuse hypoattenuation regions. Thus, hybrid approaches, where histogram-based methods are combined with methods that quantify spatial/texture information, may be developed.
Because of the retrospective standardization of the image acquisition protocol, we used CT data from the same CT scanner from only one center. Therefore, additional validation is needed to prove the effectiveness of the method in a general setting.
Some user-interaction was still needed in the study since the appropriate atlas needed to be selected manually. A detection method of the ventricles could be used to determine the ventricle volume and select the dilated or nondilated atlas automatically. Alternatively, the residual normalized cross-correlation after registration could be used as a basis for this atlas selection. Adding more atlases is not needed since the two atlases cover the anatomical variances sufficiently, as the residual registration errors were independent of the atlas used and small errors were obtained over the entire training set.
To evaluate the impact of the different scores on the decision to initiate treatment, when using a threshold value of 7, the data were dichotomized using this threshold. There was agreement between individual observers in 83%, whereas agreement occurred in 89% between automated and consensus scoring. Please note that kappa values were not presented in this study since the kappa statistic is affected by prevalence, and the distribution of low and high scores was indeed skewed in our study population. Using a threshold value of 7, the decision for initiating treatment would be different between the individual observers in 11 out of 63 cases (17%), whereas between automated and consensus scoring, this decision would be different in 7 out of 63 cases (11%). This difference was, however, not statistically significant.
The agreement analysis for each ASPECTS region (see Fig. 5) showed that the observers agreed less in the internal capsule (78%), lentiform nucleus (79%) and insular ribbon (67%), compared to the other regions, whereas the agreement between the automated and consensus scoring was less variable, but generally at a lower level than the observer agreement. In the regions M2, M3, and M6, the interobserver agreement was significantly higher. This difference in agreement was mainly determined by the difference in agreement in negative EIC detections; for the positive detections, the interobserver agreement and agreement between automated and consensus ASPECTS was more evenly divided over the different regions. This may explain the difference in results between local and regional agreement.
In this paper, an automated ASPECTS scoring method has been presented as an alternative to manual ASPECTS score. The method can also be used, however, as a supplementary tool to assist manual scoring. The display of the shifts in density may help physicians in deciding whether a region of interest is affected. The added value of using our tool in such a manner needs to be evaluated in a future study in a population with a wide range of ASPECTS scores. It should be noted that ASPECTS is currently not a part of standard diagnosis workflow but is mainly used in clinical research.4,5 However, with the advent of alternative stroke therapies such as intraarterial treatment, ASPECTS has the potential to be included in the acute setting in the near future.
In conclusion, we have demonstrated the feasibility of a stroke severity measurement by automated brain densitometry of contralateral ASPECTS regions. This study provides an automated method to transform brain atlases to a patient's NCCT scan and to automatically detect and quantify EICs. This automated method was in agreement with manual consensus scoring in 73% without bias or outliers, in contrast with individual observers. With the same limits of agreement between observers and between automated and consensus scoring, we can conclude that the automated method performed comparable to the manual ASPECTS scoring, keeping in mind that in the acute setting, ASPECTS is not scored in consensus but by a single observer. Since our method obtained similar correlations with clinical outcome measures, it shows the potential to assist or eventually replace manual ASPECTS assessments as indicators of stroke severity.
A large portion of this work has been performed as a graduation project by F. E. van Rijn, at the Institute of Technical Medicine, University of Twente, Enschede, The Netherlands.
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Biographies for the authors are not available.
© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Berend C. Stoel, Henk A. Marquering, Marius Staring, Ludo F. Beenen, Cornelis H. Slump, Yvo B. Roos, and Charles B. Majoie "Automated brain computed tomographic densitometry of early ischemic changes in acute stroke," Journal of Medical Imaging 2(1), 014004 (24 March 2015). https://doi.org/10.1117/1.JMI.2.1.014004
Densitometry
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Berend C. Stoel, Henk A. Marquering, Marius Staring, Ludo F. Beenen, Cornelis H. Slump, Yvo B. Roos, Charles B. Majoie, "Automated brain computed tomographic densitometry of early ischemic changes in acute stroke," J. Med. Imag. 2(1) 014004 (24 March 2015) https://doi.org/10.1117/1.JMI.2.1.014004

