f78bono/deep-cine-cardiac-mri
Exploiting temporal redundancies of multi-coil cine cardiac data for MRI reconstruction with unrolled cross-domain networks.
This project helps medical imaging specialists and radiologists improve the quality of cardiac MRI scans, especially when data acquisition is accelerated. It takes raw, undersampled multi-coil cine cardiac MRI k-space data and reconstructs clearer, more detailed dynamic (2D space + 1D time) images of the heart. The primary users are researchers and practitioners in medical imaging who work with cardiac MRI data and need to enhance image reconstruction for better diagnosis or study.
No commits in the last 6 months.
Use this if you need to reconstruct high-quality, dynamic cardiac MRI images from accelerated, multi-coil k-space data, aiming to reduce scan times while maintaining image fidelity.
Not ideal if you are working with static MRI data (e.g., brain or knee scans), single-coil data, or if you need a pre-packaged software solution without deep learning model configuration.
Stars
18
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 09, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/f78bono/deep-cine-cardiac-mri"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
ProjectNeura/MIPCandy
Build a complete experiment pipeline for your PyTorch MIP model in 10 seconds.
canlab/CanlabCore
Core tools required for running Canlab Matlab toolboxes. The heart of this toolbox is...
tbepler/topaz
Pipeline for particle picking in cryo-electron microscopy images using convolutional neural...
MPI-Dortmund/tomotwin-cryoet
cryo-ET particle picking by representation and metric learning
bioimage-io/core-bioimage-io-python
Python libraries for loading, running and packaging bioimage.io models