5 citations · 6 across the 12 of their papers we have counts for
9 papers · 1 filter
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Veronika Spieker, Wenqi Huang, Cemre Ariyurek +5
Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions usin…
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…
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…
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…
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…
Self-supervised feature learning for cardiac Cine MR image reconstruction
Siying Xu, Marcel Früh, Kerstin Hammernik +6
We propose a self-supervised feature learning assisted reconstruction (SSFL-Recon) framework for MRI reconstruction to address the limitation of existing supervised learning method…