61 citations · 157 across the 19 of their papers we have counts for
21 papers
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…
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…
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…
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…
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…
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…
Self-supervised learning of inverse problem solvers in medical imaging
Ortal Senouf, Sanketh Vedula, Tomer Weiss +3
In the past few years, deep learning-based methods have demonstrated enormous success for solving inverse problems in medical imaging. In this work, we address the following questi…
Joint learning of cartesian undersampling and reconstruction for accelerated MRI
Tomer Weiss, Sanketh Vedula, Ortal Senouf +3
Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality for soft tissues. The long acquisition times, however, make it more prone to motion artifacts as w…
Learning beamforming in ultrasound imaging
Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov +3
Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. In this paper, we demo…
High frame-rate cardiac ultrasound imaging with deep learning
Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov +5
Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received…
High quality ultrasonic multi-line transmission through deep learning
Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov +5
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography. Several methods have been proposed in the medical ultrasound literature aiming at acceleratin…
Towards CT-quality Ultrasound Imaging using Deep Learning
Sanketh Vedula, Ortal Senouf, Alex M. Bronstein +2
The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based i…
Spatially regularized reconstruction of fibre orientation distributions in the presence of isotropic diffusion
Q. Zhou, O. Michailovich, Y. Rathi
The connectivity and structural integrity of the white matter of the brain is nowadays known to be implicated into a wide range of brain-related disorders. However, it was not befo…
A New Similarity Measure for Non-Local Means Filtering of MRI Images
Sudipto Dolui, Alan Kuurstra, Iván C. Salgado Patarroyo +1
The acquisition of MRI images offers a trade-off in terms of acquisition time, spatial/temporal resolution and signal-to-noise ratio (SNR). Thus, for instance, increasing the time…
Image Deblurring Using Derivative Compressed Sensing for Optical Imaging Application
Mohammad Rostami, Oleg Michailovich, Zhou Wang
Reconstruction of multidimensional signals from the samples of their partial derivatives is known to be a standard problem in inverse theory. Such and similar problems routinely ar…
Spatially regularized compressed sensing of diffusion MRI data
Oleg Michailovich, Yogesh Rathi, Sudipto Dolui
The present paper introduces a method for substantial reduction of the number of diffusion encoding gradients required for reliable reconstruction of HARDI signals. The method expl…
Image Segmentation Using Weak Shape Priors
Robert Sheng Xu, Oleg Michailovich, Magdy Salama
The problem of image segmentation is known to become particularly challenging in the case of partial occlusion of the object(s) of interest, background clutter, and the presence of…
Regularized Richardson-Lucy Algorithm for Sparse Reconstruction of Poissonian Images
Elad Shaked, Oleg Michailovich
Restoration of digital images from their degraded measurements has always been a problem of great theoretical and practical importance in numerous applications of imaging sciences.…
An Iterative Shrinkage Approach to Total-Variation Image Restoration
Oleg Michailovich
The problem of restoration of digital images from their degraded measurements plays a central role in a multitude of practically important applications. A particularly challenging…
Information tracking approach to segmentation of ultrasound imagery of prostate
Robert Sheng Xu, Oleg Michailovich, Magdy Salama
The size and geometry of the prostate are known to be pivotal quantities used by clinicians to assess the condition of the gland during prostate cancer screening. As an alternative…
Iterative Shrinkage Approach to Restoration of Optical Imagery
E. Shaked, O. Michailovich
The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging science…