This is the Linux app named fastMRI whose latest release can be downloaded as Bugfixreleasesourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named fastMRI with OnWorks for free.
Segui queste istruzioni per eseguire questa app:
- 1. Scaricata questa applicazione sul tuo PC.
- 2. Entra nel nostro file manager https://www.onworks.net/myfiles.php?username=XXXXX con il nome utente che desideri.
- 3. Carica questa applicazione in tale file manager.
- 4. Avviare l'emulatore online OnWorks Linux o Windows online o l'emulatore online MACOS da questo sito Web.
- 5. Dal sistema operativo OnWorks Linux che hai appena avviato, vai al nostro file manager https://www.onworks.net/myfiles.php?username=XXXXX con il nome utente che desideri.
- 6. Scarica l'applicazione, installala ed eseguila.
IMMAGINI:
risonanza magnetica rapida
DESCRIZIONE:
fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. It includes reference implementations for key MRI reconstruction architectures such as U-Net and Variational Networks (VarNet), along with example scripts for model training and evaluation using the PyTorch Lightning framework. The project also releases several fully anonymized public MRI datasets, including knee, brain, and prostate scans.
Caratteristiche
- Open source PyTorch framework for accelerated MRI reconstruction research
- Includes MRI datasets (knee, brain, prostate) with raw k-space and DICOM data
- Provides U-Net, VarNet, and ESPIRiT-based reconstruction models
- Offers standardized evaluation metrics and reproducible training pipelines
- Compatible with PyTorch Lightning for modular training and logging
- Benchmarks from major publications and challenge datasets included
Linguaggio di programmazione
Python
Categorie
This is an application that can also be fetched from https://sourceforge.net/projects/fastmri.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.