2 citations · 3 across the 7 of their papers we have counts for
7 papers
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation
Lichao Wang, Jiahao Huang, Xiaodan Xing +7
This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diff…
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study
Jiahao Huang, Pedro F. Ferreira, Lichao Wang +11
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the micro-structure of myocardial tissue in the living hear…
ViGU: Vision GNN U-Net for Fast MRI
Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schonlieb +1
Deep learning models have been widely applied for fast MRI. The majority of existing deep learning models, e.g., convolutional neural networks, work on data with Euclidean or regul…
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI
Jiahao Huang, Xiaodan Xing, Zhifan Gao +1
Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhil…
Fast MRI Reconstruction: How Powerful Transformers Are?
Jiahao Huang, Yinzhe Wu, Huanjun Wu +1
Magnetic resonance imaging (MRI) is a widely used non-radiative and non-invasive method for clinical interrogation of organ structures and metabolism, with an inherently long scann…
Swin Transformer for Fast MRI
Jiahao Huang, Yingying Fang, Yinzhe Wu +6
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for…