ICNGC 2025 The 11th International Conference on Next Generation Computing 2025 (2025.12)바로가기
페이지
pp.219-222
저자
Maksadbek Khasanov, Youngwoo Kim
언어
영어(ENG)
URL
https://www.earticle.net/Article/A478498
원문정보
초록
영어
Cone-beam computed tomography (CBCT) is essential in adaptive radiation therapy (ART), yet its clinical utility is hindered by high noise levels, artifacts, and degraded textures. This study introduces a deep learning framework based on a Conditional Denoising Diffusion Probabilistic Model (DDPM) to synthesize high-quality CT (sCT) images from CBCT scans. The model incorporates a specialized encoder and Fusion Block for better fusing input and label images and preserve fine anatomical details. Trained on paired CBCT and deformed CT(dCT) pelvic datasets, the proposed method significantly reduces noise and artifacts while enhancing anatomical fidelity. This approach promises to improve CBCT usability in clinical workflows and enhancing ART planning accuracy.
목차
Abstract I. INTRODUCTION II. METHOD A. Overview of Diffusion Processes B. Architecture of the Proposed Model C. Training Strategy D. Sampling Strategy III. EXPERIMENTAL RESULTS A. Dataset and Implementation B. Quantitative Analysis C. Qualitative Analysis IV. CONCLUSION ACKNOWLEDGMENT REFERENCE