output
20102026
most citedThe role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies

818 citations

Showing physics.med-phShow all

10 papers · 1 filter

physics.med-ph2025

q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Models

Shishuai Wang, Florian Wiesinger, Noemi Sgambelluri +4

The 3D fast silent multi-parametric mapping sequence with zero echo time (MuPa-ZTE) is a novel quantitative MRI (qMRI) acquisition that enables nearly silent scanning by using a 3D…

physics.med-ph2025

Evaluation of the Automatic Classifier AutoSense Sky OS for Pediatric Cochlear Implant Users using a Virtual Classroom Environment

Maartje M. E. Hendrikse, André Goedegebure, Kars R. Tjepkema +1

This study evaluated whether AutoSense Sky OS, an automatic classifier used in pediatric Advanced Bionics cochlear implants (CIs), improves speech intelligibility from the front wi…

physics.med-ph2025★ 3 cited

Enhancing kidney quality assessment: Power Doppler during normothermic machine perfusion

Yitian Fang, Anton V. Nikolaev, Jeroen Essers +5

Objectives: Marginal donor kidneys are increasingly used for transplantation to overcome organ shortage. This study aims to investigate the additional value of Power Doppler (PD) i…

physics.med-ph2024★ 2 cited

Polydisperse versus monodisperse microbubbles: A simulation study for contrast-enhanced ultrasound imaging

Agisilaos Matalliotakis, Martin D. Verweij

Contrast-enhanced ultrasound (CEUS) presents distinct advantages in diagnostic echography. Utilizing microbubbles (MBs) as conventional contrast agents enhances vascular visualizat…

physics.med-ph2024★ 9 cited

Impact of wavefront shape on nonlinear ultrasound imaging of monodisperse microbubbles

Agisilaos Matalliotakis, Rick Waasdorp, Martin D. Verweij +1

The field of contrast-enhanced ultrasound (CEUS) combines nonlinear, resonant microbubbles (MBs) with dedicated pulse sequences to reveal the vascular function of organs. Clinical…

physics.med-ph2022★ 13 cited

A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy

Oscar Pastor-Serrano, Steven Habraken, Mischa Hoogeman +5

In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. Motion models can be used to simulate motion pattern…