CNN-based region-of-interest image reconstruction from truncated data in cone-beam CT

Kihong Son; Hyoeun Kim; Yurim Jang; Seunghoon Chae; Sooyeul Lee

doi:10.1117/12.2611310

4 April 2022 CNN-based region-of-interest image reconstruction from truncated data in cone-beam CT

Kihong Son, Hyoeun Kim, Yurim Jang, Seunghoon Chae, Sooyeul Lee

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 1203122 (2022) https://doi.org/10.1117/12.2611310
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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

Citation Download Citation

Kihong Son, Hyoeun Kim, Yurim Jang, Seunghoon Chae, and Sooyeul Lee "CNN-based region-of-interest image reconstruction from truncated data in cone-beam CT", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 1203122 (4 April 2022); https://doi.org/10.1117/12.2611310

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