8 papers
Characterizing the Reliability of a Novel Upright CT for Proton Therapy
Yuhao Yan, Jordan Slagowski, Jessica Miller +4
Purpose: To evaluate reliability of upright CT for proton dose calculation and feasibility of a simplified phantom configuration for accelerated routine QA. Methods: A calibration…
Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation
Nicholas Summerfield, Qisheng He, Alex Kuo +9
Cardiac substructure delineation is emerging in treatment planning to minimize the risk of radiation-induced heart disease. Deep learning offers efficient methods to reduce contour…
Evaluation of a Novel Quantitative Multiparametric MR Sequence for Radiation Therapy Treatment Response Assessment
Yuhao Yan, R. Adam Bayliss, Adam R. Burr +5
Purpose: To evaluate a Deep-Learning-enhanced MUlti-PArametric MR sequence (DL-MUPA) for treatment response assessment for brain metastases patients undergoing stereotactic radiosu…
Technical assessment of a novel vertical CT system for upright radiotherapy simulation and treatment planning
Jordan M. Slagowski, Yuhao Yan, Jessica R. Miller +4
Purpose: To characterize image quality, imaging dose, and dose calculation accuracy for an upright CT scanner with a six-degree-of-freedom patient positioning system. Methods: Imag…
Volumetric medical image segmentation through dual self-distillation in U-shaped networks
Soumyanil Banerjee, Nicholas Summerfield, Ming Dong +1
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework in…
Deducing Cardiorespiratory Motion of Cardiac Substructures Using a Novel 5D-MRI Workflow for Radiotherapy
Chase Ruff, Tarun Naren, Oliver Wieben +5
Objective: Cardiotoxicity is a devastating complication of thoracic radiotherapy. Current radiotherapy imaging protocols are insufficient to decouple and quantify cardiac motion, l…