Evaluating a potential technique with local optical flow vectors for automatic organ-at-risk (OAR) intrusion detection and avoidance during radiotherapy

• Published in: Physics in medicine & biology Vol. 64; no. 14; p. 145008
• Main Authors: Troy Teo, P, Guo, Kaiming, Ahmed, Bilal, Alayoubi, Nadia, Kehler, Katherine, Fontaine, Gabrielle, Sasaki, David, Pistorius, Stephen
• Format: Journal Article
• Language: English
• Published: England 16-07-2019
• Subjects: Algorithms; Humans; Lung Neoplasms - radiotherapy; Movement; Organs at Risk - radiation effects; Phantoms, Imaging; Radiotherapy Planning, Computer-Assisted - methods; Reproducibility of Results; Respiration; Respiratory-Gated Imaging Techniques - instrumentation; Respiratory-Gated Imaging Techniques - methods
• ISSN: 1361-6560
• DOI: 10.1088/1361-6560/ab2db4

Various techniques of deep inspiration breath hold (DIBH) have been used to mitigate the likelihood and risk of exposing the heart, an organ-at-risk (OAR) for unintended radiation during left breast radiotherapy. However, issues of reproducibility of these techniques warrant further investigation into the feasibility of detecting the intrusion of an OAR into the treatment field during intra-fractional treatment delivery. The increase of high-dose, low-fraction radiotherapy treatments makes it important to immediately adapt treatment once an OAR is detected in the treatment field. This proof-of-concept implementation includes an algorithm that detects and tracks the motion at the edges of a treatment field and a control algorithm that adapts the treatment aperture according to the motion detected. In accordance to the AAPM Task-Group (TG-132) report, image registration techniques should be verified with virtual and physical phantoms prior to clinical application. Since most OARs move as a result of respiration-induced motion, we have used a lung phantom to generate images of a generic OAR intruding into a treatment field with known velocity. The phantom was programmed to move with sinusoidal and lung patient tumor motion patterns and the accuracy of intrusion tracking and MLC adaptation were benchmarked with the ground truth-programmed motion of the OAR. The motions were recorded with an electronic portal imaging device (EPID). An optimal cluster size of 9  ×  9 motion vectors was found to provide the smallest average absolute position error of 0.3 mm. A strong linear correlation between the adapted MLC leaves and the actual OAR position was observed. The algorithm had a mean position tracking error of  -0.4  ±  0.3 mm and a precision of 1.1 mm. It is possible to adapt MLC leaves based on the motion detected at the edges of the irradiated field, and it would be feasible to shield an unplanned intrusion of an OAR into the treatment field.