Long scan duration remains a challenge for high-resolution MRI. Several accelerated imaging strategies have been proposed based on deep learning (DL) that require databases of fully-sampled images for training. However, scan-specific training is desired where individual variability is important, e.g. in free-breathing cardiac MRI, or where such datasets are not available due to scan time constraints for acquiring fully-sampled data. Building on our earlier method called Self-consistent Robust Artificial-neural-networks for k-space Interpolation (sRAKI), we propose a scan-specific DL reconstruction method based on recurrent neural networks that combines training and reconstruction phases of sRAKI. We use self-consistency among coils in k-space and regularization in arbitrary domains, as well as consistency with acquired data, in each iteration of the recurrent network. Results on knee MRI show that this method improves upon parallel imaging and compressed sensing methods.
|Original language||English (US)|
|Title of host publication||ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Apr 2020|
|Event||17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020 - Iowa City, United States|
Duration: Apr 4 2020 → …
|Name||ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings|
|Conference||17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020|
|Period||4/4/20 → …|
Bibliographical noteFunding Information:
This work was supported by NIH U01EB025144, P41EB027061; NSF CAREER CCF-1651825. Knee MRI data were obtained from the NYU fastMRI initiative database . NYU fastMRI database was acquired with the relevant institutional review board approvals as detailed in . A listing of NYU fastMRI investigators, subject to updates, can be found at fastmri.med.nyu.edu.
- Parallel imaging
- compressed sensing
- deep learning
- image reconstruction.
- machine learning
- neural networks