Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies

no code implementations • 29 Jan 2024 • Jiahao Huang, Yinzhe Wu, Fanwen Wang, Yingying Fang, Yang Nan, Cagan Alkan, Lei Xu, Zhifan Gao, Weiwen Wu, Lei Zhu, Zhaolin Chen, Peter Lally, Neal Bangerter, Kawin Setsompop, Yike Guo, Daniel Rueckert, Ge Wang, Guang Yang

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans.

Federated Learning MRI Reconstruction

AutoSamp: Autoencoding MRI Sampling via Variational Information Maximization

2 code implementations • 5 Jun 2023 • Cagan Alkan, Morteza Mardani, Shreyas S. Vasanawala, John M. Pauly

Experiments on public MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling.

Anatomy

Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple Contrasts

1 code implementation • NeurIPS Workshop Deep_Invers 2021 • Victoria Liu, Kanghyun Ryu, Cagan Alkan, John M. Pauly, Shreyas Vasanawala

To address this issue, we propose multi-task learning (MTL) schemes that can jointly reconstruct multiple datasets.

MRI Reconstruction Multi-Task Learning

Learning to Sample MRI via Variational Information Maximization

no code implementations • 23 Oct 2020 • Cagan Alkan, Morteza Mardani, Shreyas Vasanawala, John M. Pauly

Accelerating MRI scans requires optimal sampling of k-space data.