2023 Fiscal Year Final Research Report
Development of a Deep Learning-based extending the effective field of view in cone-beam CT
| Project/Area Number |
22K15804
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Keywords | 放射線治療 / 医学物理 / AI / CBCT / 適応放射線治療 |
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| Outline of Final Research Achievements |
This study examines the application of cone-beam CT (CBCT) in radiation therapy, which contains up-to-date information about the patient’s body and can be used to recalculate dose distributions for treatment planning and monitoring. However, the limited field of view (FOV) of CBCT results in a lack of body contours, which makes accurate dose calculation difficult. The aim of this study was to extend the CBCT FOV using deep learning to complete sinograms.
Three models were created using the pix2pix deep-learning algorithm: a sinogram-based model and a CT-based model, each using different image pairings.This study indicate that deep learning-based sinogram completion using CT-based modelis a viable method for extending the FOV in CBCT with missing body contours.
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| Free Research Field |
放射線治療
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，pCTを用いた深層学習に基づくサイノグラム補完を行うことで，CBCTの有効視野（FOV）拡張を目指した．サイノグラムを補完するモデルが，画像自体を補完するモデルよりも最大値，中央値，最小値の全てにおいてMAEとRMSEの値が小さく，SSIMの値が大きかった．サイノグラムを補完するモデルが画像自体を補完するモデルよりもFOVを拡張できていた理由は，サイノグラムの情報の連続性が深層学習の学習や予測において有利に働いたからだと考えられる．本研究成果により，狭いFOVを有するCBCTを利用した正確な線量分布の計算が可能になることが期待される．
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