X-ray luminescence computed tomography imaging based on X-ray distribution model and adaptively split Bregman method.

Area of Science:

• Biomedical Imaging
• Medical Physics
• Nanotechnology

Background:

• X-ray luminescence computed tomography (XLCT) is a promising imaging technology utilizing phosphor nanoparticles for biological applications.
• Existing XLCT systems include pencil beam, narrow beam, and cone beam configurations, with narrow beam XLCT offering a balance between efficiency and image quality.
• Traditional narrow beam XLCT assumes parallel X-ray beams, which may not accurately reflect real-world experimental setups.

Purpose of the Study:

• To develop an improved narrow beam XLCT system that accounts for the actual fan-shaped broadening of X-ray beams.
• To enhance imaging efficiency by collecting optical data from only two perpendicular directions.
• To improve image reconstruction accuracy using a novel depth-related adaptive regularized split Bregman (DARSB) method.

Main Methods:

• Incorporated the distribution of fan-shaped X-ray beams into the physical model for XLCT.
• Collected optical data from two perpendicular directions to accelerate scanning time.
• Developed and applied a depth-related adaptive regularized split Bregman (DARSB) method for image reconstruction.

Main Results:

• Simulation experiments demonstrated superior performance of the proposed model and DARSB method compared to traditional split Bregman methods, showing improvements in location error, dice coefficient, mean square error, and intensity error.
• Phantom experiments achieved a location error of less than 1.1 mm.
• Validation that incorporating fan-shaped X-ray beams in the model yields better results than assuming parallel X-rays.

Conclusions:

• The proposed physical model and DARSB reconstruction method significantly improve the accuracy and efficiency of narrow beam XLCT.
• Accounting for fan-shaped X-ray beams is crucial for accurate XLCT imaging.
• The developed XLCT system is feasible and effective for biological imaging applications.