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Three-dimensional dose prediction based on two-dimensional verification measurements for IMRT.

ABSTRACT: Dose verifications for intensity-modulated radiation therapy (IMRT) are generally performed once before treatment. A 39-fraction treatment course for prostate cancer delivers a dose prescription of 78 Gy in eight weeks. Any changes in multileaf collimator leaf position over the treatment course may affect the dosimetry. To evaluate the magnitude of deviations from the predicted dose over an entire treatment course with MLC leaf calibrations performed every two weeks, we tracked weekly changes in relative dose error distributions measured with two-dimensional (2D) beam-by-beam analysis. We compared the dosimetric results from 20 consecutive patient-specific IMRT quality assurance (QA) tests using beam-by-beam analysis and a 2D diode detector array to the dose plans calculated by the treatment planning system (TPS). We added back the resulting relative dose error measured weekly into the original dose grid for each beam. To validate the prediction method, the predicted doses and dose distributions were compared to the measurements using an ionization chamber and film. The predicted doses were in good agreement, within 2% of the measured doses, and the predicted dose distributions also presented good agreement with the measured distributions. Dose verification results measured once as a pretreatment QA test were not completely stable, as results of weekly beam-by-beam analysis showed some variation. Because dosimetric errors throughout the treatment course were averaged, the overall dosimetric impact to patients was small.

SUBMITTER: Sumida I

PROVIDER: S-EPMC5711089 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Project description:ObjectivesAccurate beam modelling is essential for dose calculation in stereotactic radiation therapy (SRT), such as CyberKnife treatment. However, the present deep learning methods only involve patient anatomical images and delineated masks for training. These studies generally focus on traditional intensity-modulated radiation therapy (RT) plans. Nevertheless, this paper aims to develop a deep CNN-based method for CyberKnife plan dose prediction about brain cancer patients. It utilized modelled beam information, target delineation, and patient anatomical information.MethodsThis study proposes a method that adds beam information to predict the dose distribution of CyberKnife in brain cases. A retrospective dataset of 88 brain and abdominal cancer patients treated with the Ray-tracing algorithm was performed. The datasets include patients' anatomical information (planning CT), binary masks for organs at risk (OARs) and targets, and clinical plans (containing beam information). The datasets were randomly split into 68, 6, and 14 brain cases for training, validation, and testing, respectively.ResultsOur proposed method performs well in SRT dose prediction. First, for the gamma passing rates in brain cancer cases, with the 2 mm/2% criteria, we got 96.7% ± 2.9% for the body, 98.3% ± 3.0% for the planning target volume, and 100.0% ± 0.0% for the OARs with small volumes referring to the clinical plan dose. Secondly, the model predictions matched the clinical plan's dose-volume histograms reasonably well for those cases. The differences in key metrics at the target area were generally below 1.0 Gy (approximately a 3% difference relative to the prescription dose).ConclusionsThe preliminary results for selected 14 brain cancer cases suggest that accurate 3-dimensional dose prediction for brain cancer in CyberKnife can be accomplished based on accurate beam modelling for homogeneous tumour tissue. More patients and other cancer sites are needed in a further study to validate the proposed method fully.Advances in knowledgeWith accurate beam modelling, the deep learning model can quickly generate the dose distribution for CyberKnife cases. This method accelerates the RT planning process, significantly improves its operational efficiency, and optimizes it.

Project description:Background and objectiveTo develop a real-time recording system based on computer binocular vision and two-dimensional image feature extraction to accurately record mandibular movement in three dimensions.MethodsA computer-based binocular vision device with two digital cameras was used in conjunction with a fixed head retention bracket to track occlusal movement. Software was developed for extracting target spatial coordinates in real time based on two-dimensional image feature recognition. A plaster model of a subject's upper and lower dentition were made using conventional methods. A mandibular occlusal splint was made on the plaster model, and then the occlusal surface was removed. Temporal denture base resin was used to make a 3-cm handle extending outside the mouth connecting the anterior labial surface of the occlusal splint with a detection target with intersecting lines designed for spatial coordinate extraction. The subject's head was firmly fixed in place, and the occlusal splint was fully seated on the mandibular dentition. The subject was then asked to make various mouth movements while the mandibular movement target locus point set was recorded. Comparisons between the coordinate values and the actual values of the 30 intersections on the detection target were then analyzed using paired t-tests.ResultsThe three-dimensional trajectory curve shapes of the mandibular movements were consistent with the respective subject movements. Mean XYZ coordinate values and paired t-test results were as follows: X axis: -0.0037 ± 0.02953, P = 0.502; Y axis: 0.0037 ± 0.05242, P = 0.704; and Z axis: 0.0007 ± 0.06040, P = 0.952. The t-test result showed that the coordinate values of the 30 cross points were considered statistically no significant. (P<0.05).ConclusionsUse of a real-time recording system of three-dimensional mandibular movement based on computer binocular vision and two-dimensional image feature recognition technology produced a recording accuracy of approximately ± 0.1 mm, and is therefore suitable for clinical application. Certainly, further research is necessary to confirm the clinical applications of the method.

Project description:Orthogonality metrics (OMs) for three and higher dimensional separations are proposed as extensions of previously developed OMs, which were used to evaluate the zone utilization of two-dimensional (2D) separations. These OMs include correlation coefficients, dimensionality, information theory metrics and convex-hull metrics. In a number of these cases, lower dimensional subspace metrics exist and can be readily calculated. The metrics are used to interpret previously generated experimental data. The experimental datasets are derived from Gilar's peptide data, now modified to be three dimensional (3D), and a comprehensive 3D chromatogram from Moore and Jorgenson. The Moore and Jorgenson chromatogram, which has 25 identifiable 3D volume elements or peaks, displayed good orthogonality values over all dimensions. However, OMs based on discretization of the 3D space changed substantially with changes in binning parameters. This example highlights the importance in higher dimensions of having an abundant number of retention times as data points, especially for methods that use discretization. The Gilar data, which in a previous study produced 21 2D datasets by the pairing of 7 one-dimensional separations, was reinterpreted to produce 35 3D datasets. These datasets show a number of interesting properties, one of which is that geometric and harmonic means of lower dimensional subspace (i.e., 2D) OMs correlate well with the higher dimensional (i.e., 3D) OMs. The space utilization of the Gilar 3D datasets was ranked using OMs, with the retention times of the datasets having the largest and smallest OMs presented as graphs. A discussion concerning the orthogonality of higher dimensional techniques is given with emphasis on molecular diversity in chromatographic separations. In the information theory work, an inconsistency is found in previous studies of orthogonality using the 2D metric often identified as %O. A new choice of metric is proposed, extended to higher dimensions, characterized by mixes of ordered and random retention times, and applied to the experimental datasets. In 2D, the new metric always equals or exceeds the original one. However, results from both the original and new methods are given.

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Three-dimensional dose prediction based on two-dimensional verification measurements for IMRT.

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