595 citations · 734 across the 14 of their papers we have counts for
15 papers
Task-Based Adaptive Transmit Beamforming for Efficient Ultrasound Quantification
Oisín Nolan, Wessel L. van Nierop, Louis D. van Harten +2
Wireless and wearable ultrasound devices promise to enable continuous ultrasound monitoring, but power consumption and data throughput remain critical challenges. Reducing the numb…
Strategies to Minimize Out-of-Distribution Effects in Data-Driven MRS Quantification
Julian P. Merkofer, Antonia Kaiser, Anouk Schrantee +2
This study systematically compared data-driven and model-based strategies for metabolite quantification in magnetic resonance spectroscopy (MRS), focusing on resilience to out-of-d…
Nuclear Diffusion Models for Low-Rank Background Suppression in Videos
Tristan S. W. Stevens, Oisín Nolan, Jean-Luc Robert +1
Video sequences often contain structured noise and background artifacts that obscure dynamic content, posing challenges for accurate analysis and restoration. Robust principal comp…
Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Tristan S. W. Stevens, Oisín Nolan, Ruud J. G. van Sloun
Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be signifi…
Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy
Julian P. Merkofer, Dennis M. J. van de Sande, Alex A. Bhogal +1
Magnetic resonance spectroscopy (MRS) is a non-invasive technique to measure the metabolic composition of tissues, offering valuable insights into neurological disorders, tumor det…
Sequential Posterior Sampling with Diffusion Models
Tristan S. W. Stevens, Oisín Nolan, Jean-Luc Robert +1
Diffusion models have quickly risen in popularity for their ability to model complex distributions and perform effective posterior sampling. Unfortunately, the iterative nature of…
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
Dor H. Shmuel, Julian P. Merkofer, Guy Revach +2
Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the m…
Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
Itay Buchnik, Damiano Steger, Guy Revach +3
The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applic…
Unsupervised Learned Kalman Filtering
Guy Revach, Nir Shlezinger, Timur Locher +3
In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF…
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Itzik Klein, Guy Revach, Nir Shlezinger +3
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system. Classic state estimators, such as the Kalman filter (KF), provide a…
RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)
Guy Revach, Xiaoyong Ni, Nir Shlezinger +2
The smoothing task is core to many signal processing applications. A widely popular smoother is the Rauch-Tung-Striebel (RTS) algorithm, which achieves minimal mean-squared error r…
Deep Learning for Ultrasound Beamforming
Ruud JG van Sloun, Jong Chul Ye, Yonina C Eldar
Diagnostic imaging plays a critical role in healthcare, serving as a fundamental asset for timely diagnosis, disease staging and management as well as for treatment choice, plannin…
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
Julian P. Merkofer, Guy Revach, Nir Shlezinger +2
Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multi-signal DoA estimation method is the multiple signal classifi…
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Guy Revach, Nir Shlezinger, Xiaoyong Ni +3
State estimation of dynamical systems in real-time is a fundamental task in signal processing. For systems that are well-represented by a fully known linear Gaussian state space (S…
Super-resolution Ultrasound Localization Microscopy through Deep Learning
Ruud J. G. van Sloun, Oren Solomon, Matthew Bruce +4
Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerou…