Nishant Kumar Verma

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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)
Cerebral palsy (CP) continues to be a major problem in India. The present study provides an insight into the various clinical and neuroradiological correlates of CP. The study included 102 children with CP and was subjected to magnetic resonance imaging (MRI) of the brain. Forty-seven (46%) patients belonged to the 1-3 years age group and 84 (82%) were born(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)
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)
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)
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)
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)
OBJECTIVE Ictal SPECT is promising for accurate non-invasive localization of the epileptogenic brain tissue in focal epilepsies. However, high quality ictal scans require meticulous attention to the seizure onset. In a relatively large cohort of pediatric patients, this study investigated the impact of the timing of radiotracer injection, MRI findings and(More)