PURPOSE:Evaluation of different calculation methods for dose modification due to intrafraction prostate motion using film measurements as ground truth.METHODS:We acquired intrafraction motion data with the Calypso tum...
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PURPOSE:Evaluation of different calculation methods for dose modification due to intrafraction prostate motion using film measurements as ground truth.
METHODS:We acquired intrafraction motion data with the Calypso tumor tracking system by Varian Medical Systems Inc for 4 prostate IMRT patients treated with 35 fractions each. These motion data were transferred to a phantom platform which reproduces the observed motion and has a 20 cm diameter cylindrical solid water phantom mounted. For each patient all fractions were irradiated on one radiochromic MD-V2-55 film placed in the isocentric transversal slice of this phantom. These films serve as ground truth for three calculation Methods: 1) Recalculation of the plan with shifted target point for every segment with the segment's mean Calypso position. 2)+3) Convolution of the static dose distribution with a probabilitydensity function of the observed positions. For 2) only Calypso positions with activated beam on signal were used whereas for 3) all Calypso positions between the first and the last beam on signal for all fractions were employed. The comparisons between films and calculated dose distributions were made with the verification software VeriSoft 3.2 (PTW, Freiburg, Germany) where an 8×8 cm̂2 ROI around the isocenter was selected for gamma evaluation.
RESULTS:The segment shifted plans reach 3%/3mm gamma values above 90% against the films for all four patients. For both convolution methods three values are above 90%, only for the patient with the largest intrafraction motion they decrease to 89%.
CONCLUSIONS:Shifting of the target point for every segment is well suited to estimate the dosimetric consequences of intrafraction prostate motion. This may facilitate the evaluation of different margin sizes or dose prescribing recipes under different motion conditions. If such a lengthy calculation is not possible, a convolution with motion data can be used for acceptable results, too. Our work was partially supporte
Purpose: Cone-beam CT (CBCT) projection images provide anatomical data in real-time over several respiratory cycles, forming a comprehensive picture of tumor movement. We developed a method using these projections to ...
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Purpose: Cone-beam CT (CBCT) projection images provide anatomical data in real-time over several respiratory cycles, forming a comprehensive picture of tumor movement. We developed a method using these projections to determine the trajectory and dose of highly mobile tumors during each fraction of treatment. Methods: CBCT images of a respiration phantom were acquired, where the trajectory mimicked a lung tumor with high amplitude (2.4 cm) and hysteresis. A template-matching algorithm was used to identify the location of a steel BB in each projection. A Gaussian probabilitydensity function for tumor position was calculated which best fit the observed trajectory of the BB in the imager geometry. Two methods to improve the accuracy of tumor track reconstruction were investigated: first, using respiratory phase information to refine the trajectory estimation, and second, using the Monte Carlo method to sample the estimated Gaussian tumor position distribution. 15 clinically-drawn abdominal/lung CTV volumes were used to evaluate the accuracy of the proposed methods by comparing the known and calculated BB trajectories. Results: With all methods, the mean position of the BB was determined with accuracy better than 0.1 mm, and root-mean-square (RMS) trajectory errors were lower than 5% of marker amplitude. Use of respiratory phase information decreased RMS errors by 30%, and decreased the fraction of large errors (>3 mm) by half. Mean dose to the clinical volumes was calculated with an average error of 0.1% and average absolute error of 0.3%. Dosimetric parameters D90/D95 were determined within 0.5% of maximum dose. Monte-Carlo sampling increased RMS trajectory and dosimetric errors slightly, but prevented over-estimation of dose in trajectories with high noise. Conclusions: Tumor trajectory and dose-of-the-day were accurately calculated using CBCT projections. This technique provides a widely-available method to evaluate highly-mobile tumors, and could facilitate better
Purpose: Monoscopic x-ray imaging with on-board kV devices is an attractive approach for real-time image guidance in modern radiation therapy, but it falls short in providing reliable information along the direction o...
