Regularization parameter based on incomplete variables for X-ray luminescence computed tomography.

Area of Science:

• Biomedical imaging
• Medical physics
• Radiological sciences

Background:

• X-ray luminescence computed tomography (XLCT) is an advanced molecular imaging modality with significant potential in biological research.
• Accurate and stable image reconstruction remains a critical challenge in XLCT, hindering its widespread application.

Purpose of the Study:

• To develop and validate a novel regularization parameter selection strategy for improving XLCT reconstruction.
• To enhance the stability and accuracy of XLCT imaging through a data-driven approach.

Main Methods:

• A new regularization parameter selection strategy was developed using an incomplete variables frame.
• The strategy employs residual information derived from Karush-Kuhn-Tucker (KKT) conditions to guide parameter selection.
• Performance was evaluated using both simulated data and phantom experiments.

Main Results:

• The proposed strategy effectively determined regularization parameters, leading to improved recovered XLCT images.
• The residual information incorporated solution norm and gradient norm, enhancing reconstruction quality.
• Both simulation and phantom experiments demonstrated the efficacy of the algorithm.

Conclusions:

• The developed regularization parameter selection strategy offers a robust solution for enhancing XLCT reconstruction accuracy and stability.
• This advancement is expected to accelerate the development and preclinical application of XLCT in fields like FMT.
• Further research may explore clinical translation of this improved imaging technique.