redpajama_set_name: RedPajamaCommonCrawl


student journalism – Page 2 – Write. Snap. Click.
As I mentioned in my last night, today my journalism class had a field trip. We went down to the old/new Chinatown in the city. Today I choose to tell the day's tales in a visual manner, enjoy.
Okay I might have added some spices in that headline, what I mean to say is 'Let the in-depth games begin'.
Today was our first real introduction to our in-depth projects. A month dedicated to writing and producing in-depth features on a certain topic.
This year's topic is China–Johannesburg – a look at the Chinese diaspora in this city. A topic that didn't come as a surprise to most of us because there was a leak (journalists, can't tell them anything).
We were all divided into groups, each getting a sub-topic and a mentor. My group got given the topic 'history and the future' – a topic I begin to appreciate more and more as the day went on. Our mentor is the one and only, Kenichi Serino (yays).
Basically what we have to do over the next four weeks is immerse ourselves fully in this community in order to produce a long form feature with multimedia components (ranging from video, photography, audio and and and).
Our prep consisted of a number of guest speakers to give us some background and advice on our topic. It was a long day but a very informative one. Ideas were coming in and out of my mainframe all day. Especially because a bulk of what the speakers had to say touched heavily on my group's topic.
Hearing the stories about the realities of being a migrant and of life in China made it abundantly clear that the next month would be an enlightening one. In a real and genuine way, in a way that would bring me closer and deeper than any documentary or article could.
My favourite quote from the day came from Emma Chen, owner of restaurant Red Chamber in Rosebank, she said: "Nobody dislikes the Chinese government as much as the Chinese themselves". A loaded statement, that left me with much to think about (and investigate).
This little thing called life got in the way of me posting a 'best reads'post last week, I will not let this thing called life do that to me again.
Last week was one of the last editions we'll produce in a while, so it was jam packed with copy to feed the appetites of our readers.
EFF triggers PYA exodus by Thuleto Zwane. One would imagine that it would take more than kitch red berets to sway comrades' alliances, one would be wrong. I kid, I kid. Interesting to see how quickly the new political party is gaining ground among some students.
Dr Last loses by Shandukani Mulaudzi. Things came full circle last week when a verdict was reached regarding our supposed ethical misdemeanour with one of our sexual harassment stories. Good to know that even back then our ethics were intact.
Enjoying food that has roots by Mfuneko Toyana. For no other reason than the deliciousness that came from this meeting. Glad I got to tag along and literally get a taste of Kenya.
"Go see Josh" by Sibusisiwe Nyanda. Lovely and inspirational story about weight loss. Josh had me going til he mentioned something about no carbs, that's when I knew I couldn't do what he did. But inspirational nonetheless.
Witsie bail-out by Nolwazi Mjwara. A look at the contentious issue of students on campus being bailed out of jail by the university.
Survivor: ANN7 edition by Nokuthula Manyathi. Another one I got to sit in on last week. It was interesting to hear first-hand how things are going over at ANN7. The tweets weren't lying.
The week that was is somewhat of a blur. Feels like it took a short Usain Bolt like sprint and before I knew it, here I am on a Sunday afternoon trying to string together something semi interesting to write.
Monday morning is a haze of people typing frantically, in 11th hour attempts to get features done. I wished I could have joined in on the madness but I had already accepted that it was late for me, very late.
Later that morning we had to take a crack at writing travel features with Fred de Vries. He made us go to a mall, preferably one we had never visited before and take notes that would bring that mall to life. I went to the newly opened Oliven Plaza and man did I see some interesting things, hope it came out in the feature I managed to write.
That afternoon I dashed off to a Right2Know protest outside Luthuli House. Protesters were appealing to president Jacob Zuma, to ask for the Protection of State Information Bill to be taken back to parliament for some revision. I cause I would expect any media practitioner to support, this expectation was quickly dashed upon arrival.
We stood with some journalists and photographers from The Times and SAPA, they were obviously disappointed by the dismal turn out at the protest – granted. What I didn't understand was their attitude towards the protest itself. There were scoffs, sighs and comments about what a waste of time the whole thing was. One photographer condescendingly said, "How many of these people (the protesters) have even read or really know what the Secrecy Bill is about?" he quickly went on to answer is own question "Only the journalists do," he said with a chuckle.
I was like whaaaaat?! These people are gathered here to defend your right to do your job mister (all in my head of course, and in a tweet later that night). I suppose it was just disappointing to find out that some of the people we look up to are so jaded. Anyway I managed to get some great pictures and that's all that mattered that day.
On Tuesday we had guest speaker Phillip Altbeker come in to discuss movie reviews with us. We wrote reviews on a movie the whole class went to watch a few weeks prior, Of Good Report. There were no comments on our work but he did say, "These will probably be the first and the last reviews you write." I suppose that says it all doesn't it?
Friday was the best day of the whole week. It started off with a trip to Engineers Breakfast to get some good photographs, which we managed to do. Fomo hit us hard when we had to leave, we also wanted to sit under a gazebo, with meat on the coals and not a care in the world.
But we couldn't, we had to rush off of to a guest photography lecture. I was very grateful for once it had ended, it might be one of the best lectures we have ever had. We got to see some of the meaningful work people are doing out there, as clichéd as it is pictures really are worth a thousand words.
Today was our first full day of photojournalism with lecturer TJ Lemon.
Took these two photo's as test shots when we were packing up in the morning.
A snip in the bush vs a scalpel in the bed by Shandukani Mulaudzi. The headline alone makes this a best read. It's a story that looks at traditional circumcision and it's seemingly unwavering popularity.
Albert goes to Paris by Leigh-ann Carey. I love this story, this dude is a champ!
SRC campaigns at a glance by Mfuneko Toyana and Emelia Motsai. Great way to let people know what the differing parties are all about, to help them make informed decisions as we vote for a new SRC this week.
This week's paper came out a little later than usual, hence that lead to delayed reading of all the copy.
Cheers, I'll drink to that by Liesl Frankson. This was one of the first 'fun' front page stories we have had all year. It was a refreshing read and I'm glad it got to go on the front page. Also love the picture that went with it.
The power of woman by Prelene Singh. This piece spoke to the other side of the Vavi scandal. Probably an unpopular debate to have with it being women's month but an important one I rate.
Supreme Failure by Emelia Mostai and Shandukani Mulaudzi. This breaking news story looks at a high school across the road from us; parents and students complained about the lack of teaching staff for grade 12 learners. The publishing of the story has led to the Department of Education promising to visit the school tomorrow to assess the situation. The efforts of great journalism.


