2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Fluence-map generation for prostate intensity-modulated radiotherapy planning using a deep-neural-network

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A deep-neural-network (DNN) was successfully used to predict clinically-acceptable dose distributions from organ contours for intensity-modulated radiotherapy (IMRT). To provide the next step in the DNN-based plan automation, we propose a DNN that directly generates beam fluence maps from the organ contours and volumetric dose distributions, without inverse planning. We collected 240 prostate IMRT plans and used to train a DNN using organ contours and dose distributions. After training was done, we made 45 synthetic plans (SPs) using the generated fluence-maps and compared them with clinical plans (CP) using various plan quality metrics including homogeneity and conformity indices for the target and dose constraints for organs at risk, including rectum, bladder, and bowel. The network was able to generate fluence maps with small errors. The qualities of the SPs were comparable to the corresponding CPs. The homogeneity index of the target was slightly worse in the SPs, but there was no difference in conformity index of the target, V 60Gy of rectum, the V 60Gy of bladder and the V 45Gy of bowel. The time taken for generating fluence maps and qualities of SPs demonstrated the proposed method will improve efficiency of the treatment planning and help maintain the quality of plans.

          Related collections

          Most cited references 18

          • Record: found
          • Abstract: not found
          • Article: not found

          Loss Functions for Image Restoration With Neural Networks

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            MR-based synthetic CT generation using a deep convolutional neural network method.

             Xiao Han (2017)
            Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Predicting dose-volume histograms for organs-at-risk in IMRT planning.

              The objective of this work was to develop a quality control (QC) tool to reduce intensity modulated radiotherapy (IMRT) planning variability and improve treatment plan quality using mathematical models that predict achievable organ-at-risk (OAR) dose-volume histograms (DVHs) based on individual patient anatomy. A mathematical framework to predict achievable OAR DVHs was derived based on the correlation of expected dose to the minimum distance from a voxel to the PTV surface. OAR voxels sharing a range of minimum distances were computed as subvolumes. A three-parameter, skew-normal probability distribution was used to fit subvolume dose distributions, and DVH prediction models were developed by fitting the evolution of the skew-normal parameters as a function of distance with polynomials. Cohorts of 20 prostate and 24 head-and-neck IMRT plans with identical clinical objectives were used to train organ-specific average models for rectum, bladder, and parotids. A sum of residuals analysis quantifying the integrated difference between the clinically approved DVH and predicted DVH evaluated similarity between DVHs. The ability of the average models to prospectively predict DVHs was evaluated on an independent validation cohort of 20 prostate plans. Statistical comparison of the sums of residuals between training and validation cohorts quantified the accuracy of the average model. Restricted sums of residuals (RSR) were used to identify potential outliers, where large values of RSR indicate a clinical DVH that exceeds the predicted DVH by a considerable amount. A refined model was obtained for each organ by excluding outliers with large RSR values from the training cohort. The refined model was applied to the original training cohort and restricted sums of residuals were utilized to estimate potential DVH improvements. All cases were replanned and evaluated by the physician that approved the original plan. The ability of the refined models to correctly identify outliers was assessed using the residual sum between the original and replanned DVHs to quantify dosimetric gains realized under replanning. Statistical analysis of average sum of residuals for rectum (SR(rectum)=0.003±0.037), bladder (SR(bladder)=-0.008±0.037), and parotid (SR(parotid)=-0.003±0.060) training cohorts yielded mean values near zero and small with respect to the standard deviations, indicating that the average models are capturing the essential behavior of the training cohorts. The predictive abilities of the average rectum and bladder models were statistically indistinguishable between the training and validation sets, with SR(rectum)=0.002±0.044 and SR(bladder)=-0.018±0.058 for the validation set. The refined models' ability to detect outliers and predict achievable OAR DVHs was demonstrated by a strong correlation between predicted gains (RSR) and realized gains after replanning with sample correlation coefficients of r = 0.92 for the rectum, r = 0.88 for the bladder, and r = 0.84 for the parotid glands. The results demonstrate that our mathematical framework and modest training cohorts successfully predict achievable OAR DVHs based on individual patient anatomy. The models correctly identified suboptimal plans that demonstrated further OAR sparing after replanning. This modeling technique requires no manual intervention except for appropriate selection of a training set with identical evaluation criteria. Clinical implementation is in progress to evaluate impact on real-time IMRT QC.
                Bookmark

                Author and article information

                Contributors
                cho.byungchul@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 October 2019
                30 October 2019
                2019
                : 9
                Affiliations
                [1 ]ISNI 0000 0001 2292 0500, GRID grid.37172.30, Department of Nuclear and Quantum Engineering, , Korea Advanced Institute of Science and Technology, ; Daejeon, South Korea
                [2 ]ISNI 0000 0004 0533 4667, GRID grid.267370.7, Department of Radiation Oncology, Asan Medical Center, , University of Ulsan College of Medicine, ; Seoul, South Korea
                Article
                52262
                10.1038/s41598-019-52262-x
                6821767
                31666647
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003725, National Research Foundation of Korea (NRF);
                Award ID: 2013M2A2A7043506
                Award ID: 2013M2A2A7043506
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized

                prostate cancer, radiotherapy

                Comments

                Comment on this article