Publications by authors named "Mark D Brooke"
Article Synopsis
- The study examines the use of a deep learning (DL) autosegmentation model to speed up organ-at-risk segmentation in radiation therapy for head and neck cancer, aiming to improve treatment access without sacrificing accuracy.
- Expert radiation oncologists created gold standard (GS) contours on CT images, and a custom 3D U-Net DL model was trained to generate contour predictions, which were then compared to contours made by medical dosimetry assistants (MDAs) in a randomized trial.
- Results showed that using the DL model significantly reduced contouring time by 76% overall and 35% specifically in RO revisions, while the accuracy of DL-generated contours was equal or superior to those revised by MDAs, with 76%
Purpose: Multiple Coulomb scattering (MCS) poses a challenge in proton CT (pCT) image reconstruction. The assumption of straight paths is replaced with Bayesian models of the most likely path (MLP). Current MLP-based pCT reconstruction approaches assume a water scattering environment.
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