SinoSynth: A Physics-Based Domain Randomization Approach for Generalizable CBCT Image Enhancement.

ABSTRACT: Cone Beam Computed Tomography (CBCT) finds diverse applications in medicine. Ensuring high image quality in CBCT scans is essential for accurate diagnosis and treatment delivery. Yet, the susceptibility of CBCT images to noise and artifacts undermines both their usefulness and reliability. Existing methods typically address CBCT artifacts through image-to-image translation approaches. These methods, however, are limited by the artifact types present in the training data, which may not cover the complete spectrum of CBCT degradations stemming from variations in imaging protocols. Gathering additional data to encompass all possible scenarios can often pose a challenge. To address this, we present SinoSynth, a physics-based degradation model that simulates various CBCT-specific artifacts to g

SUBMITTER: Pang Y 

PROVIDER: S-EPMC12711319 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature