A two-stage reconstruction of fluorescence molecular tomography based on sparse regularization

Area of Science:

• Biomedical Imaging
• Molecular Imaging
• Computational Biology

Background:

• Fluorescence molecular tomography (FMT) is a powerful in vivo imaging technique for monitoring cellular and molecular processes.
• Accurate and stable reconstruction of fluorescence signals is critical for reliable FMT but remains a significant challenge.

Purpose of the Study:

• To develop and evaluate a novel two-stage reconstruction algorithm for improving the accuracy and stability of FMT.
• To address the limitations of current FMT reconstruction methods.

Main Methods:

• A two-stage reconstruction algorithm combining sparse regularization and an adaptive finite element method was proposed.
• Two distinct inversion algorithms were applied sequentially on an initial coarse mesh and a subsequently refined mesh.

Main Results:

• Numerical experiments using a digital mouse model were conducted to validate the algorithm.
• The proposed method demonstrated improved stability and computational efficiency for FMT reconstruction.

Conclusions:

• The developed two-stage reconstruction algorithm offers a stable and computationally efficient solution for FMT.
• This approach shows promise for advancing in vivo molecular and cellular imaging applications.