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
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