Keywords: Spectroscopy, Spectroscopy, Automated Prescription, Automated Scan Planning, Brain Tumors
Motivation: The expertise required to acquire high-quality Magnetic Resonance Spectroscopic Imaging (MRSI) data is a barrier to its adoption into routine clinical practice.
Goal(s): To develop a machine learning-based automated technique for the prescription of 3D MRSI examinations.
Approach: We developed an ML-based pipeline to generate an oblique excitation volume based on MR images of the brain. The models were trained on a dataset of 714 MRI/MRSI exams of patients with brain tumors and evaluated on a validation set.
Results: The models achieved a mean intersection over union (IOU) of 0.85 (axial) and 0.75 (sagittal) with standard deviations of 0.07 and 0.09 respectively.
Impact: This study demonstrates the feasibility of a machine learning-based automated prescription of 3D MRSI acquisition of the brain. This would help solve a major challenge of incorporating metabolic imaging into the routine clinical care of patients with brain tumors.
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