상세정보
학술대회
CNN-based Region-of-Interest Image Reconstruction from Truncated Data in Cone-Beam CT
- 발행일
- 202203
- 출처
- SPIE Medical Imaging 2022 (SPIE 12031), pp.1-6
- 협약과제
- 21IR2800, 3D Navigation 융합형 저선량 C-Arm CT 시스템 개발, 이수열
- 초록
- We developed a region-of-interest (ROI) image reconstruction method that effectively reduces truncation artifacts in CBCT. By using U-Net-based deep learning (DL) methods, we devised a method to reduce truncation artifacts for ROI imaging. A total of 16294 image slices from 49 patient cases were used to generate projection data. The center of the projected image was cropped to a width of 150 mm. Then, the outer part of the truncation image was filled with each outermost pixel value for the initial correction. After the filtering process, the truncation area was cut off and used as input data in the DL model. Finally, inference images were reconstructed by use of the FDK algorithm. SSIM values for the test set of 14 patients were calculated as 0.541, 0.709 and 0.979 for FBP, Extension and the proposed ROI method, respectively. We have achieved promising results and believe that the proposed ROI image reconstruction method can help reduce radiation dose while preserving image quality
- KSP 제안 키워드
- DL model, FDK algorithm, Image quality, Image reconstruction, Pixel Value, Radiation dose, Reconstruction method, Region Of Interest(ROI), Test Set, cone-beam CT, deep learning(DL)