Publications by authors named "A A Darekar"
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify the blood-brain barrier (BBB) permeability-surface area product. Serial measurements can indicate changes in BBB health, of interest to the study of normal physiology, neurological disease, and the effect of therapeutics. We performed a scan-rescan study to inform both sample size calculation for future studies and an appropriate reference change value for patient care.
View Article and Find Full Text PDFThis paper presents statistical shape models of the four fingers of the hand, with an emphasis on anatomic analysis of the proximal and distal interphalangeal joints. A multi-body statistical shape modelling pipeline was implemented on an exemplar training dataset of computed tomography (CT) scans of 10 right hands (5F:5M, 27-37 years, free from disease or injury) imaged at 0.3 mm resolution, segmented, meshed and aligned.
View Article and Find Full Text PDFIn preclinical models of multiple sclerosis, systemic inflammation has an impact on the compartmentalized inflammatory process within the central nervous system and results in axonal loss. It remains to be shown whether this is the case in humans, specifically whether systemic inflammation contributes to spinal cord or brain atrophy in multiple sclerosis. Hence, an observational longitudinal study was conducted to delineate the relationship between systemic inflammation and atrophy using magnetic resonance imaging: the SIMS (Systemic Inflammation in Multiple Sclerosis) study.
View Article and Find Full Text PDFIntracranial aneurysms are common, but only a minority rupture and cause subarachnoid haemorrhage, presenting a dilemma regarding which to treat. Vessel wall imaging (VWI) is a contrast-enhanced magnetic resonance imaging (MRI) technique used to identify unstable aneurysms. The pathological basis of MR enhancement of aneurysms is the subject of debate.
View Article and Find Full Text PDFArticle Synopsis
- - The study focuses on using susceptibility weighted imaging (SWI) combined with a convolutional neural network to assess brain injuries in infants with hypoxic-ischemic encephalopathy (HIE), aiming to improve prognosis.
- - To address the challenge of limited data, researchers employed transfer learning by fine-tuning a pre-trained ResNet 50 model, ensuring a balanced dataset for accurate classification.
- - Results showed a high classification accuracy of 0.93 ± 0.09, and the use of Grad-CAM heatmaps helped identify key brain regions that influence neurodevelopmental outcomes in HIE-affected infants by age 2.