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Differentiating treatment-induced necrosis from tumor recurrence is a central challenge in neuro-oncology. These 2 very different outcomes after brain tumor treatment often appear similarly on routine follow-up imaging studies. They may even manifest with similar clinical symptoms, further confounding an already difficult process for physicians attempting(More)
As currently used, the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) has low sensitivity for measuring Alzheimer’s disease progression in clinical trials. A major reason behind the low sensitivity is its sub-optimal scoring methodology, which can be improved to obtain better sensitivity. Using item response theory, we developed a new(More)
Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) suffers from low sensitivity in detecting changes in Alzheimer's disease progression in clinical trials of disease-modifying treatments. A comprehensive psychometric analysis of the items in ADAS-cog assessment can help in identifying and improving the insensitive items. Item response theory(More)
A large number of sophisticated techniques have been proposed over the last few decades for automatic analysis of brain MR images to help clinicians better diagnose and understand anatomical changes due to neurological disorders. While significant improvements in performance have been achieved, almost all techniques suffer from a common limitation of high(More)
Accurate segmentation of magnetic resonance (MR) images of the brain to differentiate features such as soft tissue, tumor, edema and necrosis is critical for both diagnosis and treatment purposes. Region-based formulations of geometric active contour models are popular choices for segmentation of MR and other medical images. Most of the traditional(More)
Retrospective correction of intensity inhomogeneities in magnetic resonance images of the brain is an essential pre-processing step before any sophisticated image analysis task can be performed. A popular choice when defining the degradation model in MR images is to use multiplicative intensity inhomogeneities that slowly varying across the image domain;(More)
Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue(More)
Although many genetic markers are identified as being associated with Alzheimer's disease (AD), not much is known about their association with the structural changes that happen as the disease progresses. In this study, we investigate the genetic etiology of neurodegeneration in AD by associating genetic markers with atrophy profiles obtained using patient(More)