A novel reconstruction algorithm to extend the CT scan field-of-view

Area of Science:

• Medical Imaging
• Image Reconstruction
• Computed Tomography

Background:

• Projection data truncation in computed tomography (CT) scanners leads to significant imaging artifacts.
• Artifacts from truncated data reduce overall image quality and diagnostic accuracy.
• Accurate reconstruction requires addressing data gaps outside the scan field-of-view (SFOV).

Purpose of the Study:

• To develop and validate a novel CT reconstruction algorithm for estimating truncated projection data.
• To mitigate image artifacts caused by portions of the object lying outside the SFOV.
• To improve the accuracy and robustness of CT image reconstruction in the presence of truncation.

Main Methods:

• Proposed a reconstruction algorithm utilizing the constant total attenuation property in parallel sampling CT.
• Estimated missing projection data by fitting water cylinders to projection samples at truncation boundaries.
• Incorporated continuity constraints on fitting parameters to enhance algorithm robustness.

Main Results:

• The algorithm effectively estimated projection data outside the SFOV.
• Demonstrated significant reduction in truncation-induced artifacts.
• Validated accuracy and robustness through extensive phantom and patient experiments.

Conclusions:

• The proposed algorithm provides an adequate estimation of missing projection data.
• Successfully addresses the challenge of projection truncation in CT imaging.
• Offers a robust solution for improving CT image quality in clinical and research settings.