Keywords: Electromagnetic Tissue Properties, Machine Learning/Artificial Intelligence, Brachytherapy, Segmentation, Susceptibility
Motivation: Deep learning (DL) networks trained with synthetically generated data enable the visualization of I-125 brachytherapy seeds in prostate cancer patients in quantitative susceptibility mapping (QSM), possibly eliminating the need for a CT-scan in future.
Goal(s): The Goal was to automatically detect and segment I-125 seeds in-vivo by using a DL network directly (without QSM) on gradient-echo-sequence (GRE) data.
Approach: A U-Net was trained with synthetically generated multi-echo GRE magnitude and phase input data and corresponding target seed segmentations.
Results: The seed segmentations were of high visual quality and showed good agreement (85% detection rate) with corresponding CT-scans in five prostate cancer patients.
Impact: This work proposes a fast and completely automatic MRI-only based workflow for segmenting in-vivo brachytherapy seeds in prostate cancer patients. This approach has the potential to eliminate the need for a CT-scan, thereby reducing the use of ionizing radiation.
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