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Abstract #0610

Visual Evaluative Control Technology Of Resonance Spectroscopy (VECTORS): Automated Data Quality Control Pipeline for Spectroscopy Data

Bodhi Beroukhim1, Zhou Lan2,3, Ved Halkotar1,4, Skyler McComas1, Katherine Breedlove3, and Alexander Peter Lin3
1Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital, Boston, MA, United States, 2Center for Clinical Investigation, Department of Medicine, Brigham and Women's Hospital/Harvard Med School, Boston, MA, United States, 3Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital/Harvard Med School, Boston, MA, United States, 4Department of Biomedical and Molecular Sciences, Queens University, Kingston, ON, Canada

Synopsis

Keywords: Spectroscopy, Software Tools, Data Quality Control, Automated Pipeline, Artifacts

Motivation: The absence of a robust data quality control (DQC) process inhibits the widespread adoption of MRS. Standard quantitative metrics (noise, linewidth, and metabolite fitting) don’t account for all DQ criteria, while manual DQC is subjective, irreproducible, labor-intensive, and requires expertise.

Goal(s): Develop an automated DQC pipeline that accounts for all DQC criteria while minimizing manual labor, subjectivity, variability, and required expertise.

Approach: The pipeline quantifies and replicates an expert’s qualitative analysis.

Results: The pipeline accurately flags additional spectra containing movement, phase, lipid, out-of-volume lipid, suppression, ghosting, and poor fit artifacts

Impact: As a comprehensive, automated, and efficient MRS DQC process, the pipeline can help standardize DQC and expand the field to scientists with little MRS expertise. The pipeline should be used in addition to standard quantitative metrics.

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Keywords