This is the Windows 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.
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- 7. Unduh Wine dari repositori perangkat lunak distribusi Linux Anda. Setelah terinstal, Anda kemudian dapat mengklik dua kali aplikasi untuk menjalankannya dengan Wine. Anda juga dapat mencoba PlayOnLinux, antarmuka mewah di atas Wine yang akan membantu Anda menginstal program dan game Windows populer.
Wine adalah cara untuk menjalankan perangkat lunak Windows di Linux, tetapi tidak memerlukan Windows. Wine adalah lapisan kompatibilitas Windows sumber terbuka yang dapat menjalankan program Windows secara langsung di desktop Linux apa pun. Pada dasarnya, Wine mencoba untuk mengimplementasikan kembali Windows dari awal sehingga dapat menjalankan semua aplikasi Windows tersebut tanpa benar-benar membutuhkan Windows.
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DESKRIPSI
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.
Fitur
- 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
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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.
