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Segmentation-Guided Accelerating Diffusion Model for Cardiac CT Motion Artifact Reduction via Limited-Angle Imaging
Insights
A new Segmentation-Guided Accelerating Diffusion Model (SGADM) reconstructs high-quality coronary CT angiography (CCTA) images. This method minimizes motion and wedge artifacts from limited-angle CT (LA-CT) scans, improving cardiac imaging diagnostics.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Cardiovascular Diagnostics
Background:
- Coronary computed tomography angiography (CCTA) is crucial for diagnosing cardiac disease but suffers from motion artifacts at high heart rates.
- Limited-angle CT (LA-CT) reduces acquisition time and motion artifacts but introduces severe wedge artifacts.
- Existing diffusion models for medical imaging have high computational costs, limiting clinical use.
Purpose of the Study:
- To develop a novel method for reconstructing motion-free cardiac CT images from LA-CT data.
- To suppress severe wedge artifacts inherent in LA-CT reconstruction.
- To improve the clinical applicability of diffusion models in cardiac imaging.
Main Methods:
- Proposed a Segmentation-Guided Accelerating Diffusion Model (SGADM) for LA-CT imaging.
- SGADM employs an innovative diffusion model for direct, high-quality CT image generation with reduced sampling steps (<10).
- Integrated diffusion perceptual loss for data distribution consistency and segmentation guidance for enhanced coronary artery accuracy.
Main Results:
- SGADM effectively reconstructs high-quality CCTA images with minimal motion artifacts.
- The model successfully suppresses wedge artifacts from LA-CT data.
- Quantitative and qualitative evaluations on simulated and real datasets confirmed SGADM's efficacy.
Conclusions:
- SGADM offers a computationally efficient and effective solution for motion- and artifact-free cardiac CT image reconstruction.
- The method significantly enhances the diagnostic quality of CCTA, especially in challenging cases (arrhythmias, high heart rates).
- SGADM shows strong potential for clinical translation in cardiovascular imaging.
Abstract:
Coronary computed tomography angiography (CCTA) is a pivotal non-invasive imaging modality for diagnosing cardiac disease. However, due to the temporal resolution limitations, cardiac structures, specifically coronary arteries, may suffer from motion artifacts when CCTA is applied to patients with arrhythmias or high heart rates. Limited-angle CT (LA-CT) emerges as a promising alternative by significantly reducing the acquisition time, thereby mitigating the motion artifacts. Yet, LA-CT unavoidably leads to severe wedge artifacts, posing a significant challenge. Therefore, to reconstruct motion-free cardiac CT images while suppressing the wedge artifacts, we propose a Segmentation-Guided Accelerating Diffusion Model (SGADM) tailored for LA-CT imaging. While diffusion models have demonstrated exceptional performance in medical imaging, their extensive sampling procedures impose high computational costs, hindering clinical applicability. To address this issue, SGADM employs an innovative diffusion model that directly generates high-quality CT images. Moreover, SGADM adopts the diffusion perceptual loss to ensure data distribution consistency between two successive sampling steps. As a result, SGADM can provide satisfactory results in fewer than 10 steps. Additionally, SGADM incorporates segmentation guidance to enhance the spatial-positional accuracy of generated coronary arteries explicitly. Both quantitative and qualitative evaluations on simulated and real datasets reveal that the SGADM effectively restores high-quality CCTA images with minimal motion artifacts, highlighting its potential for clinical applications.