Many popular bulb plants, such as Tulips, Daffodils and Hyacinths are best planted in the fall. Here are some tips to help you get the most out of your bulb investment.
Since the food the plant needs to flower in spring is contained in the bulb already if you have quality, healthy bulbs to work with, you can choose to plant new bulbs every year to (almost) guarantee a spectacular display in spring. This is what they do in the bulb area at Missouri Botanical Garden where I took my Master Gardener Training. If you are interested in your bulb plantings lasting for several years, try to match the species of bulb you're planting with the light and moisture conditions in your garden so that the plants can make enough food for next year. For example, in my garden Daffodils do not get enough sun to have produced flowers after the first year I planted them. They've survived for over a decade now and grow foliage but there are no flowers. I'm leaving them be because some of the trees in my area are sickly and if they are ever taken down my bulbs might flower in the future. If they don't, the foliage is better than empty space and it's ephemeral so they're not in the way. But that's not the most satisfying way to enjoy bulbs!
Bulbs need a lot of Potassium in the soil but low Nitrogen and mid-level Phosphorus. A fertilizer regimen that is suitable for onions will also work for bulbs. For spring blooming bulbs, apply fertilizer in late fall.
Soil with good drainage is very important for bulbs. If your soil does not drain well as is, the product Turface is recommended to improve the soil structure. You could also try planting in raised beds filled with well-draining soil.
As you decide what bulbs to plant where in your garden, also consider what plants could "take over" for the bulb plants once the foliage dies down and the bulb plants go dormant for the summer. This is called succession planting. You could wait until spring and work annuals in among the bulb plants or you could plant them with a perennial that is late to get started in spring or is not so aggressive that it will out-compete the bulbs. Bulb plants look wonderful emerging from low groundcovers. You'll want to take into account height, foliage colors and texture, bloom colors and bloom times as you plan your bed. Keep good records of what you did because it's really easy to forget the location of ephemeral plants! You could also try interplanting fall-blooming bulbs to remind you where the bulb areas are when the spring bulbs are dormant.
Some bulb plants with a strong vertical aspect such as Alliums and Tulips might look good as part of a formal planting but many other bulbs look better in a natural planting style. For an informal look, planting in "amoeba" shapes in odd numbers works well. You could also try tossing a bunch of bulbs in the air and planting them where they fall. The smaller the plants are, the more you will need to get a good show.
Animals that snack on bulbs need to be considered while planning your bulb plantings also. If certain animals are a problem in your area, look for bulbs that they don't like – there are repellents you can try but although it will limit your selection it's much easier to plant something less tasty. For example in my garden squirrels are a big problem but there are no deer, while other neighborhoods have many deer.
Squirrels that dig up bulbs might be deterred by a wire screen placed over the bulbs and just under the soil. This takes some effort and planning but is a lot less trouble than constantly applying repellents. Make sure the holes in the screen are big enough for the plant stems to get through without allowing access to the bulbs. Schnarr's sells plastic-coated wire mesh which will last a lot longer than uncoated wire.
I've heard that squirrels can smell bulbs. In case this is true, you could try placing the bulbs in the holes with tongs to avoid getting the bulb smell on your gloves or changing gloves after you handle the bulbs so you don't get the smell on the soil and other things. If you discard any bulb parts in the compost such as some of the papery sheath that falls off the bulb, make sure your pile is not near the bulbs. I don't know for sure if this helps but it's a small effort that might pay off.
When planting, the hole should be big enough to accommodate the bulb without having to jam it in. If you can find spacing and depth recommendations for the specific plant, use those guidelines. If you are not sure make the hole thrice the depth of the bulb and space them apart the the same distance (thrice the depth of the bulb) . It's better to plant a little too deep than a little too shallow because a too-shallow planting can hinder blooming in some bulbs. The orientation of the bulb is important – the root side needs to point down. Discard any bulbs that are mushy or moldy.
Water in your new bulbs well while planting and give adequate moisture through fall so they have a chance to grow some roots before deep cold sets in.
Do you have any bulb plants in your garden that are not winter hardy? Depending on the species they may need to be dug up and stored for winter before the first hard frost. Other plants such as Dahlias, Cannas and Pineapple Lilies can be overwintered by putting a circle of mesh around the plant and filling it with loose mulch 1-2 feet deep. Look up the guidelines for your particular plant if you are not sure.
To store non-hardy bulbs that need to remain dormant over winter, after digging, divide and clean them. Let them dry out then dust them lightly with sulphur powder to prevent rot. Pack them with peat moss or wood shavings in a crate or box and store them in a dark, dry, cool location. Make sure the bulb storage area is away from stored fruits and vegetables so the ethylene gas emitted by the ripening fruits and vegetables does not harm the bulbs viability. Replant in spring after the last frost.
For tender bulb plants that do not go dormant, repot in a container. Trim off any old foliage but leave the good foliage. Keep evenly moist in a well-lit warm environment.
Here are links to some specific planting and care guidelines for bulbs that we sell at Schnarr's. The bulb offerings at each of our stores differ somewhat so call ahead to check on stock if you want a specific plant.


