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Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be performed using a select number of modalities that are not…
Background & Aims: Hepatic steatosis is a major cause of chronic liver disease. 2D ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective. We developed a scalable…
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis. However, there is a lack of clear understanding of why and how the Convolutional Neural Network performs so well for…
Micro-vascular flow in the myocardium is of significant importance clinically but remains poorly understood. Up to 25% of patients with symptoms of coronary heart diseases have no obstructive coronary arteries and have suspected…
Analysing the haemodynamics of flow in carotid artery disease serves as a means to better understand the development and progression of associated complex diseases. Carotid artery disease can predispose people to major adverse…
To better understand the hydrodynamic flow behavior in turbulence, Particle-Fluid flow have been studied using our Direct Numerical(DNS) based software DSM on MUSCL-QUICK and finite volume algorithm. The particle flow was studied using…
Advances in the development of largely automated microscopy methods such as MERFISH for imaging cellular structures in mouse brains are providing spatial detection of micron resolution gene expression. While there has been tremendous…
Tissue deformation in ultrasound (US) imaging leads to geometrical errors when measuring tissues due to the pressure exerted by probes. Such deformation has an even larger effect on 3D US volumes as the correct compounding is limited by the…
Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder affecting millions of individuals today. The prognosis of the disease solely depends on treating symptoms as they arise and proper caregiving, as there are no current…
This work examines the hypothesis that intravoxel incoherent motion MRI (IVIM) can quantify local cerebral blood flow (qCBF), infarct volume, and define the ischemic penumbra for determination of the perfusion-diffusion mismatch (PWI/DWI)…
Purpose: Spinal instability is a widespread condition that causes pain, fatigue, and restricted mobility, profoundly affecting patients' quality of life. In clinical practice, the gold standard for diagnosis is dynamic X-ray imaging.…
Uncertainty Quantification (UQ) is crucial for ensuring the reliability of machine learning models deployed in real-world autonomous systems. However, existing approaches typically quantify task-level output prediction uncertainty without…
Medical therapy for patients with severe heart disease often relies on left ventricular assist devices (LVADs). It is an ongoing topic of research how complications like stagnation and thrombosis can be prevented by using artificial…
Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo…
Non-invasive observation of spatiotemporal neural activity of large neural populations distributed over entire brains is a longstanding goal of neuroscience. We developed a real-time volumetric and multispectral optoacoustic tomography…
Flow fluctuations have recently emerged as a promising hemodynamic metric for understanding the rupture risk of intracranial aneurysms. Several investigations have reported in the literature corresponding flow instabilities using…
Cerebral blood flow is a critical metric for cerebrovascular monitoring, with applications in stroke detection, brain injury evaluation, aging, and neurological disorders. Non-invasively measuring cerebral blood dynamics is challenging due…
Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in inpainting these lesions, most existing methods operate…
This project intends to study a cardiovascular disease risk early warning model based on one-dimensional convolutional neural networks. First, the missing values of 13 physiological and symptom indicators such as patient age, blood glucose,…
3D-Polarized Light Imaging (3D-PLI) reconstructs nerve fibers in histological brain sections by measuring their birefringence. This study investigates another effect caused by the optical anisotropy of brain tissue - diattenuation. Based on…