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Abstract #3435

Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK) 

Onur Beker1,2, Congyu Liao1,3, Jaejin Cho1,3, Zijing Zhang1,4, Kawin Setsompop1,3,5, and Berkin Bilgic1,3,5
1Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, 3Harvard Medical School, Boston, MA, United States, 4College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, 5Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States

We propose a convolutional neural network (CNN) approach that works synergistically with physics-based reconstruction methods to reduce artifacts in accelerated MRI. Given reconstructed coil k-spaces, our network predicts a k-space correction term for each coil. This is done by matching the difference between the acquired autocalibration lines and their erroneous reconstructions, and generalizing this error term over the entire k-space. Application of this approach on existing reconstruction methods show that SPARK suppresses reconstruction artifacts at high acceleration, while preserving and improving on detail in moderate acceleration rates where existing reconstruction algorithms already perform well; indicating robustness.

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