White matter integrity alterations in post-traumatic stress disorder.

Post-traumatic stress disorder (PTSD) is a debilitating condition which can develop after exposure to traumatic stressors. Seventy-five adults were recruited from the community, 25 diagnosed with PTSD along with 25 healthy and 25 trauma-exposed age- and gender-matched controls. Participants underwent clinical assessment and magnetic resonance imaging. A previous voxel based morphometry (VBM) study using the same subject cohort identified decreased grey matter (GM) volumes within frontal/subcortical brain regions including the hippocampus, amygdala, and anterior cingulate cortex (ACC). This study examines the microstructural integrity of white matter (WM) tracts connecting the aforementioned regions/structures. Using diffusion tensor imaging, we investigated the integrity of frontal/subcortical WM tracts between all three subject groups. Trauma exposed subjects with and without PTSD diagnosis were identified to have significant disruption in WM integrity as indexed by decreased fractional anisotropy (FA) in the uncinate fasciculus (UF), cingulum cingulate gyrus (CCG), and corpus callosum (CC), when compared with healthy non-trauma-exposed controls. Significant negative correlations were found between total Clinician Administered PTSD scale (CAPS) lifetime clinical subscores and FA values of PTSD subjects in the right UF, CCG, CC body, and right superior longitudinal fasciculus (SLF). An analysis between UF and SLF FA values and VBM determined rostral ACC GM values found a negative correlation in PTSD subjects. Findings suggest that compromised WM integrity in important tracts connecting limbic structures such as the amygdala to frontal regions including the ACC (i.e., the UF and CCG) may contribute to impairments in threat/fear processing associated with PTSD.

Frontal and subcortical grey matter reductions in PTSD.

Post-traumatic stress disorder (PTSD) is characterised by a range of debilitating psychological, physical and cognitive symptoms. PTSD has been associated with grey matter atrophy in limbic and frontal cortical brain regions. However, previous studies have reported heterogeneous findings, with grey matter changes observed beyond limbic/frontal areas. Seventy-five adults were recruited from the community, 25 diagnosed with PTSD along with 25 healthy and 25 trauma exposed age and gender matched controls. Participants underwent clinical assessment and magnetic resonance imaging. The data-analyses method Voxel Based Morphometry (VBM) was used to estimate cortical grey matter volumes. When compared to both healthy and trauma exposed controls, PTSD subjects demonstrated decreased grey matter volumes within subcortical brain regions-including the hippocampus and amygdala-along with reductions in the anterior cingulate cortex, frontal medial cortex, middle frontal gyrus, superior frontal gyrus, paracingulate gyrus, and precuneus cortex. Significant negative correlations were found between total CAPS lifetime clinical scores/sub-scores and GM volume of both the PTSD and TC groups. GM volumes of the left rACC and right amygdala showed a significant negative correlation within PTSD diagnosed subjects.

iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists' Decisions While Reading Mammograms.

This study introduces an individualized tool for identifying mammogram interpretation errors, called eye-Computer Assisted Perception (iCAP). iCAP consists of two modules, one which processes areas marked by radiologists as suspicious for cancer and classifies these as False Positive (FP) or True Positive (TP) decisions, while the second module classifies fixated but not marked locations as False Negative (FN) or True-Negative (TN) decisions. iCAP relies on both radiologists' gaze-related parameters, extracted from eye tracking data, and image-based features. In order to evaluate iCAP, eye tracking data from eight breast radiologists reading 120 two-view digital mammograms were collected. Fifty-nine cases had biopsy proven cancer. For each radiologist, a user-specific support vector machine model was built to classify the radiologist' s reported areas as TPs or FPs and fixated locations as TNs or FNs. The performances of the classifiers were evaluated by utilizing leave-one-out cross validation. iCAP was tested retrospectively in a simulated scenario in which it was assumed that the radiologists would accept all iCAP decisions. Using iCAP led to an average increase of 12%±6% in the number of correctly localized cancer and an average decrease of 44.5%±22.7% in the number of FPs per image.

4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging.

Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [(11)C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity.

Markerless motion tracking of awake animals in positron emission tomography.

Noninvasive functional imaging of awake, unrestrained small animals using motion-compensation removes the need for anesthetics and enables an animal's behavioral response to stimuli or administered drugs to be studied concurrently with imaging. While the feasibility of motion-compensated radiotracer imaging of awake rodents using marker-based optical motion tracking has been shown, markerless motion tracking would avoid the risk of marker detachment, streamline the experimental workflow, and potentially provide more accurate pose estimates over a greater range of motion. We have developed a stereoscopic tracking system which relies on native features on the head to estimate motion. Features are detected and matched across multiple camera views to accumulate a database of head landmarks and pose is estimated based on 3D-2D registration of the landmarks to features in each image. Pose estimates of a taxidermal rat head phantom undergoing realistic rat head motion via robot control had a root mean square error of 0.15 and 1.8 mm using markerless and marker-based motion tracking, respectively. Markerless motion tracking also led to an appreciable reduction in motion artifacts in motion-compensated positron emission tomography imaging of a live, unanesthetized rat. The results suggest that further improvements in live subjects are likely if nonrigid features are discriminated robustly and excluded from the pose estimation process.

Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer.

OBJECTIVES: To investigate the impact of respiratory motion on localization, and quantification of lung lesions for the Gross Tumor Volume utilizing a fully automated Auto3Dreg program and dynamic NURBS-based cardiac-torso digitized phantom (NCAT). METHODS: Respiratory motion may result in more than 30% underestimation of the SUV values of lung, liver and kidney tumor lesions. The motion correction technique adopted in this study was an image-based motion correction approach using, a voxel-intensity-based and a multi-resolution multi-optimization (MRMO) algorithm. The NCAT phantom was used to generate CT attenuation maps and activity distribution volumes for the lung regions. All the generated frames were co-registered to a reference frame using a time efficient scheme. Quantitative assessment including Region of Interest (ROI), image fidelity and image correlation techniques, as well as semi-quantitative line profile analysis and qualitatively overlaying non-motion and motion corrected image frames were performed. RESULTS: The largest motion was observed in the Z-direction. The greatest translation was for the frame 3, end inspiration, and the smallest for the frame 5 which was closet frame to the reference frame at 67% expiration. Visual assessment of the lesion sizes, 20-60mm at 3 different locations, apex, mid and base of lung showed noticeable improvement for all the foci and their locations. The maximum improvements for the image fidelity were from 0.395 to 0.930 within the lesion volume of interest. The greatest improvement in activity concentration underestimation was 7.7% below the true activity for the 20 mm lesion in comparison to 34.4% below, prior to correction. The discrepancies in activity underestimation were reduced with increasing the lesion sizes. Overlaying activity distribution on the attenuation map showed improved localization of the PET metabolic information to the anatomical CT images. CONCLUSION: The respiratory motion correction for the lung lesions has led to an improvement in the lesion size, localization and activity quantification with a potential application in reducing the size of the PET GTV for radiotherapy treatment planning applications and hence improving the accuracy of the regime in treatment of lung cancer.