activity
20242026
most citedEvaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations

1 citations · 1 across the 12 of their papers we have counts for

collaborators
Showing eess.IVShow all

14 papers · 1 filter

eess.IV2026

Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives

Wenqi Huang, Zhitao Li, Nan Wang +8

Echo Planar Imaging (EPI) is the standard acquisition technique for diffusion and functional neuroimaging, enabling rapid imaging but suffering from geometric distortions caused by…

eess.IV2026

Towards a Unified Theoretical Framework for Splitting-based Self-Supervised MRI Reconstruction

Siying Xu, Kerstin Hammernik, Daniel Rueckert +2

The demand for high-resolution, non-invasive imaging continues to drive innovation in magnetic resonance imaging (MRI), but long acquisition times remain a major practical limitati…

eess.IV2026

Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction

Wenqi Huang, Veronika Spieker, Nil Stolt-Ansó +6

Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific recons…

eess.IV2025

Reconstruction-free segmentation from undersampled k-space using transformers

Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan +3

Motivation: High acceleration factors place a limit on MRI image reconstruction. This limit is extended to segmentation models when treating these as subsequent independent process…

eess.IV2025

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction

Niklas Bubeck, Suprosanna Shit, Chen Chen +8

Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D sho…

eess.IV2025

Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRI

Niklas Bubeck, Yundi Zhang, Suprosanna Shit +2

In medical imaging, generative models are increasingly relied upon for two distinct but equally critical tasks: reconstruction, where the goal is to restore medical imaging (usuall…