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Preliminary Study for Super-Resolution of Dose Calculation Grid Size Using a Deep Learning in VMAT Prostate Plan

D Shin1*, K Kim1, S Kang1, S Kang2, T Kim3, J Chung2, T Suh1, (1) The Catholic University of Korea, Seoul, KR, (2) Seoul National University Bundang Hospital, Seongnam, 41, KR, (3) Proton Therapy Center, National Cancer Center, Goyang-si, Gyeonggi-do, KR


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Dose grid size is one of factors that affect accuracy of dose calculation. Although use of small grid (less than 2 mm) can improve the accuracy, it is not typically used in clinic due to hard computation. The purpose of this study is to propose a deep learning strategy to predict the dose of small grid from those of large grid with less time.

Our deep learning architecture consisted of two networks: (1) feature-learning and (2) super-resolution networks. Each network was independently trained using 2D slice-by-slice manner. Doses of 1- and 3-mm grid for 20 patients (training: 16, test: 4), which were calculated by VMAT prostate plan (prescription: 78 Gy) and AXB algorithm, were used. The doses of 1-mm were downsampled to 3-mm to organize two training data pairs: (1) dose of 3-mm/downsampled dose and (2) downsampled dose/dose of 1-mm. The first and second pairs were used to train the feature-learning and super-resolution networks, respectively. The trained networks were connected by using output of the feature-learning network as input of the super-resolution network. Predicted doses by the network were compared with doses of 1-mm using dose-volume histogram (DVH) and dice similarity coefficient (DSC).

The DVH of planning-target-volume (PTV) for the prediction were visually more similar to those for dose of 1-mm than 3-mm grid. Mean/maximum doses in PTV for the prediction were similar to those for 1- and 3-mm. Average minimum dose differences were 1.9±0.4% of the prescription (prediction vs. 1-mm) and 7.7±7.4% (3-mm vs. 1-mm). The DSC between the prediction and doses of 1-mm was more close to 1 compared to those between 3-mm and 1-mm.

Proposed method predicted dose of small grid from those of large grid with less time. The predicted doses were comparable to calculated dose with small grid.

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Funding Support, Disclosures, and Conflict of Interest: Funding: This research was supported by Mid-career Researcher Program (No. 2018R1A2B2005343) through the National Research Foundation of Korea funded by the Ministry of Science and ICT. Disclosures and Conflict of Interest: The authors have no conflicts of interest.


Dose, Grids


TH- External Beam- Photons: Development (new technology and techniques)

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