58 citations · 174 across the 19 of their papers we have counts for
22 papers
Ultrasound Operator Guidance Using World Modeling and Retrieval Based Action Planning
Noortje I. P. Schueler, Hans van Gorp, Ruud J. G. van Sloun
Ultrasound is widely used, but acquisition quality is heavily dependent on the operator's knowledge and expertise. With demand for examinations outpacing the supply of trained sono…
Deep Bayesian REFoCUS
Simon Penninga, Ruud van Sloun
In this work we formulate ultrasound multistatic recovery from arbitrary transmit sequences as a Bayesian inference problem. To that end, we train a deep generative prior on multis…
Patient-Adaptive Echocardiography using Cognitive Ultrasound
Wessel L. van Nierop, Oisín Nolan, Tristan S. W. Stevens +1
Focused transmits are the most commonly used transmit strategy for echocardiograms, but suffer from relatively low frame rates, and in 3D, even lower volume rates. Fast imaging bas…
High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models
Tristan S. W. Stevens, Oisín Nolan, Oudom Somphone +2
Three-dimensional ultrasound enables real-time volumetric visualization of anatomical structures. Unlike traditional 2D ultrasound, 3D imaging reduces reliance on precise probe ori…
Deep Variational Sequential Monte Carlo for High-Dimensional Observations
Wessel L. van Nierop, Nir Shlezinger, Ruud J. G. van Sloun
Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-…
A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
Hans van Gorp, Merel M. van Gilst, Pedro Fonseca +4
Gold-standard sleep scoring is based on epoch-based assignment of sleep stages based on a combination of EEG, EOG and EMG signals. However, a polysomnographic recording consists of…
Off-Grid Ultrasound Imaging by Stochastic Optimization
Vincent van de Schaft, Oisín Nolan, Ruud J. G. van Sloun
Ultrasound images formed by delay-and-sum beamforming are plagued by artifacts that only clear up after compounding many transmissions. Some prior works pose imaging as an inverse…
Anomalous Change Point Detection Using Probabilistic Predictive Coding
Roelof G. Hup, Julian P. Merkofer, Alex A. Bhogal +3
Change point detection (CPD) and anomaly detection (AD) are essential techniques in various fields to identify abrupt changes or abnormal data instances. However, existing methods…
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging
M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun +2
Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausibl…
Dehazing Ultrasound using Diffusion Models
Tristan S. W. Stevens, Faik C. Meral, Jason Yu +3
Echocardiography has been a prominent tool for the diagnosis of cardiac disease. However, these diagnoses can be heavily impeded by poor image quality. Acoustic clutter emerges due…
A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge
Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab +5
This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covaria…
A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow
Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab +6
This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS c…
Deep Task-Based Analog-to-Digital Conversion
Nir Shlezinger, Ariel Amar, Ben Luijten +2
Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. Their conversion consists of two stages: Sampling, which maps a continuous-time s…
Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning
Tristan S. W. Stevens, Nishith Chennakeshava, Frederik J. de Bruijn +2
Intravascular ultrasound (IVUS) offers a unique perspective in the treatment of vascular diseases by creating a sequence of ultrasound-slices acquired from within the vessel. Howev…
Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors
Vincent van de Schaft, Ruud J. G. van Sloun
Ultrasonography offers an inexpensive, widely-accessible and compact medical imaging solution. However, compared to other imaging modalities such as CT and MRI, ultrasound images n…
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
Iris A. M. Huijben, Wouter Kool, Max B. Paulus +1
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning communit…
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection
Tristan S. W. Stevens, R. Firat Tigrek, Eric S. Tammam +1
Cognitive radars are systems that rely on learning through interactions of the radar with the surrounding environment. To realize this, radar transmit parameters can be adapted suc…
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities
Lizeth Gonzalez-Carabarin, Iris A. M. Huijben, Bastiaan S. Veeling +2
Unstructured neural network pruning algorithms have achieved impressive compression rates. However, the resulting - typically irregular - sparse matrices hamper efficient hardware…
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse +2
Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) co…
Deep learning in ultrasound imaging
Ruud JG van Sloun, Regev Cohen, Yonina C Eldar
We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. Our goal is to provide the reader with a broad understanding of the possibl…
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
Oren Solomon, Regev Cohen, Yi Zhang +5
Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It…
Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound
Oren Solomon, Ruud J. G. van Sloun, Hessel Wijkstra +2
Ultrasound localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Sequential localization of echoes returned from inert mic…