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Blogging The Deathly Hallows: Prelude and Chapter 1
Today and for the next 36 posts, the subject of this space will be a chapter of, what is for me the most problematic book in the entire Harry Potter series: The Deathly Hallows. I am not going to write these posts pretending that I can un-know what I know, so on the remote chance that there are are any of my flisties who *haven't* read DH and want to remain in the dark, just take it for granted that any post concerning the book is going to have spoilers and act accordingly. :-) You can also take it as read that I will be free with my opinions, and I'd very much like to have you join the discussion and do the same. I will also be rec-ing, from time to time, favorite fanfic pieces that I think compliment the canon. I will try to post at least one chapter a day. There may be days when I am not going to have the time to post, but I'll do my best to keep them to a minimum. DH is loooooonnnngggggg (759 pages in my hardback edition, 36 chapters plus The Horrid Epilogue) and there *are* other things I'd like to talk about this summer! ::g::
Before I get started, I'd like to do a little bit of reminiscing. I didn't know any of you in 2007 (which is a darned shame ;-) ), so inquiring minds would like to know: what were you doing in the summer Deathly Hallows was released? Miss M had just finished 2nd grade, and I was doing the same juggling of my own schedule and hers as I am doing now, but with a lot less dance in it (only 2 hours a week - imagine that!). I was 37 years old and planning on spending my evening camped in front of a bookstore with my 2nd eldest niece, who is also a very big HP fan, to be among the first to purchase the book we'd all been waiting for. Poodles (the terrible nickname said niece has had to put up with from the time she was about 18 months old - she is now 23) and I had been having a friendly argument discussion concerning the final developments of the story, and, being on opposing sides for a great many things concerning the outcome, she and I decided to make a little wager.
Each of us made ten predictions and put them in a sealed envelope in the custody of my youngest brother; we figured that since he's a cop he could more than likely be trusted. ::g:: After we had both read the book, the predictions were revealed. Seven of my 10 were correct, so I won (2 nights of baby sitting - a prize *not* to be sneezed at). Among my better attempts at channeling Sybill was the certainty that Severus Snape was not the villain he appeared to be at the end of Half-Blood Prince. My divination of his motivations, however, fell flat. I entirely missed the Lily thing. Sadly, I also knew that he would die, because the anti-hero very rarely survives in these sorts of stories, though I thought he'd reveal his real allegiances by dying to protect Harry. I also predicted Horcrux!Harry. My niece was very against that idea because she thought that meant he would have to die, as did I. SInce he did, but then lived again, we counted that one as a draw. Also in my crystal ball were the death of a Weasley (though I predicted Percy), Regulus Black as R.A.B. and the locket mentioned off-handedly in Order of the Phoenix being the horcrux missing from the basin, and Hero!Neville (though I predicted that he'd be the one to off Bella). All in all, I did pretty well, but there were still many surprises, including some very unpleasant ones. Did any of you make predictions about the outcome? What were they, if you did, and how close did you come? OK then, on with the show!
Chapter One: The Dark Lord Ascending
Had I not been so throughly convinced that Severus was still on the Order's side, this chapter would have certainly made things look very black against him. We see him here for the first time among the Death Eaters, and, presumably due to his accomplishing the death of Albus Dumbledore, in a position of power and trust with Voldemort. In keeping with the tradition of medieval kings, Severus takes his place to the immediate right of the Dark Lord; Yaxley must go lower. The news he brings certainly seems traitorous. And he is quite calm in the face of Voldemort's 'fierce' Legilimency.
There's been a lot of fandom speculation about how this works. What we saw of it in OotP leads me to believe that it (Legilimency) is all about memories. When Snape breaks through into Harry's head (and when Harry breaks through into Severus's), that's what we 'see', particularly memories of the unpleasant things they'd have rather forgotten. So, is Occlumency the ability to direct a Legilimen's search without them being aware of it? The ability to alter memories without the tell-tale traces that alteration has taken place? To fabricate memories completely? But if that is the case, why would Voldemort need to ask who the source was? Wouldn't he have seen it in the 'memory' he believes he's taken? So it looks here as if he believes he is discerning truth from lie, and there are other instances in the various books where Snape, Voldemort or Dumbledore seem to be using Legilimency as a substitute for veritaserum. Is this inconsistent or am I reading too much into this (has been known to happen ::g::)? What do you think? In any case, it is a powerful demonstration of Severus's abilities as an Occlumens, since he satisfies the world's most skillful Legilimens in 'a moment or two'. I also wonder who the mysterious 'source' is supposed to be, since we never hear of them again.
Next we hear of Pius Thickness' (great Dickensian name!) corruption by Imperious and we get a look at the court intrigue just barely under the surface as well: Yaxley's resentment of Severus's position, Lucius's disgrace, the palpable fear among the followers even as they rejoice at the Malfoys brought low. Notice how both Draco and Lucius look to Narcissa for guidance as Voldemort twists the knife? It's the first hint we have that Narcissa is made of something stronger than she looks to be.
It's interesting that Voldemort chooses Lucius's wand to substitute for his own for the mission to intercept Harry's transfer from Little Whinging - not as an honor, but with the devastating ' I see no reason for you to have a wand anymore.' With that comment, he strips from Lucius his status as a wizard. This is not a temporary borrowing, but a permanent change. With no wand, Lucius Malfoy is reduced, in essence, to the status of squib and will have to depend on Narcissa and Draco for any magic he might need to do, and for magical defense. That's quite a bed to have to lie in. The only one seemingly more beneath his contempt is the even more debased part of the Black family tree that had the indecency to mate (in his view) with a beast. It almost seems as if we are supposed to feel sorry for the Malfoys. Does anyone else get that sense? Upon first reading, I probably did, a bit. But now, especially when I contrast their fate with Severus's, I feel as contemptuous as Voldemort. They deserve a little bit of suffering for their poor choices, methinks.
And now we come to the prisoner we saw dangling over the table when first we entered the room. It's a tradition in every book to meet a new professor, and though we assume that Charity Burbage is not new to Hogwarts, she is new to us. She is, we discover, the Muggle Studies teacher. She has been brought to Malfoy Manor to be executed for the crime of teaching the heresy that Muggles aren't that different from wizards and, even worse, advocating that wizards should interbreed with them and fully accept Muggleborns as magical. She recognizes Severus at the table and pleads repeatedly for his help. We are told: 'Snape looked back at her, quite impassive, as she turned slowly away from him again.' From this, we are meant to infer that he is in complete agreement with her fate. One cannot know what Severus thought of her as a colleague, since we never see them interact at Hogwarts, or what sort of relationship, if any, they had with one another. But the first thing I noticed about this is the lack of contempt upon his face. No sneer, no display of hatred, just impassivity. Since he can't save her without dooming himself, what is going on in his head? There is a lovely little fanfic piece of 'missing canon' which was written by mountainmoira which has a very comforting answer to this question. It's called The Measure of Mercy, and if you've not had the pleasure of reading it, go ahead and treat yourself to it now. It's short, but very intense and satisfying.
The chapter ends with Charity's death and the disgusting image of Voldemort releasing Nagini to eat her corpse, and we are left to contemplate the horrors to come under the Dark Lord's rule. I think this opening chapter sets us up pretty well for the dark tone of the rest of the book and lays the groundwork for the next several chapters. What do you think?
Current Location:In my garden
Current Mood: contemplative
Current Music:birdsong
It's interesting that you focus on the punishment bit. You're right, there is certainly that, but I'd argue that there's more to dealing with supporters of atrocities. For example, the South Africans didn't call their commission "Truth and Reconciliation" for nothing. JKR spent a lot of time working for Amnesty International and claims that things she saw and heard there were a major influence on her thinking about this. It is for that reason, (as well as the 'kiddies don't understand if the bad guys aren't punished' mindset), I think, that the shades of grey and ambiguous motives in which of humanity operates are missing. When you spend your day hearing about the evil that humans are perpetrating against their fellow humans for the paltriest if reasons, it's hard to not want to go pound the bad guys into sand. Only Snape is seen to be grey, and he gets dealt the most horrible fate in the series.
I'd say more's the reason to get the premise right. It may be "only" a children's book, but children's books contribute to framing our ideas of Good and Evil and how to deal with them. The hero story arc told here is a "Our Side=Good, Their Side=Bad, Women=Secondary, Foreigners=Foils" kind of thing, and to my mind, it isn't just the best of children's literature that should move beyond perpetuating the myth that the world works like that. Oh, I agree with you. (Except for the part that it's a myth that the world works like that. It still does to a great degree in even the Western nations. In parts of the world, it's a good deal worse.) Simplistic thinking tends to rule in commercially viable kid lit, and tends to reflect the most reassuring conventional morals rather than taking an aspirational view of things. At least now, there are some strong female characters, there's more than racial group in the supporting cast (though rarely among the principals), and the occasional non-heterosexual person (though such things are usually only referred to in the most oblique terms - we are still very uncomfortable with any expression of sexuality in children's works). It's not much, but it is progress compared to what went before.
Ah, if I ever go to another convention, I'd love to do a paper or a discussion on the history and politics of Harry Potter. There's so much in there ... Why wait for a con? ::gives Tetley a sly smile:: I'd love to read such a paper!


