International audiencePurpose:This work aims at investigating intensity corrected cone-beam x-ray computed tomography (CBCT) images for accurate dose calculation in adaptive intensity modulated proton therapy (IMPT) for prostate and head and neck (H&N) cancer. A deformable image registration (DIR)-based method and a scatter correction approach using the image data obtained from DIR as prior are characterized and compared on the basis of the same clinical patient cohort for the first time.Methods:Planning CT (pCT) and daily CBCT data (reconstructed images and measured projections) of four H&N and four prostate cancer patients have been considered in this study. A previously validated Morphons algorithm was used for DIR of the planning CT to the current CBCT image, yielding a so-called virtual CT (vCT). For the first time, this approach was translated from H&N to prostate cancer cases in the scope of proton therapy. The warped pCT images were also used as prior for scatter correction of the CBCT projections for both tumor sites. Single field uniform dose and IMPT (only for H&N cases) treatment plans have been generated with a research version of a commercial planning system. Dose calculations on vCT and scatter corrected CBCT (CBCT cor) were compared by means of the proton range and a gamma-index analysis. For the H&N cases, an additional diagnostic replanning CT (rpCT) acquired within three days of the CBCT served as additional reference. For the prostate patients, a comprehensive contour comparison of CBCT and vCT, using a trained physician’s delineation, was performed.Results:A high agreement of vCT and CBCT cor was found in terms of the proton range and gamma-index analysis. For all patients and indications between 95% and 100% of the proton dose profiles in beam’s eye view showed a range agreement of better than 3 mm. The pass rate in a (2%,2 mm) gamma-comparison was between 96% and 100%. For H&N patients, an equivalent agreement of vCT and CBCT cor to the reference rpCT was observed. However, for the prostate cases, an insufficient accuracy of the vCT contours retrieved from DIR was found, while the CBCT cor contours showed very high agreement to the contours delineated on the raw CBCT.Conclusions:For H&N patients, no considerable differences of vCT and CBCT cor were found. For prostate cases, despite the high dosimetric agreement, the DIR yields incorrect contours, probably due to the more pronounced anatomical changes in the abdomen and the reduced soft-tissue contrast in the CBCT. Using the vCT as prior, these inaccuracies can be overcome and images suitable for accurate delineation and dose calculation in CBCT-based adaptive IMPT can be retrieved from scatter correction of the CBCT projections
Intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) are relatively complex treatment delivery techniques and require quality assurance (QA) procedures. Pre-treatment dosimetric verification represents a fundamental QA procedure in daily clinical routine in radiation therapy. The purpose of this study is to develop an EPID-based approach to reconstruct a 3D dose distribution as imparted to a virtual cylindrical water phantom to be used for plan-specific pre-treatment dosimetric verification for IMRT and VMAT plans. For each depth, the planar 2D dose distributions acquired in air were back-projected and convolved by depth-specific scatter and attenuation kernels. The kernels were obtained by making use of scatter and attenuation models to iteratively estimate the parameters from a set of reference measurements. The derived parameters served as a look-up table for reconstruction of arbitrary measurements. The summation of the reconstructed 3D dose distributions resulted in the integrated 3D dose distribution of the treatment delivery. The accuracy of the proposed approach was validated in clinical IMRT and VMAT plans by means of gamma evaluation, comparing the reconstructed 3D dose distributions with Octavius measurement. The comparison was carried out using (3%, 3 mm) criteria scoring 99% and 96% passing rates for IMRT and VMAT, respectively. An accuracy comparable to the one of the commercial device for 3D volumetric dosimetry was demonstrated. In addition, five IMRT and five VMAT were validated against the 3D dose calculation performed by the TPS in a water phantom using the same passing rate criteria. The median passing rates within the ten treatment plans was 97.3%, whereas the lowest was 95%. Besides, the reconstructed 3D distribution is obtained without predictions relying on forward dose calculation and without external phantom or dosimetric devices. Thus, the approach provides a fully automated, fast and easy QA procedure for plan-specific pre-treatment dosimetric verification.
Introduction: The recent developments of magnetic resonance (MR) based adaptive strategies for photon and, potentially for proton therapy, require a fast and reliable conversion of MR images to X-ray computed tomography (CT) values. CT values are needed for photon and proton dose calculation. The improvement of conversion results employing a 3D deep learning approach is evaluated. Material and methods: A database of 89 T1-weighted MR head scans with about 100 slices each, including rigidly registered CTs, was created. Twenty-eight validation patients were randomly sampled, and four patients were selected for application. The remaining patients were used to train a 2D and a 3D U-shaped convolutional neural network (Unet). A stack size of 32 slices was used for 3D training. For all application cases, volumetric modulated arc therapy photon and single-field uniform dose pencil-beam scanning proton plans at four different gantry angles were optimized for a generic target on the CT and recalculated on 2D and 3D Unet-based pseudoCTs. Mean (absolute) error (MAE/ME) and a gradient sharpness estimate were used to quantify the image quality. Three-dimensional gamma and dose difference analyses were performed for photon (gamma criteria: 1%, 1 mm) and proton dose distributions (gamma criteria: 2%, 2 mm). Range (80% fall off) differences for beam's eye view profiles were evaluated for protons. Results: Training 36 h for 1000 epochs in 3D (6 h for 200 epochs in 2D) yielded a maximum MAE of 147 HU (135 HU) for the application patients. Except for one patient gamma pass rates for photon and proton dose distributions were above 96% for both Unets. Slice discontinuities were reduced for 3D training at the cost of sharpness. Conclusions: Image analysis revealed a slight advantage of 2D Unets compared to 3D Unets. Similar dose calculation performance was reached for the 2D and 3D network.
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