2020 National Institute of Biomedical Imaging and Bioengineering (NIBIB) Trailblazer award
2020 National Heart, Lung, and Blood Institute (NHLBI) R01 award
2020 Guillermo E. Borja Award (Collegiate Award)
2018-2020 McKnight Land-Grant Professor
2017 National Science Foundation (NSF) CAREER Award
2013 Junior Fellow, International Society of Magnetic Resonance in Medicine
2012 National Institutes of Health (NIH) K99/R00 award
2023 IEEE Signal Processing Society PhD Dissertation Award for Burhaneddin Yaman [Supervisor]
K. Hammernik, T. Kustner, B. Yaman, Z. Huang, D. Rueckert, F. Knoll and M. Akçakaya, "Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging," IEEE Signal Processing Magazine, 40(1), pp. 98-114, Jan. 2023.
H. Gu, B. Yaman, S. Moeller, J. Ellermann, K. Uğurbil and M. Akçakaya, "Revisiting l1-wavelet compressed sensing MRI in the era of deep learning," Proceedings of the National Academy of Sciences (PNAS), 119(33):e2201062119, Aug. 16, 2022.
M. Akçakaya, B. Yaman, H. Chung and J. C. Ye, "Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement," IEEE Signal Processing Magazine, vol. 39, no. 2, pp. 28-44, March 2022.
S. Moeller, P. K. Pisharady, S. Ramanna, C. Lenglet, X. Wu, E. Yacoub, K. Uğurbil 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.
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.
S. A. Hosseini, B. Yaman, S. Moeller, M. Hong and M. Akçakaya, "Dense Recurrent Neural Networks for Inverse Problems: History-Cognizant Unrolling of Optimization Algorithms," IEEE Journal on Special Topics in Signal Processing, 14(6), pp. 1280-1291, Oct. 2020.
F. Knoll, K. Hammernik, C. Zhang, S. Moeller, T. Pock, D. K. Sodickson and M. Akçakaya, "Deep Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction," IEEE Signal Processing Magazine, 37(1), pp. 128-140, Jan. 2020.
M. Akçakaya, S. Moeller, S. Weingärtner and K. Uğurbil, "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):439-453, Jan. 2019.
S. Weingärtner, S. Moeller, S. Schmitter, E. Auerbach, P. Kellman, C. Shenoy and M. Akçakaya, "Simultaneous Multi-Slice Imaging for Native Myocardial T1 Mapping: Improved Spatial Coverage in a Single Breath-Hold," Magnetic Resonance in Medicine, 78(2), pp. 462-471, Aug. 2017.
M. Akçakaya, T. A. Basha, B. Goddu, L. A. Goepfert, K. V. Kissinger, V. Tarokh, W. J. Manning and R. Nezafat, “Low-dimensional-Structure Self-Learning and Thresholding (LOST): Regularization Beyond Compressed Sensing for MRI Reconstruction,” Magnetic Resonance in Medicine, 66(3), pp. 756-767, Sep 2011.
