Problem
Research questions and friction points this paper is trying to address.
CBCT-to-CT synthesis
low-dose radiotherapy
image quality
scatter artifacts
dose calculation
Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
🤖 AI Summary
This study addresses the challenge of poor image quality in low-dose cone-beam computed tomography (CBCT), which suffers from scatter, noise, and artifacts that hinder accurate dose calculation and adaptive radiotherapy. To overcome this limitation, the authors propose the first supervised CBCT-to-CT synthesis framework based on a conditional denoising diffusion probabilistic model (DDPM), capable of generating high-fidelity CT-equivalent images. A key innovation lies in the systematic comparison of clinical DICOM images versus FDK-reconstructed images from raw projections as inputs, revealing that the latter significantly enhances CT equivalence. Experimental results demonstrate that the proposed method effectively supports precise patient positioning and accurate dose computation within low-dose radiotherapy workflows.
📝 Abstract
During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resulting in increased cumulative X-ray dose. To mitigate this, low-dose cone-beam CT (CBCT) is routinely acquired during treatment delivery. However, CBCT image quality remains insufficient for accurate dose calculation and adaptive radiotherapy planning due to increased scatter, noise, beam hardening, and reconstruction related artifacts. This study develops a supervised deep learning based CBCT to CT synthesis framework using a conditional denoising diffusion probabilistic model (DDPM), where the generation of a CT-based planning for accurate positioning and dose calculation is obtained using generative models with low dose CBCT imaging. Beyond demonstrating CBCT to CT synthesis, the primary objective is to investigate how the representation of CBCT input data, either standard clinical DICOM CBCT images or filtered back-projection (FDK) reconstructions from raw projection data, affects the performance of diffusion based CT synthesis. The overarching aim is to assess whether physics aware CBCT representations better support CT-equivalent image quality while maintaining reduced imaging dose in radiotherapy workflows.
Innovation
Methods, ideas, or system contributions that make the work stand out.
conditional diffusion model
CBCT-to-CT synthesis
low-dose radiotherapy
input representation
denoising diffusion probabilistic model
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