### 10) Unsupervised Physics-Guided Training for pTx pulse design

- Implementation of unsupervised physics-guided training for pTx pulse design is available here, maintained by our lab member Toygan Kilic.

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

i) T. Kilic, P. Liebig, O. B. Demirel, J. Herrler, A. Nagel, K. Ugurbil and M. Akçakaya, “Unsupervised Deep Learning with Convolutional Neural Networks for Static Parallel Transmit Design”, *Magnetic Resonance in Medicine*, 91(6), pp. 2498-2507, Jan 2024.

### 9) SMS-COOKIE

- Implementation of SMS-COOKIE for leakage-blocking regularized SMS reconstruction is available here, maintained by our lab member Burak Demirel.

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

i) O. B. Demirel, S. Weingartner, S. Moeller and** **M. Akçakaya, "Improved Simultaneous Multi-slice Imaging with Composition of k-space Interpolations (SMS-COOKIE) for Myocardial T1 Mapping," *PLOS ONE, *March 2023.

### 8) Signal Intensity Informed Multi-Coil Operator for Deep Learning Reconstruction

- Implementation of physics-guided deep learning MRI reconstruction with signal intensity informed multi-coil operator is available here, maintained by our lab member Burak Demirel.

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

i) O. B. Demirel, B. Yaman, C. Shenoy, S. Moeller, S. Weingartner and** **M. Akçakaya, "Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR," *Magnetic Resonance in Medicine, *89(1), pp. 308-321, Jan. 2023.

### 7) Residual RAKI (rRAKI) Reconstruction

- Implementation of rRAKI, a hybrid linear and non-linear approach for scan-specific k-space deep learning is available here, maintained by our lab member Chi Zhang.

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

i) C. Zhang, S. Moeller, O. B. Demirel, K. Uğurbil and** **M. Akçakaya, "Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning," *Neuroimage, *256:119248, Aug. 1 2022.

### 6) Revisiting *l*_{1}-wavelet Compressed Sensing Reconstruction

- Implementation of l1-wavelet compressed sensing reconstructing using modern data science tolls is available here, maintained by our lab member Hongyi Gu.

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

i) H. Gu, B. Yaman, S. Moeller, J. Ellermann, K. Uğurbil and** **M. Akçakaya, "Revisiting l_{1}-wavelet compressed sensing MRI in the era of deep learning," *Proceedings of the National Academy of Sciences* (*PNAS*), 119(33):e2201062119, Aug. 16 2022.

### 5) Zero-Shot Self-Supervised Deep Learning Reconstruction (ZS-SSL)

- Implementation of zero-shot self-supervised learning for scan-specific reconstruction is available here, maintained by our lab member Burhaneddin Yaman.

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

i) B. Yaman, S. A. Hosseini and** **M. Akçakaya, "Zero-Shot Self-Supervised Learning for MRI Reconstruction," *International Conference on Learning Representations, *April 2022.

### 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.