Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography.

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Radiology

Background:

• Cone-beam X-ray luminescence computed tomography (CB-XLCT) is a hybrid imaging technique for early tumor detection.
• Severe ill-posedness remains a significant challenge in CB-XLCT, limiting its diagnostic accuracy.

Purpose of the Study:

• To develop and evaluate an adaptive shrinking reconstruction framework for CB-XLCT.
• To overcome the ill-posedness challenge and improve imaging quality in CB-XLCT.

Main Methods:

• Proposed an adaptive shrinking reconstruction framework that does not require prior information.
• Implemented an automatic mesh node selection process to identify regions contributing to target distribution.
• Designed an adaptive shrinking function to control the source region dynamically at multiple scales.

Main Results:

• The proposed framework demonstrated significant improvements in CB-XLCT imaging quality.
• Validation was performed using both 3D digital mouse models and in vivo experiments.
• The adaptive shrinking approach effectively addressed the ill-posedness inherent in CB-XLCT.

Conclusions:

• The novel adaptive shrinking reconstruction framework offers a promising solution for enhancing CB-XLCT imaging.
• This advancement has the potential to improve early detection of small tumors in vivo.
• The method provides a robust approach to mitigate ill-posedness in hybrid tomographic imaging.