Keywords: Artifacts, Motion Correction, abdomen, respiration
Motion, particularly from breathing, compromises the quality of magnetic resonance images. In this work, we hypothesize that detected breathing patterns can be utilized to predict whether adequate MR image quality will be obtained. With a K-means clustering algorithm, 9 in 10 forty-second breathing waveforms were correctly predicted as either resulting in a high or low image quality image; this finding can save time from unnecessary scans. Other models achieved similar results as K-means clusterings.
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