42 citations · 67 across the 11 of their papers we have counts for
12 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…
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…
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 Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging
Tristan S. W. Stevens, Jeroen Overdevest, Oisín Nolan +3
Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that u…
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…
Active Diffusion Subsampling
Oisin Nolan, Tristan S. W. Stevens, Wessel L. van Nierop +1
Subsampling is commonly used to mitigate costs associated with data acquisition, such as time or energy requirements, motivating the development of algorithms for estimating the fu…
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…
Removing Structured Noise with Diffusion Models
Tristan S. W. Stevens, Hans van Gorp, Faik C. Meral +4
Solving ill-posed inverse problems requires careful formulation of prior beliefs over the signals of interest and an accurate description of their manifestation into noisy measurem…
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…
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…