activity
20092020
most citedTowards CT-quality Ultrasound Imaging using Deep Learning

32 citations · 32 across the 6 of their papers we have counts for

collaborators

13 papers

eess.IV2020

Towards a quantitative assessment of neurodegeneration in Alzheimer's disease

Oleg Michailovich, Rinat Mukhometzianov

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that progressively destroys memory and other cognitive domains of the brain. While effective therapeutic mana…

eess.IV2020

Towards learned optimal q-space sampling in diffusion MRI

Tomer Weiss, Sanketh Vedula, Ortal Senouf +2

Fiber tractography is an important tool of computational neuroscience that enables reconstructing the spatial connectivity and organization of white matter of the brain. Fiber trac…

cs.CV2020

On segmentation of pectoralis muscle in digital mammograms by means of deep learning

Hossein Soleimani, Oleg V. Michailovich

Computer-aided diagnosis (CAD) has long become an integral part of radiological management of breast disease, facilitating a number of important clinical applications, including qu…

math.OC2020

Optimization of Structural Similarity in Mathematical Imaging

D. Otero, D. La Torre, O. Michailovich +1

It is now generally accepted that Euclidean-based metrics may not always adequately represent the subjective judgement of a human observer. As a result, many image processing metho…

eess.IV2019

High-fidelity, accelerated whole-brain submillimeter in-vivo diffusion MRI using gSlider-Spherical Ridgelets (gSlider-SR)

Gabriel Ramos-Llordén, Lipeng Ning, Congyu Liao +4

Purpose: To develop an accelerated, robust, and accurate diffusion MRI acquisition and reconstruction technique for submillimeter whole human brain in-vivo scan on a clinical scann…

eess.IV2019

PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI

Tomer Weiss, Ortal Senouf, Sanketh Vedula +3

Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging. The long acquisition times, however, render MRI prone to m…