32 citations · 43 across the 10 of their papers we have counts for
13 papers
ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images
Anand Choudhary, Xiaowu Sun, Thabo Mahendiran +8
Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains subjective and prone to intra-…
Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins
Xiaowu Sun, Thabo Mahendiran, Ortal Senouf +7
Cardiovascular disease is the leading global cause of mortality, with coronary artery disease (CAD) as its most prevalent form, necessitating early risk prediction. While 3D corona…
Inductive Domain Transfer In Misspecified Simulation-Based Inference
Ortal Senouf, Antoine Wehenkel, Cédric Vincent-Cuaz +2
Simulation-based inference (SBI) is a statistical inference approach for estimating latent parameters of a physical system when the likelihood is intractable but simulations are av…
CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography
Camille Challier, Xiaowu Sun, Thabo Mahendiran +8
Accurate segmentation of coronary arteries remains a significant challenge in clinical practice, hindering the ability to effectively diagnose and manage coronary artery disease. T…
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…
3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI
Jonathan Alush-Aben, Linor Ackerman-Schraier, Tomer Weiss +3
Magnetic Resonance Imaging (MRI) has long been considered to be among the gold standards of today's diagnostic imaging. The most significant drawback of MRI is long acquisition tim…
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…