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Abstract #1768

Extending Scan-specific Artifact Reduction in K-space (SPARK) to Advanced Encoding and Reconstruction Schemes

Yamin Arefeen1, Onur Beker2, Heng Yu3, Elfar Adalsteinsson4,5,6, and Berkin Bilgic5,7,8
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Department of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 3Department of Automation, Tsinghua University, Beijing, China, 4Massachusetts Institute of Technology, Cambridge, MA, United States, 5Harvard-MIT Health Sciences and Technology, Cambridge, MA, United States, 6Institute for Medical Engineering and Science, Cambridge, MA, United States, 7Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 8Department of Radiology, Harvard Medical School, Boston, MA, United States

Scan-specific learning techniques improve accelerated MRI reconstruction by training models using data solely from the specific scan; but they are constrained to Cartesian imaging and require an integrated auto-calibration signal (ACS), reducing acceleration. This abstract extends the scan-specific model SPARK, which estimates and corrects reconstruction errors in k-space, to arbitrary acquisitions and reconstructions. We demonstrate improvements in 3D volumetric imaging either with an integrated or external ACS region and in simultaneous multi-slice, wave-encoded imaging. SPARK enables an order of magnitude acceleration with ~2-fold reduction in reconstruction error compared to advanced reconstruction techniques that serve as its input.

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