Abstract #5015
Automated MRI k-space Motion Artifact Detection in Segmented Multi-Slice Sequences
Ikbeom Jang†1,2, Robert S Frost†1,2, Malte Hoffmann1,2, Nalini M Singh3,4, Lina Chen5, Arnaud Guidon6, Marcio Aloisio Bezerra Cavalcanti Rockenbach5, Donnella S Comeau5, Bernardo C Bizzo5, Ken Chang1,4, Sage Witham6, Dan Rettmann6, Suchandrima Banerjee6, Anja Brau6, Timothy G Reese1,2, Iman Aganj1,2, Adrian Dalca1,2,3, Bruce Fischl*1,2,3,4, and Jayashree Kalpathy-Cramer*1,2,5
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 5MGH & BWH Center for Clinical Data Science, Mass General Brigham, Boston, MA, United States, 6GE Healthcare, Chicago, IL, United States
Synopsis
Motion artifacts negatively impact diagnosis and the radiology workflow, especially in cases where a patient recall is required. Detecting motion artifacts while the patient is still in the scanner could potentially improve workflow and reduce costs by enabling efficient corrective action. We introduce an algorithm that detects motion artifacts directly from raw k-space in a supervised learning manner in clinically important 2D FSE multi-slice scans, using cross-correlation between adjacent phase-encoding lines as features. This study employs a training approach that uses a motion simulator to generate k-space data with varying levels of motion artifact.
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