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Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy
- Source :
- Physics in Medicine & Biology. 64:125002
- Publication Year :
- 2019
- Publisher :
- IOP Publishing, 2019.
-
Abstract
- Cone beam computed tomography (CBCT) images can be used for dose calculation in adaptive radiation therapy (ART). The main challenges are the large artefacts and inaccurate Hounsfield unit (HU) values. Currently, deformed planning CT images are often used for this purpose, although anatomical accuracy might be a concern. Ideally, we would like to convert CBCT images to CT images with artifacts removed or greatly reduced and HU values corrected while keeping the anatomical accuracy. Recently, deep learning has achieved great success in image-to-image translation tasks. It is very difficult to acquire paired CT and CBCT images with exactly matching anatomy for supervised training. To overcome this limitation, we developed and tested a cycle generative adversarial network (CycleGAN) which is an unsupervised learning method and does not require paired training datasets to synthesize CT images from CBCT images. The synthesized CT (sCT) images have been compared with the deformed planning CT (dpCT) showing visual and quantitative similarity with artifacts being removed and HU value errors being reduced from 71.78 HU to 27.98 HU. Dose calculation accuracy using sCT images has been improved over the original CBCT images, with the average Gamma Index passing rate increased from 95.4% to 97.4% for 1 mm/1% criteria. A deformable phantom study has been conducted and demonstrated better anatomical accuracy for sCT over dpCT.<br />14 pages, 10 figures, 3 tables
- Subjects :
- Cone beam computed tomography
Dose calculation
Computer science
FOS: Physical sciences
Computed tomography
Imaging phantom
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Hounsfield scale
Image Processing, Computer-Assisted
medicine
Humans
Radiology, Nuclear Medicine and imaging
Computer vision
Radiological and Ultrasound Technology
medicine.diagnostic_test
Phantoms, Imaging
business.industry
Radiotherapy Planning, Computer-Assisted
Radiotherapy Dosage
Cone-Beam Computed Tomography
Physics - Medical Physics
Gamma index
Head and Neck Neoplasms
030220 oncology & carcinogenesis
Unsupervised learning
Medical Physics (physics.med-ph)
Artificial intelligence
business
Algorithms
Adaptive radiation therapy
Radiotherapy, Image-Guided
Subjects
Details
- ISSN :
- 13616560
- Volume :
- 64
- Database :
- OpenAIRE
- Journal :
- Physics in Medicine & Biology
- Accession number :
- edsair.doi.dedup.....ce05fedb3b15bf26829c368082b11d4d