An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray...

Area of Science:

• Medical Imaging
• Computational Imaging
• Biophysics

Background:

• In-line X-ray phase-contrast computed tomography (IL-PCCT) excels at visualizing low-Z materials like soft tissues.
• Current standard reconstruction algorithms, such as filtered back-projection (FBP), demand extensive projection data, leading to high radiation doses that limit clinical use.

Purpose of the Study:

• To develop and evaluate an iterative reconstruction algorithm for IL-PCCT.
• To reduce radiation dose requirements for IL-PCCT while maintaining image quality.
• To improve the efficiency of IL-PCCT image reconstruction.

Main Methods:

• Proposed a novel iterative reconstruction algorithm combining simultaneous algebraic reconstruction technique (SART) with eight-neighbour forward and backward (FAB8) diffusion filtering.
• Validated the algorithm using Shepp-Logan phantom simulations.
• Tested the algorithm on a real synchrotron IL-PCCT experiment.

Main Results:

• The proposed algorithm successfully reconstructed high-quality computed tomography images from limited projection data (few-view projections).
• Demonstrated improved convergence rates in computed tomography reconstruction compared to standard methods.
• Showcased the algorithm's effectiveness in dose reduction for IL-PCCT.

Conclusions:

• The developed iterative algorithm offers a viable solution for dose reduction in IL-PCCT.
• This approach enhances the clinical feasibility of IL-PCCT by enabling high-quality imaging with lower radiation exposure.
• The combination of SART and FAB8 diffusion filtering represents a significant advancement in IL-PCCT reconstruction techniques.