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Register a new userDeep learning for accelerating Monte Carlo radiation transport simulation in intensity-modulated radiation therapy
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Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distribution maps in patient need to be calculated and evaluated for the purpose of killing tumor and protecting healthy tissue. Monte Carlo (MC) radiation transport calculation is able to account for all aspects of radiological physics within 3D heterogeneous media such as the human body and generate the dose distribution maps accurately. However, an MC calculation for doses in radiotherapy usually takes a great mass of time to achieve acceptable statistical uncertainty, impeding the MC methods from wider clinic applications. Here we introduce a convolutional neural network (CNN), termed as Monte Carlo Denoising Net (MCDNet), to achieve the acceleration of the MC dose calculations in radiotherapy, which is trained to directly predict the high-photon (noise-free) dose maps from the low-photon (noise-much) dose maps. Thirty patients with postoperative rectal cancer who accepted intensity-modulated radiation therapy (IMRT) were enrolled in this study. 3D Gamma Index Passing Rate (GIPR) is used to evaluate the performance of predicted dose maps. The experimental results demonstrate that the MCDNet can improve the GIPR of dose maps of 1x107 photons over that of 1x108 photons, yielding over 10x speed-up in terms of photon numbers used in the MC simulations of IMRT. It is of great potential to investigate the performance of this method on the other tumor sites and treatment modalities.
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Cone beam CT (CBCT) has been widely used for patient setup in image guided radiation therapy (IGRT). Radiation dose from CBCT scans has become a clinical concern. The purposes of this study are 1) to commission a GPU-based Monte Carlo (MC) dose calculation package gCTD for Varian On-Board Imaging (OBI) system and test the calculation accuracy, and 2) to quantitatively evaluate CBCT dose from the OBI system in typical IGRT scan protocols. We first conducted dose measurements in a water phantom. X-ray source model parameters used in gCTD are obtained through a commissioning process. gCTD accuracy is demonstrated by comparing calculations with measurements in water and in CTDI phantoms. 25 brain cancer patients are used to study dose in a standard-dose head protocol, and 25 prostate cancer patients are used to study dose in pelvis protocol and pelvis spotlight protocol. Mean dose to each organ is calculated. Mean dose to 2% voxels that have the highest dose is also computed to quantify the maximum dose. It is found that the mean dose value to an organ varies largely among patients. Moreover, dose distribution is highly non-homogeneous inside an organ. The maximum dose is found to be 1~3 times higher than the mean dose depending on the organ, and is up to 8 times higher for the entire body due to the very high dose region in bony structures. High computational efficiency has also been observed in our studies, such that MC dose calculation time is less than 5 min for a typical case.
Purpose: To assess the effects of brain movements induced by heartbeat on dose distributions in synchrotron micro- and mini-beam radiaton therapy and to develop a model to help guide decisions and planning for future clinical trials. Methods: The Monte Carlo code PENELOPE was used to simulate the irradiation of a human head phantom with a variety of micro- and mini-beam arrays, with beams narrower than 100mum and above 500mum, respectively, and with radiation fields of 1cmx2cm and 2cmx2cm. The dose in the phantom due to these beams was calculated by superposing the dose profiles obtained for a single beam of 1mumx2cm. A parameter delta, accounting for the total displacement of the brain during the irradiation and due to the cardio-synchronous pulsation, was used to quantify the impact on peak-to-valley dose ratios and the full-width at half-maximum. Results: The difference between the maximum (at the phantom entrance) and the minimum (at the phantom exit) values of the peak-to-valley dose ratio reduces when the parameter $delta$ increases. The full-width at half-maximum remains almost constant with depth for any $delta$ value. Sudden changes in the two quantities are observed at the interfaces between the various tissues (brain, skull and skin) present in the head phantom. The peak-to-valley dose ratio at the center of the head phantom reduces when delta increases, remaining above 70% of the static value only for mini-beams and delta smaller than ~200mum. Conclusions: Optimal setups for brain treatments with synchrotron radiation micro- and mini-beam combs depend on the brain displacement due to cardio-synchronous pulsation. Peak-to-valley dose ratios larger than 90% of the maximum values obtained in the static case occur only for mini-beams and relatively large dose rates.
