Keywords: Analysis/Processing, Data Processing
Motivation: Macromolecular proton fraction (MPF) quantification based on spin-lock (MPF-SL) is a technology, which sensitively measures macromolecule content using the MT effect. However, the motion of liver can lead to inaccurate MPF-SL quantification.
Goal(s): Develop an automated processing approach that can detect motion of the liver to reduce the impact on MPF-SL quantification.
Approach: We trained a deep learning model to automatically detect the motion of the liver during MPF-SL acquisition.
Results: The proposed model demonstrated good performance with an accuracy of 86.4% and an area under the receiver operating characteristic curve (AUC) of 0.79.
Impact: Our approach enables automated motion detection of the liver during MPF-SL scan. It can improve reliability of parameter quantification by either discarding unreliable measurements retrospectively or prompt data recollection prospectively during scanning.
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