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Purpose: Monoscopic x-ray imaging with on-board kV devices is an attractive approach for real-time image guidance in modern radiation therapy, but it falls short in providing reliable information along the direction of imaging x-ray. By effectively taking consideration of projection data at prior times and/or angles through a Bayesian formalism, we develop a nonparametric algorithm for real-time and full 3D tumor localization with a single x-ray imager during treatment ***: First, we construct the a priori probabilitydensity function using the 2D tumor locations on the projection images acquired during patient setup. Whenever an x-ray image is acquired during the treatment delivery, the corresponding 2D tumor location on the imager is used to update the likelihood function. The unresolved third dimension is obtained by maximizing the posterior probability distribution. The algorithm does not involve optimization of any model parameter and therefore can be used in a ‘plug-and-play’ fashion. We validated the algorithm using the 3D tumor motion trajectories of a lung and a pancreas patient reproduced by a physical phantom. Continuous kV images were acquired over a full gantry rotation with the TrueBeam onboard imaging system. Three scenarios were considered: fluoroscopic setup, cone beam CT setup, and retrospective analysis. Results: The 3D localization error is < 1 mm on average and < 1.5 mm at 95 percentile in the lung and pancreas cases for all three scenarios. The difference in 3D localization error for different scenarios is small and is not statistically significant. Conclusions: The proposed algorithm eliminates the need for any population based model parameters in monoscopic image guided RT and allows accurate and real-time 3D tumor localization on current standard Linacs with a single x-ray imager.
Purpose: This study introduces a filtering method that enhances slow/fast diffusion contrast for the q-space analysis, ARTOP ( A pproximated R eturning T o the O rigin P robability), in clinical studies. Background: M...
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Purpose: This study introduces a filtering method that enhances slow/fast diffusion contrast for the q-space analysis, ARTOP ( A pproximated R eturning T o the O rigin P robability), in clinical studies. Background: Most clinical diffusion image analyses are based on the apparent diffusion coefficient (ADC), which uses a Gaussian model for its ensemble probabilitydensity function (PDF). However an ADC-related modality becomes problematic when it is applied to high-diffusion MRI studies (b>3ksec/mm 2 ) due to its complexity in modeling. q-space analysis is model-independent; i.e. the Fourier transformation between data profile in q-space and its displacement PDF has no modeling assumptions. Nevertheless the traditional q-space analysis also poses some difficulties in clinical implementation. Therefore we have been developing a clinical feasible q-space analysis, ARTOP, for high diffusion studies. This study improves ARTOP by increasing the slow-diffusion contrast for better imaging quality, and shortens scanning time as well. Methods and Materials: The contrast of slow/fast diffusion signal is enhanced by a high-pass filter in q-space (b >=1ksec/mm 2 ), which most fast diffusion signals diminish over that weighting range. The effect was applied on research patient datasets. Patient datasets were collected using a Siemens Trio 3T magnet and were processed by offline homemade codes. The 9-level diffusion weighting ranges were 1∼4k sec/mm 2 . Results: The slow/fast contrast was defined by the ratio of slow/fast ARTOP signal. The filtered ARTOP contrast is more than 7 times greater than the one without filtering; i.e. 15 versus 2 for the filtered versus non-filtered data. Conclusion: The better imaging quality of filtered ARTOP is suitable for radiological examination or treatment planning contour, and its quantitative information can be easily retrieved from non-filtered ARTOP map. The quantity can be used for white matter diseases, e.g. for monitoring the glioma treat
Purpose: To create a deformable organ variation probabilitydensity function (PDF) estimator for the purpose of treatment dose estimation and adaptive inverse planning optimization in the clinical applications of adap...