The purpose of this study is to develop a deep learning based method that can automatically generate segmentations on cone-beam CT (CBCT) for head and neck online adaptive radiation therapy (ART), where expert-drawn contours in planning CT (pCT) can serve as prior knowledge. Due to lots of artifacts and truncations on CBCT, we propose to utilize a learning based deformable image registration method and contour propagation to get updated contours on CBCT. Our method takes CBCT and pCT as inputs, and output deformation vector field and synthetic CT (sCT) at the same time by jointly training a CycleGAN model and 5-cascaded Voxelmorph model together.The CycleGAN serves to generate sCT from CBCT, while the 5-cascaded Voxelmorph serves to warp pCT to sCTs anatommy. The segmentation results were compared to Elastix, Voxelmorph and 5-cascaded Voxelmorph on 18 structures including left brachial plexus, right brachial plexus, brainstem, oral cavity, middle pharyngeal constrictor, superior pharyngeal constrictor, inferior pharyngeal constrictor, esophagus, nodal gross tumor volume, larynx, mandible, left masseter, right masseter, left parotid gland, right parotid gland, left submandibular gland, right submandibular gland, and spinal cord. Results show that our proposed method can achieve average Dice similarity coefficients and 95% Hausdorff distance of 0.83 and 2.01mm. As compared to other methods, our method has shown better accuracy to Voxelmorph and 5-cascaded Voxelmorph, and comparable accuracy to Elastix but much higher efficiency. The proposed method can rapidly and simultaneously generate sCT with correct CT numbers and propagate contours from pCT to CBCT for online ART re-planning.
Purpose: For Monte Carlo simulation of radiotherapy, x-ray CT number of every system needs to be calibrated and converted to mass density and elemental composition. This study aims to formulate material properties of body tissues for practical two-step conversion from CT number. Methods: We used the latest compilation on body tissues that constitute reference adult male and female. We formulated the relations among mass, electron, and elemental densities into polylines to connect representative tissues, for which we took mass-weighted mean for the tissues in limited density regions. We compared the polyline functions of mass density with a bi-line for electron density and broken lines for elemental densities, which were derived from preceding studies. Results: There was generally high correlation between mass density and the other densities except of C, N, and O for light spongiosa tissues occupying 1% of body mass. The polylines fitted to the dominant tissues and were generally consistent with the bi-line and the broken lines. Conclusions: We have formulated the invariant relations between mass and electron densities and from mass to elemental densities for body tissues. The formulation enables Monte Carlo simulation in treatment planning practice without additional burden with CT-number calibration.
Purpose: Dual-energy CT (DECT) has been used to derive relative stopping power (RSP) map by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could potentially be compromised by image noise levels and the severity of artifacts when using physics-based mapping techniques, which would affect subsequent clinical applications. This work presents a noise-robust learning-based method to predict RSP maps from DECT for proton radiation therapy. Methods: The proposed method uses a residual attention cycle-consistent generative adversarial (CycleGAN) network. CycleGAN were used to let the DECT-to-RSP mapping be close to a one-to-one mapping by introducing an inverse RSP-to-DECT mapping. We retrospectively investigated 20 head-and-neck cancer patients with DECT scans in proton radiation therapy simulation. Ground truth RSP values were assigned by calculation based on chemical compositions, and acted as learning targets in the training process for DECT datasets, and were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. Results: The predicted RSP maps showed an average normalized mean square error (NMSE) of 2.83% across the whole body volume, and average mean error (ME) less than 3% in all volumes of interest (VOIs). With additional simulated noise added in DECT datasets, the proposed method still maintained a comparable performance, while the physics-based stoichiometric method suffered degraded inaccuracy from increased noise level. The average differences in DVH metrics for clinical target volumes (CTVs) were less than 0.2 Gy for D95% and Dmax with no statistical significance. Conclusion: These results strongly indicate the high accuracy of RSP maps predicted by our machine-learning-based method and show its potential feasibility for proton treatment planning and dose calculation.
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