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On-treatment EPID images are contaminated with patient-generated scattered photons. If this component can be accurately estimated, its effect can be removed, and therefore a corresponding in vivo patient dose estimate will be more accurate. Our group previously developed a "tri-hybrid" (TH) algorithm to provide fast but accurate estimates of patient-generated photon scatter. The algorithm uses an analytical method to solve for singly-scattered photon fluence, a modified Monte Carlo hybrid method to solve for multiply-scattered photon fluence, and a pencil beam scatter kernel method to solve for electron interaction generated scattered photon fluence. However, for efficient clinical implementation, spatial and energy sampling must be optimized for speed while maintaining overall accuracy. In this work, the most significant sampling issues were examined, including spatial sampling settings for the patient voxel size, the number of Monte Carlo histories used in the modified hybrid MC method, scatter order sampling for the hybrid method, and also a range of energy spectrum sampling (i.e., energy bin sizes). The total predicted patient-scattered photon fluence entering the EPID was compared with full MC simulation (EGSnrc) for validation. Three phantoms were tested with 6 and 18 MV beam energies, field sizes of 4 × 4, 10 × 10, and 20 × 20 cm 2 , and source-to-imager distance of 140 cm to develop a set of optimal sampling settings. With the recommended sampling, accuracy and precision of the total-scattered energy fluence of the TH patient scatter prediction method are within 0.9% and 1.2%, respectively, for all test cases compared with full MC simulation results. For the mean energy spectrum across the imaging plane, comparison of TH with full MC simulation showed 95% overlap. This study has optimized sampling settings so that they have minimal impact on patient scatter prediction accuracy while maintaining maximum execution speed, a critical step for future clinical implementation., (© 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Accurate tracking of organ motion during treatment is needed to improve the efficacy of radiation therapy. This work investigates the feasibility of tracking an uncontoured target using the motion detected within a moving treatment aperture. Tracking was achieved with a weighted optical flow algorithm, and three different techniques for updating the reference image were evaluated. The accuracy and susceptibility of each approach to the accumulation of position errors were verified using a 3D-printed tumor (mounted on an actuator) and a virtual treatment aperture. Tumor motion up to 15.8 mm (peak-to-peak) taken from the breathing patterns of seven lung cancer patients was acquired using an amorphous silicon portal imager at ~ 7.5 frames/s. The first approach (INI) used the initial image detected, as a fixed reference, to determine the target motion for each new incoming image, and performed the best with the smallest errors. This method was also the most robust against the accumulation of position errors. Mean absolute errors of 0.16, 0.32, and 0.38 mm were obtained for the three methods, respectively. Although the errors are comparable to other tracking methods, the proposed method does not require prior knowledge of the tumor shape and does not need a tumor template or contour for tracking. Graphical abstract.

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.

In vivo dosimetry methods can verify the prescription dose is delivered to the patient during treatment. Unfortunately, in exit dosimetry, the megavoltage image is contaminated with patient-generated scattered photons. However, estimation and removal of the effect of this fluence improves accuracy of in vivo dosimetry methods. This work develops a 'tri-hybrid' algorithm combining analytical, Monte Carlo (MC) and pencil-beam scatter kernel methods to provide accurate estimates of the total patient-generated scattered photon fluence entering the MV imager. For the multiply-scattered photon fluence, a modified MC simulation method was applied, using only a few histories. From each second- and higher-order interaction site in the simulation, energy fluence entering all pixels of the imager was calculated using analytical methods. For photon fluence generated by electron interactions in the patient (i.e. bremsstrahlung and positron annihilation), a convolution/superposition approach was employed using pencil-beam scatter fluence kernels as a function of patient thickness and air gap distance, superposed on the incident fluence distribution. The total patient-scattered photon fluence entering the imager was compared with a corresponding full MC simulation (EGSnrc) for several test cases. These included three geometric phantoms (water, half-water/half-lung, computed tomography thorax) using monoenergetic (1.5, 5.5 and 12.5 MeV) and polyenergetic (6 and 18 MV) photon beams, 10 × 10 cm2 field, source-to-surface distance 100 cm, source-to-imager distance 150 cm and 40 × 40 cm2 imager. The proposed tri-hybrid method is demonstrated to agree well with full MC simulation, with the average fluence differences and standard deviations found to be within 0.5% and 1%, respectively, for test cases examined here. The method, as implemented here with a single CPU (non-parallelized), takes ∼80 s, which is considerably shorter compared to full MC simulation (∼30 h). This is a promising method for fast yet accurate calculation of patient-scattered fluence at the imaging plane for in vivo dosimetry applications. [ABSTRACT FROM AUTHOR]