Magnetic resonance (MR) is a sensitive diagnostic imaging modality that allows specific investigation of the structure and function of the brain and body. One major drawback is the overall MR acquisition time, which can easily exceed 30 minutes per subject. Lengthy MR acquisition times are costly (~$300 USD/per exam), increase susceptibility to motion artifacts, which negatively impact image quality, reduce patient throughput and contribute to patient discomfort. Parallel imaging (PI) and compressed sensing (CS) are two proven approaches that allow to speed-up MR exams by collecting fewer k-space samples than stated by the Nyquist sampling theorem. Deep learning methods are arguably the state-of-the-art for accelerated (i.e., from undersampled k- space) MR reconstruction. Many works in the literature indicate that there is potential to make MR exams up to ten times faster using sophisticated deep-learning-based reconstruction algorithms.To put that in perspective, in this challenge we use 1 mm isotropic 3D T1-weighted brain MR acquisitions that took on average nearly six minutes to be acquired. Making it ten times faster would reduce the exam time to nearly 36 seconds and that is expected to have an enormous societal impact. Deep learning reconstruction are divided in four groups: k-space-domain, image-domain, domain-transform, and hybrid k-space/image-domains learning. At the moment, there is no clear winner among these proposed models. That happens in part due to the lack of benchmark datasets that allow fair comparisons. The fastMRI initiative is one good step in that direction,. Our challenge is a complimentary initiative that provides high-resolutuon 3D brain data. Working with 3D data allows you to undersample in both the phase-encoded and the slice-encoded directions (i.e. sparser data), which potentially allows to further undersample k-space during acquisition. Most works so far investigated models that are specific to a coil with a given number of channels. Our challenge tackles this issue. The goals of this challenge are:
The challenge is composed of two separate tracks and teams are free to decide whether to submit to just one track or both. We encourage teams to submit to both tracks. Each track will have a separate ranking.
In these two tracks, we expect to be able to assess MR reconstruction quality, which tends to result in reconstruction with noticeable loss in the high-frequency components, specially for such high acceleration rates. Also by having two separate tracks, we expect to be able to quantify whether a more generic model capable of reconstructing images acquired using coils with different number of channels will have a decreased performance (if any) compared to a more specific model.
./requirements.txt
python setup.py develop
in this directory to add the project directory to your PYTHONPATH environment variableUSER_SPECIFIED_DATA_PATH
variable in ./mrrec/constants.py
module to that directory.DataGenerator
and baseline models.- [Baseline models](https://github.com/rmsouza01/CD-Deep-Cascade-MR-Reconstruction)
- [ResoNNance](https://github.com/directgroup/direct)
- [The Enchanted](https://github.com/amritkumar9595/the-enchanted-MC-MRRec)
- [TUMRI](https://github.com/tum-ri/mcmrireconz
- [M-L UNICAMP](https://github.com/alelopes/hybrid-multipath-reconstruction-network)
More details about the challenge are available at the challenge webpage If you have any question or doubts, please contact Dr. Roberto Souza ([email protected]). He should be able to answer them and potentially add them to the FAQ page in the website.