Keywords: Tumors (Pre-Treatment), Software Tools, AI/ML Software; Brain; Machine Learning/Artificial Intelligence;Neuro; Spectroscopy; Tumors
Motivation: Acquiring single-voxel Magnetic Resonance Spectroscopy (MRS) data in clinic currently involves manual voxel placement by technicians without the time capacity to review tumor biology in detail, leading to poor-quality spectra.
Goal(s): To achieve consistent and accurate single-voxel placement to minimize variability in metabolite quantification.
Approach: We developed an auto-placement algorithm that identifies an optimized MRS single-voxel position and rotation based on tumor biology (tumor core, necrosis, and edema) and outputs this voxel as a mask on MR Imaging.
Results: Performance of the automated MRS single-voxel placement rivals clinical placement and integrates with an existing clinically implemented automated brain tumor segmentation workflow.
Impact: Our new algorithm will assist radiology technicians in reliably placing MR Spectroscopy single-voxels with accuracy that rivals clinical placement. This is a primary need for non-invasive diagnosis and management of diffuse gliomas.
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