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Everyone knows the only thing scarier than a horde of zombies
is a clown…
Nothing is scarier than a clown
About 99 Psycho Clowns
99 Psycho clowns began in 2010 when team owner and creator Daniel McMahon decided he wanted to go racing again!
Over the past 5 years, there has been numerous boats – 99PC, 99 Psycho Clowns F2, 99 Psycho Clowns F1, 99 Psycho Clowns, 99 Psycho Clowns Team 4, 99 Psycho Clowns F1 Tunnel Boat and The Ringmaster.
As a shed can only house so much, a few have been sold, dewrapped and created into new teams.
We used to race a fleet of boats, with many great skiers, observers and drivers joining the crew, however This season we are racing just 2 – 99 Psycho Clowns and 99 Psycho Clowns F2. The main race boat is the 2013 World Title winning number 99 heavily modified force.We have had the privilege of towing some of the best skiers in the world with the likes of Wayne Mawer and Mick Kelly, and currently Steve Berry and Codie Rigg.
Our team has been lucky enough to have travelled all over Australia and the world doing what we love and picked up a few titles along the way.
Ian Baker
We heartbreakingly lost our best friend, best observer and biggest believer Ian Baker in a boat crash in November 2014. Ian was one of the founders of this team and will be in our memories forever. Ian was known all over the world as the nicest guy with a heart of gold, biggest clown, but most of all best Dad in the universe to his beautiful 4 girls.
Where to this year for 99 Psycho Clowns?
The previous 2 seasons the 99 team has been competing in the Superclass division in the River Classics. This year we have stepped it back a class to Unlimited as we introduced a new driver to the boat – Jessica Pearse. JP was only meant to drive whilst Dan was injured, however she keeps beating his times, so she keeps the drivers seat and Dan is now in the observer seat.
Racing with the best partners in the country…
Support the team - get the gear!
Stay Connected! Sign up for our newsletter now!
© Copyright - 99 Psycho Clowns Racing Team - Site by: ANT Design