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Purpose: To create a deformable organ variation probabilitydensity function (PDF) estimator for the purpose of treatment dose estimation and adaptive inverse planning optimization in the clinical applications of adaptive ***: Principle Component Analysis (PCA) was applied to variations of organs of interest manifested on multiple treatment CBCT images. Utilizing the PCA model for deformable organ PDF estimation includes the determination of the eigenvectors and the corresponding coefficients. The coefficients can be either random variables or random functions of treatment time depending on the characteristics of organ deformation as stationary or non-stationary random process. The least square regression method with time-varying weighting parameters was applied on the pre-collected patient images to determine the function form of the coefficients. Seven h&n cancer patients, 31 images per patient, were included in the construction of the estimator and the optimization of the corresponding weighting factor in the estimator. The estimator was evaluated using total 19 organs of interest of another eight patients. Results: The deformable variation of organ can be accurately represented by 3 to 4 eigenvectors. These vectors change and need to be updated with the new image observations during the treatment course. The estimation error in the mean of the organ PDF is less than 3 mm for cord and CTV for elective nodes, and less than 2 mm for the other organs of interest. The estimation error in the standard deviation of the organ PDF is less than 1 mm for all organs of interest. Conclusions: Deformable organ variation estimator is feasible to perform acceptable estimation for deformable organ PDF during the h&n cancer radiation treatment. This estimator will provide an important role in the treatment course of adaptive radiotherapy. This study is partially supported by the research grant from Elekta Oncology System, Inc.
Purpose: MRI significantly improves the accuracy and reliability of target delineation for patient simulation and treatment planning in radiation therapy, due to its superior soft tissue contrast as compared to CT. An...
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Purpose: MRI significantly improves the accuracy and reliability of target delineation for patient simulation and treatment planning in radiation therapy, due to its superior soft tissue contrast as compared to CT. An MRI based simulation will reduce cost and simplify clinical workflow with zero ionizing radiation. However, MRI lacks the key electron density information. The purpose of this work is to develop a reliable method to derive electron density from MRI. Methods: We adopt a probabilistic Bayesian approach for electron density mapping based on T1-weighted head MRI. For each voxel, we compute conditional probability of electron densities given its: (1) T1 intensity and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of test patient and atlas. Intensity and geometry information are combined into a unifying posterior probabilitydensity function whose mean gives the electron density. Mean absolute HU error between the estimated and true CT, as well as ROC's for bone detection (HU>200) were calculated for 8 patients. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). Results: The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 132, compared with 139 for deformable registration (p=10 −3 ), 371 for the intensity approach (p=10 −5 ) and 282 without density correction (p=2×10 −4 ). For 90% sensitivity in bone detection, the proposed method had a specificity of 85% and that for deformable registration, intensity and without density correction are 80%, 24% and 10% respectively. Conclusion: The proposed unifying method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous regions. This paves the way for accurate dose calculation and generating reference images for patient setup in MRI-based treatment planning.
Partial breast irradiation (PBI) following breast-conserving surgery is emerging as an effective means to achieve local control and reduce irradiated breast volume. Patients are planned on a static CT image; however, ...
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Partial breast irradiation (PBI) following breast-conserving surgery is emerging as an effective means to achieve local control and reduce irradiated breast volume. Patients are planned on a static CT image; however, treatment is delivered while the patient is free-breathing. Respiratory motion can degrade plan quality by reducing target coverage and/or dose homogeneity. A variety of methods can be used to determine the required margin for respiratory motion in PBI. We derive geometric and dosimetric respiratory 1D margin. We also verify the adequacy of the typical 5 mm respiratory margin in 3D by evaluating plan quality for increasing respiratory amplitudes (2–20 mm). Ten PBI plans were used for dosimetric evaluation. A database of volunteer respiratory data, with similar characteristics to breast cancer patients, was used for this study. We derived a geometric 95%-margin of 3 mm from the population respiratory data. We derived a dosimetric 95%-margin of 2 mm by convolving 1D dose profiles with respiratory probability density functions. The 5 mm respiratory margin is possibly too large when 1D coverage is assessed and could lead to unnecessary normal tissue irradiation. Assessing margins only for coverage may be insufficient; 3D dosimetric assessment revealed degradation in dose homogeneity is the limiting factor, not target coverage. Hotspots increased even for the smallest respiratory amplitudes, while target coverage only degraded at amplitudes greater than 10 mm. The 5 mm respiratory margin is adequate for coverage, but due to plan quality degradation, respiratory management is recommended for patients with respiratory amplitudes greater than 10 mm.
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