Software

4) Self-Supervised Deep Learning Reconstruction (SSDU)

- Implementation of self-supervision via data undersampling (SSDU) is available here, maintained by our lab member Burhaneddin Yaman.

- If you use this software in one of your publications, please cite the two papers listed below:

i) B. Yaman, S. A. Hosseini, S. Moeller, J. Ellermann, K. Uğurbil and M. Akçakaya, "Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data," Magnetic Resonance in Medicine, 84(6), pp. 3172-3191, Dec. 2020.

ii) B. Yaman, S. A. Hosseini, S. Moeller, J. Ellermann, K. Uğurbil and M. Akçakaya, “Self-Supervised Physics-Based Deep Learning MRI Reconstruction without Fully-Sampled Data,” IEEE International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, April 2020.

 

3) ReadOut Concatenated k-space SPIRiT (ROCK-SPIRiT) Reconstruction

- Implementation of ROCK-SPIRiT is available here, maintained by our lab member Burak Demirel.

- If you use this software in one of your publications, please cite the paper below:

i) O. B. Demirel, S. Weingärtner, S. Moeller and M. Akçakaya, "Improved Simultaneous Multislice Cardiac MRI using readout concatenated k-space SPIRiT (ROCK-SPIRiT)," Magnetic Resonance in Medicine, 85(6), pp. 3036-3048, June 2021.

 

2) NOise Reduction with DIstribution Corrected (NORDIC) PCA Denoising

- Implementation of NORDIC denoising is available here, maintained by our collaborator Steen Moeller.

- If you use this software in one of your publications, please cite the paper below:

i) S. Moeller, P. K. Pisharady, S. Ramanna, C. Lenglet, X. Wu, E. Yacoub, K. Ugurbil and M. Akçakaya, "NOise Reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing," NeuroImage, 226:117539, Nov. 10, 2020.

 

1) Robust Artificial-neural-networks for k-space Interpolation (RAKI) Reconstruction 

- Implementation of RAKI is available here, maintained by our lab member Chi Zhang.

- If you use this software in one of your publications, please cite the two papers listed below:

i) M. Akçakaya, S. Moeller, S. Weingärtner and K. Ugurbil, "Scan-specific Robust Artificial-neural-networks for k-space Interpolation (RAKI) Reconstruction: Database-free Deep Learning for Fast Imaging," Magnetic Resonance in Medicine, 81(1), pp. 439-453, Jan. 2019

ii) C. Zhang, S. A. Hosseini, S. Weingärtner, S. Moeller, K. Ugurbil and M. Akçakaya, "Optimized Fast GPU Implementation of Robust Artificial-neural-networks for k-space Interpolation (RAKI) Reconstruction," PLoS ONE, 14(10):e0223315, Oct. 23, 2019.