Strut on the streets in comfort wearing this T-shirt by Rigo. This unique creation is made using cotton that will highlight your torso. Live the moment wearing this slim-fit T-shirt with casual trousers and flip-flops as you hit the street with best buds.


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iOS 8 will be available to download on September 17th
Dana Wollman|@danawollman|September 9, 2014 1:41 PM
New iPhones mean new software to go with it. So, with the iPhone 6 and 6 Plus going on sale soon, that means iOS 8 is on the way as well. Apple just confirmed the next-gen operating system will arrive next week, on September 17th, as a free download. In particular, you'll be able to download it for every iPhone from the 4s on, every iPad starting with the iPad 2 and the fifth-generation iPod touch.
Included in the update is Apple Pay, a new feature that allows iPhone 6 and 6 Plus users to make payments via NFC. Otherwise, not much has changed since we last saw iOS 8, back at WWDC in June. Key features still include interactive notifications, an improved mail app, Spotlight search, support for third-party keyboards, Apple's new HealthKit application, audio messages and improved integration with other Apple devices, like Macs. We'll have a review up at some point but of course, you can also just download it for yourself.
iphoneevent2014


Money may be able to buy you happiness if you 1) have enough discretionary income and 2) spend it to purchase some free time — such as by paying someone to do cleaning, cooking and other household chores.
That's the interesting, but by no means definitive, finding of a study published this week in the Proceedings of the National Academy of Sciences (PNAS).
Researchers surveyed more than 6,000 people in four developed countries, including the United States, and found that "buying time" — defined as paying others to do chores that you personally dislike — was associated with greater life satisfaction.
These findings come with all sorts of caveats, of course, but we'll get to those in a minute.
As background information in the study notes, people living in developed countries — whether it be Germany, Korea or the United States — report greater time scarcity than did people in past generations, including those with higher incomes.
The stress from that lack of time comes with a hefty health-related price tag: Research has linked it to lower well-being, including reduced happiness, increased anxiety and insomnia.
Time stress also plays a critical role in the rising rates of obesity, because when people lack time, they often fail to eat healthful foods or exercise regularly.
To determine whether money could be used to buy time and, thus, happiness, an international team of researchers examined data collected through lengthy questionnaires filled out by almost 6,300 participants in four countries — the U.S., Canada, the Netherlands and Denmark. The annual household income of the participants ranged from $30,000 to $1 million-plus.
Some of the participants were recruited online, some from public places, such as train stations and public parks. The study also included a group of 818 Dutch millionaires.
Among the questions on the surveys were ones designed to assess the participants' personal levels of life satisfaction and time-related stress. Other questions elicited information about how much money, if any, the participants paid others each month to do routine tasks in order to free up their own time.
An analysis of the answers revealed that only 28 percent of the participants across all four countries said they spent money to give themselves more free time each month. The average amount spent monthly: $148.
The analysis also showed that the people who "bought time" reported greater life satisfaction than those who didn't.
To make sure that the findings did not just reflect the participants' level of discretionary income, the researchers broadened the definition of timesaving purchases to include anything that the participants felt freed up their time. They then tested this broader definition with a sample of 1,802 working adults in the U.S. In that sample, 50 percent said they spent money to give themselves more time, with most buying help with cooking, shopping and housecleaning.
And again, even after controlling for several confounding factors, people who spent money tended to be more satisfied with their lives — no matter what their income.
