SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement
This addresses the challenge of improving CBCT image quality for medical diagnosis and treatment by providing a scalable method to enhance model robustness across diverse imaging protocols.
The paper tackled the problem of limited generalizability in CBCT image enhancement due to insufficient training data by proposing SinoSynth, a physics-based domain randomization approach that generates synthetic CBCT images, resulting in generative networks trained on this data outperforming those trained on actual data on multi-institutional datasets.
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 generate a diverse set of synthetic CBCT images from high-quality CT images without requiring pre-aligned data. Through extensive experiments, we demonstrate that several different generative networks trained on our synthesized data achieve remarkable results on heterogeneous multi-institutional datasets, outperforming even the same networks trained on actual data. We further show that our degradation model conveniently provides an avenue to enforce anatomical constraints in conditional generative models, yielding high-quality and structure-preserving synthetic CT images.