Abstract #1768

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

Yamin Arefeen¹, Onur Beker², Heng Yu³, Elfar Adalsteinsson^4,5,6, and Berkin Bilgic^5,7,8

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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