X-ray luminescence computed tomography using a hybrid proton propagation model and Lasso-LSQR algorithm

Area of Science:

• Biomedical Imaging
• Medical Physics
• Optical Engineering

Background:

• X-ray luminescence computed tomography (XLCT) is a promising molecular imaging technique.
• Accurate optical propagation models and reconstruction methods are crucial for XLCT's performance.
• Existing models like Diffusion Equation (DE) and Simplified Spherical Harmonics (SP3) have limitations.

Purpose of the Study:

• To develop an improved optical propagation model for XLCT.
• To enhance the accuracy and efficiency of luminescence tomographic reconstruction.
• To address the limitations of single propagation models in XLCT.

Main Methods:

• A hybrid DE-SP3 model was developed by classifying tissues based on light propagation characteristics (DE-fitted vs. SP3-fitted).
• The Lasso-LSQR algorithm was integrated for fast iteration and sparse reconstruction.
• Simulations and in vivo experiments were conducted to evaluate the model's performance.

Main Results:

• The hybrid DE-SP3 model accurately describes light transport in various tissues.
• XLCT reconstruction using the hybrid model showed improved location accuracy and spatial resolution compared to the DE model.
• The hybrid model offered reduced computational cost compared to the SP3 model.

Conclusions:

• The hybrid DE-SP3 model offers a balance between accuracy and efficiency for XLCT.
• This approach advances XLCT's potential for molecular imaging with enhanced performance.
• The developed model improves tomographic mapping of luminescence distribution in XLCT.