6 papers
Patient-Adaptive Echocardiography using Cognitive Ultrasound
Wessel L. van Nierop, OisÃn 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, OisÃn 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…
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…
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging
Tristan S. W. Stevens, Jeroen Overdevest, OisÃn 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…
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…
Active inference and deep generative modeling for cognitive ultrasound
Ruud JG van Sloun
Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultra-portable and cost-effective, akin to the stethoscope. N…