"If anything, within our United States samples, we observed a stronger relationship between buying time and life satisfaction among less-affluent individuals," the researchers write.
On the evening of each purchase day, the participants were interviewed over the phone to assess their mood and level of time-related stress. The results showed that their mood was higher and their stress was lower when they spent the money on the timesaving purchase rather than on the material one.
And now for the caveats. This study was observational, which means it cannot prove a direct cause-and-effect between "buying time" and greater life satisfaction. Although the researchers controlled for a variety of factors, including income, they did not include many other important ones, such as health status, family life, employment and personality. Even in the field experiment, an unidentified event or activity or piece of news may have affected the mood and stress levels of the participants on the day they made their purchases — something that had nothing to do with what they bought.
In addition, the participants self-reported their own estimates on how much money they spend on timesaving purchases. Some may not have been entirely truthful with their answers, especially if they were embarrassed to admit how much they pay for outside help.
And, of course, it goes without saying that few people, at least in the U.S., have enough discretionary income to hire a personal shopper or a housecleaner or even a neighborhood teenager to mow their lawn. And, as the researchers acknowledge, relatively few people at the lowest runs of the income spectrum participated in their questionnaires.
Still, time stress is a real and growing problem — including a health problem — for people of all incomes. And that's particularly true for women.
"Within many cultures, women may feel obligated to complete household tasks themselves, working a 'second-shift' at home, even when they can afford to pay someone to help," the researchers write.
"Increasing uptake of time-saving services may provide a pathway toward reducing the harmful effects of women's second shift," they add.
Let's just make sure that we help the working poor reduce their time stress as well.
FMI: You can read the study in full on the website of the Proceedings of the National Academy of Sciences.
What makes us happy today appears to be markedly different from what made us happy 78 years ago, a British study finds.
Then again, people who dance might just be happier in the first place.


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Q: Possible encoding issue with Google PubSub When running a subscription source from the Alpakka PubSub library I received possible encoded data.
@Singleton
class Consumer @Inject()(config: Configuration, credentialsService: google.creds.Service)(implicit actorSystem: ActorSystem) {

  implicit val m: ActorMaterializer = ActorMaterializer.create(actorSystem)
  val logger = Logger(this.getClass)
  val subName: String = config.get[String]("google.pubsub.subname")
  val credentials: Credentials = credentialsService.getCredentials
  val pubSubConfig = PubSubConfig(credentials.projectId, credentials.clientEmail, credentials.privateKey)

  val subSource: Source[ReceivedMessage, NotUsed] = GooglePubSub.subscribe(subName, pubSubConfig)
  val ackSink: Sink[AcknowledgeRequest, Future[Done]] = GooglePubSub.acknowledge(subName, pubSubConfig)

  val computeGraph = Flow[ReceivedMessage].map {
    x =>
      logger.info(x.message.data)
      x
  }

  val ackGraph = Flow.fromFunction((msgs: Seq[ReceivedMessage]) => AcknowledgeRequest(msgs.map(_.ackId).toList))

  subSource
    .via(computeGraph)
    .groupedWithin(10, 5.minutes)
    .via(ackGraph)
    .to(ackSink)
    .run()
}

I publish the message from the PubSub console. I am expecting my test message to appear however when publishing test I receive dGVzdA==. Is this an expected result? I have had issues with importing the private key and this might be a result of it?
The consumer is bound eagerly with Guice.

A: Data that is received over REST apis will be base64 encoded.  My guess would be that the Alpakka Pub/Sub library which uses the REST APIs is not properly decoding the received data.  It looks like they also have a library that uses the GRPC Pub/Sub client as the underlying layer which may not suffer from this defect?  You can also use the Cloud Pub/Sub Java client library from Scala directly.

redpajama_set_name: RedPajamaStackExchange