An end-to-end neural network for 4D cardiac CT reconstruction using single-beat scans

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Disease

Background:

• Motion artifacts are a major challenge in cardiac CT, hindering accurate diagnosis.
• Traditional methods like rescans increase radiation and are ineffective for irregular heart rhythms.

Purpose of the Study:

• To develop a deep learning-based framework for motion artifact reduction in cardiac CT using single-beat rapid scanning.
• To improve the accuracy of cardiac disease detection and diagnosis.

Main Methods:

• A deep learning end-to-end reconstruction framework was proposed.
• A sliding-window approach divided projection data into cardiac phase-centered intervals.
• Denoising and registration networks computed deformation vector fields for motion-compensated reconstruction.

Main Results:

• The method effectively reduced motion artifacts and restored anatomical structures.
• Key metrics showed significant improvements: SSIM increased to 0.7795, PSNR to 37.58, and RMSE (HU) decreased to 49.28.
• Segmentation accuracy improved, with Dice scores for the left ventricle, coronary arteries, and calcified plaques reaching 0.9614, 0.8811, and 0.7774, respectively.

Conclusions:

• The proposed deep learning framework shows strong potential for generalizability in clinical applications.
• It enables accurate cardiac CT imaging from single-cycle scans without heart rate restrictions.
• This approach offers a promising solution for overcoming motion artifacts in dynamic cardiac imaging.