/data_adaptive_recon_MRI

Matlab demos for data adaptive dynamic and diffusion MRI

Primary LanguageC++

Open source software package for data adaptive reconstruction methods for multi-dimensional MRI

This package provides MATLAB demos for the below class of learning methods are provided in dynamic MRI and diffusion MRI applications.

a) Sparsity and low rank constrained reconstruction (k-t SLR) [1]
b) Blind compressed sensing (BCS) [2] [3]
c) Explicit motion compensated recovery: Deformation correction compressed sensing (DC-CS) [4]
d) Patch based regularization for implicit motion compensation (PRICE) [5]
e) Smoothness regularization on a manifold (STORM) [6]
f) Multi shot sensitivity encoded diffusion data recovery using structured low rank matrix completion (MUSSELS) [7]

Demos are based on example raw single coil and multi coil Cartesian and non-Cartesian k-space datasets from a variety of applications involving arbitrary spatio-temporal dynamics and contrast changes. These include free breathing ungated cardiac cine MRI, free breathing myocardial perfusion MRI, vocal tract MRI during speech, multi-parametric MRI, and multi-shot diffusion weighted EPI.

Code: https://github.com/sajanglingala/data_adaptive_recon_MRI

Data: https://drive.google.com/drive/folders/1OZ-K9xyAwyVSVhUv9VqOAf21LtXqRSS0?usp=sharing

References:

[1] Lingala, S. G., Hu, Y., DiBella, E., & Jacob, M. (2011). Accelerated dynamic MRI exploiting sparsity and low-rank structure: kt SLR. IEEE transactions on medical imaging, 30(5), 1042-1054.
[2] Lingala, S. G., & Jacob, M. (2013). Blind compressive sensing dynamic MRI. IEEE transactions on medical imaging, 32(6), 1132-1145.
[3] Bhave, S., Lingala, S. G., Johnson, C. P., Magnotta, V. A., & Jacob, M. (2016). Accelerated whole‐brain multi‐parameter mapping using blind compressed sensing. Magnetic resonance in medicine, 75(3), 1175-1186.
[4] Lingala, S. G., DiBella, E., & Jacob, M. (2014). Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI. IEEE transactions on medical imaging, 34(1), 72-85.
[5] Mohsin, Y. Q., Lingala, S. G., DiBella, E., & Jacob, M. (2017). Accelerated dynamic MRI using patch regularization for implicit motion compensation. Magnetic resonance in medicine, 77(3), 1238-1248.
[6] Ahmed, A. H., Zhou, R., Yang, Y., Nagpal, P., Salerno, M., & Jacob, M. (2020). Free-Breathing and Ungated Dynamic MRI Using Navigator-Less Spiral SToRM. IEEE Transactions on Medical Imaging, 39(12), 3933-3943.
[7] Mani M, Aggarwal HK, Magnotta V, Jacob M. Improved MUSSELS reconstruction for high-resolution multi-shot diffusion weighted imaging. Magn Reson Med. 2020 Jun;83(6):2253-2263. doi: 10.1002/mrm.28090. Epub 2019 Dec 2. PMID: 31789440.

For any questions, please feel free to contact:

Sajan Lingala (sajangoud-lingala@uiowa.edu)
Merry Mani (merry-mani@uiowa.edu)
Abdul Haseeb Ahmed (abdul-ahmed@uiowa.edu)
Mathews Jacob (mathews-jacob@uiowa.edu)