Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. (21st October 2019)
- Record Type:
- Journal Article
- Title:
- Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. (21st October 2019)
- Main Title:
- Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning
- Authors:
- Liu, Yingzi
Lei, Yang
Wang, Yinan
Shafai-Erfani, Ghazal
Wang, Tonghe
Tian, Sibo
Patel, Pretesh
Jani, Ashesh B
McDonald, Mark
Curran, Walter J
Liu, Tian
Zhou, Jun
Yang, Xiaofeng - Abstract:
- Abstract: The purpose of this work is to validate the application of a deep learning-based method for pelvic synthetic CT (sCT) generation that can be used for prostate proton beam therapy treatment planning. We propose to integrate dense block minimization into 3D cycle-consistent generative adversarial networks (cycleGAN) framework to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 17 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT generation method by leave-one-out cross-validation. Image quality between the sCT and CT images, gamma analysis passing rate, dose-volume metrics, distal range displacement, and the individual pencil beam Bragg peak shift between sCT- and CT-based proton plans were evaluated. The average mean absolute error (MAE) was 51.32 ± 16.91 HU. The relative differences of the statistics of the PTV dose-volume histogram (DVH) metrics in between sCT and CT were generally less than 1%. Mean values of dose difference, absolute dose difference (in percent of the prescribed dose) were −0.07% ± 0.07% and 0.23% ± 0.08%. Mean gamma analysis pass rate of 1 mm/1%, 2 mm/2%, 3 mm/3% criteria with 10% dose threshold were 92.39% ± 5.97%, 97.95% ± 2.95% and 98.97% ± 1.62% respectively. The median, mean and standard deviation of absolute maximum range differences were 0.09 cm and 0.23 ± 0.25 cm. The median and mean Bragg peak shifts among the 17 patients were 0.09 cm andAbstract: The purpose of this work is to validate the application of a deep learning-based method for pelvic synthetic CT (sCT) generation that can be used for prostate proton beam therapy treatment planning. We propose to integrate dense block minimization into 3D cycle-consistent generative adversarial networks (cycleGAN) framework to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 17 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT generation method by leave-one-out cross-validation. Image quality between the sCT and CT images, gamma analysis passing rate, dose-volume metrics, distal range displacement, and the individual pencil beam Bragg peak shift between sCT- and CT-based proton plans were evaluated. The average mean absolute error (MAE) was 51.32 ± 16.91 HU. The relative differences of the statistics of the PTV dose-volume histogram (DVH) metrics in between sCT and CT were generally less than 1%. Mean values of dose difference, absolute dose difference (in percent of the prescribed dose) were −0.07% ± 0.07% and 0.23% ± 0.08%. Mean gamma analysis pass rate of 1 mm/1%, 2 mm/2%, 3 mm/3% criteria with 10% dose threshold were 92.39% ± 5.97%, 97.95% ± 2.95% and 98.97% ± 1.62% respectively. The median, mean and standard deviation of absolute maximum range differences were 0.09 cm and 0.23 ± 0.25 cm. The median and mean Bragg peak shifts among the 17 patients were 0.09 cm and 0.18 ± 0.07 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for prostate proton radiotherapy. … (more)
- Is Part Of:
- Physics in medicine & biology. Volume 64:Number 20(2019:Oct.)
- Journal:
- Physics in medicine & biology
- Issue:
- Volume 64:Number 20(2019:Oct.)
- Issue Display:
- Volume 64, Issue 20 (2019)
- Year:
- 2019
- Volume:
- 64
- Issue:
- 20
- Issue Sort Value:
- 2019-0064-0020-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-10-21
- Subjects:
- MRI-only treatment planning -- proton therapy -- prostate -- synthetic CT
Biophysics -- Periodicals
Medical physics -- Periodicals
610.153 - Journal URLs:
- http://ioppublishing.org/ ↗
http://iopscience.iop.org/0031-9155 ↗ - DOI:
- 10.1088/1361-6560/ab41af ↗
- Languages:
- English
- ISSNs:
- 0031-9155
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
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