17 citations · 17 across the 8 of their papers we have counts for
5 papers · 1 filter
Unsupervised patch-based dynamic MRI reconstruction using learnable tensor function with implicit neural representation
Yuanyuan Liu, Yuanbiao Yang, Jing Cheng +8
Dynamic MRI suffers from limited spatiotemporal resolution due to long acquisition times. Undersampling k-space accelerates imaging but makes accurate reconstruction challenging. S…
Guided MRI Reconstruction via Schrödinger Bridge
Yue Wang, Yuanbiao Yang, Zhuo-xu Cui +5
Magnetic Resonance Imaging (MRI) is an inherently multi-contrast modality, where cross-contrast priors can be exploited to improve image reconstruction from undersampled data. Rece…
Quantum Neural Network for Accelerated Magnetic Resonance Imaging
Shuo Zhou, Yihang Zhou, Congcong Liu +4
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms t…
Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network
Ling Lin, Yihang Zhou, Zhanqi Hu +8
Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications. This study addresses the critical need for robust classification mo…
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
Shanshan Wang, Huitao Cheng, Leslie Ying +5
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Differe…