Purpose: To develop a deep learning method on a nonlinear manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data from highly undersampled measurements. Methods:
The dataset contains two folders: 1) Imaging, and 2) Post-processing, each containing data from individual patients (P1, P2, D1, D2, S1 and S2). In details: - the Imaging folder contains MRI DICOM d
Raw data and scripts for performing a limited meta-review in the aneurysm literature on method reproducibility. Search in PubMed: https://biturl.top/MjABrq
This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The data provided to applicants of the challenge