Precise tracking of lung tumor motion during treatment delivery still represents a challenge in radiation therapy. Prototypes of MRI-linac hybrid systems are being created which have the potential of ionization-free real-time imaging of the tumor. This study evaluates the performance of lung tumor tracking algorithms in cine-MRI sagittal images from five healthy volunteers. Visible vascular structures were used as targets. Volunteers performed several series of regular and irregular breathing. Two tracking algorithms were implemented and evaluated: a template matching (TM) algorithm in combination with surrogate tracking using the diaphragm (surrogate was used when the maximum correlation between the template and the image in the search window was less than specified), and an artificial neural network (ANN) model based on the principal components of a region of interest that encompasses the target motion. The mean tracking error ē and the error at 95% confidence level e(95) were evaluated for each model. The ANN model led to ē = 1.5 mm and e(95) = 4.2 mm, while TM led to ē = 0.6 mm and e(95) = 1.0 mm. An extra series was considered separately to evaluate the benefit of using surrogate tracking in combination with TM when target out-of-plane motion occurs. For this series, the mean error was 7.2 mm using only TM and 1.7 mm when the surrogate was used in combination with TM. Results show that, as opposed to tracking with other imaging modalities, ANN does not perform well in MR-guided tracking. TM, however, leads to highly accurate tracking. Out-of-plane motion could be addressed by surrogate tracking using the diaphragm, which can be easily identified in the images.
The clinical use of surface imaging has increased dramatically, with demonstrated utility for initial patient positioning, real-time motion monitoring, and beam gating in a variety of anatomical sites. The Therapy Physics Subcommittee and the Imaging for Treatment Verification Working Group of the American Association of Physicists in Medicine commissioned Task Group 302 to review the current clinical uses of surface imaging and emerging clinical applications. The specific charge of this task group was to provide technical guidelines for clinical indications of use for general positioning, breast deep-inspiration breath hold treatment, and frameless stereotactic radiosurgery. Additionally, the task group was charged with providing commissioning and on-going quality assurance (QA) requirements for surface-guided radiation therapy (SGRT) as part of a comprehensive QA program including risk assessment. Workflow considerations for other anatomic sites and for computed tomography simulation, including motion management, are also discussed. Finally, developing clinical applications, such as stereotactic body radiotherapy (SBRT) or proton radiotherapy, are presented. The recommendations made in this report, which are summarized at the end of the report, are applicable to all video-based SGRT systems available at the time of writing.
Background and Purpose Brain radiotherapy is limited in part by damage to white matter, contributing to neurocognitive decline. We utilized diffusion tensor imaging (DTI) with multiple b-values (diffusion weightings) to model the dose-dependency and time course of radiation effects on white matter. Materials and Methods Fifteen patients with high-grade gliomas treated with radiotherapy and chemotherapy underwent MRI with DTI prior to radiotherapy, and after months 1, 4-6, and 9-11. Diffusion tensors were calculated using three weightings (high, standard, and low b-values) and maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ‖), and radial diffusivity (λ⊥) were generated. The region of interest was all white matter. Results MD, λ‖, and λ⊥increased significantly with time and dose, with corresponding decrease in FA. Greater changes were seen at lower b-values, except for FA. Time-dose interactions were highly significant at 4-6 months and beyond (p < .001), and the difference in dose response between high and low b-values reached statistical significance at 9-11 months for MD, λ‖, and λ⊥ (p < .001, p < .001, p = .005 respectively) as well as at 4-6 months for λ‖ (p = .04). Conclusions We detected dose-dependent changes across all doses, even <10 Gy. Greater changes were observed at low b-values, suggesting prominent extracellular changes possibly due to vascular permeability and neuroinflammation.
Late cardiac complications may arise after left-breast radiation therapy. Deep-inspiration breath hold (DIBH) allows reduction of the irradiated heart volume at the same time as it reduces tumor bed motion and increases lung sparing. In the present study, we have evaluated the improvement in reproducibility and stability of the DIBH for left-breast-cancer treatment when visual coaching is provided with the aid of 3D video surface imaging and video goggles. Five left-breast-cancer patients and fifteen healthy volunteers were asked to perform a series of DIBHs without and with visual coaching. Reproducibility and stability of DIBH were measured for each individual with and without visual coaching. The average reproducibility and stability changed from 2.1 mm and 1.5 mm, respectively, without visual feedback to 0.5 mm and 0.7 mm with visual feedback, showing a significant statistical difference (p < 0.001 for reproducibility, p < 0.01 for stability). Significant changes (>2 mm) in reproducibility and stability were observed in 35% and 15% of the subjects, respectively. The average chest wall excursion of the DIBH with respect to the free breathing preceding the DIBH was found to be 11.3 mm. The reproducibility and stability of the DIBH improve significantly from the visual coaching provided to the patient, especially in those patients with poor reproducibility and stability.
Lung tumor motion due to respiration poses a challenge in the application of 10 modern three-dimensional conformal radiotherapy. Direct tracking of the lung tumor during radiation therapy is very difficult without implanted fiducial markers. Indirect tracking relies on the correlation of the tumor's motion and the surrogate's motion. The present paper presents an analysis of the correlation between the tumor motion and the diaphragm motion in order to evaluate the 15 potential use of diaphragm as a surrogate for tumor motion. We have analyzed the correlation between diaphragm motion and superior-inferior lung tumor motion in 32 fluoroscopic image sequences from 10 lung cancer patients. A simple linear model and a more complex linear model that accounts for phase delays between the two motions have been used. Results show that the 20 diaphragm is a good surrogate for tumor motion prediction for most patients, resulting in an average correlation factor of 0.94 and 0.98 with each model respectively. The model that accounts for delays leads to an average localization prediction error of 0.8mm and an error at the 95% confidence level of 2.1mm. However, for one patient studied, the correlation is much weaker compared to 25 other patients. This indicates that, before using diaphragm for lung tumor prediction, the correlation should be examined on a patient-by-patient basis.
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