Paper ID: 2409.05680

Cherenkov Imaged Bio-morphological Features Verify Patient Positioning with Deformable Tissue Translocation in Breast Radiotherapy

Yao Chen, Savannah M. Decker, Petr Bruza, David J. Gladstone, Lesley A. Jarvis, Brian W. Pogue, Kimberley S. Samkoe, Rongxiao Zhang

Accurate patient positioning is critical for precise radiotherapy dose delivery, as positioning errors can significantly affect treatment outcomes. This study introduces a novel method for tracking loco-regional tissue deformation through Cherenkov image analysis during fractionated breast cancer radiotherapy. The primary goal was to develop and test an algorithm for Cherenkov-based regional position accuracy quantification, specifically for loco-regional deformations, which lack ideal quantification methods in radiotherapy. Blood vessel detection and segmentation were developed in Cherenkov images using a tissue phantom with incremental movements, and later applied to images from fractionated whole breast radiotherapy in human patients (n=10). A combined rigid and non-rigid registration technique was used to detect inter- and intra-fractional positioning variations. This approach quantified positioning variations in two parts: a global shift from rigid registration and a two-dimensional variation map of loco-regional deformation from non-rigid registration. The methodology was validated using an anthropomorphic chest phantom experiment, where known treatment couch translations and respiratory motion were simulated to assess inter- and intra-fractional uncertainties, yielding an average accuracy of 0.83 mm for couch translations up to 20 mm. Analysis of clinical Cherenkov data from ten breast cancer patients showed an inter-fraction setup variation of 3.7 plus minus 2.4 mm relative to the first fraction and loco-regional deformations (95th percentile) of up to 3.3 plus minus 1.9 mm. This study presents a Cherenkov-based approach to quantify global and local positioning variations, demonstrating feasibility in addressing loco-regional deformations that conventional imaging techniques fail to capture.

Submitted: Sep 9, 2024