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Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography.

Yu An¹, Hanfan Wang², Jiaqian Li¹

¹the Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, School of Engineering Medicine, Beihang University, Beijing, 100191, China.

Biomedical Optics Express

|May 19, 2023

Summary

A new adaptive group least angle regression elastic net (AGLEN) method improves 3D tumor imaging by accounting for clustered cells. This advanced technique enhances reconstruction accuracy for guiding cancer immunotherapy.

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Area of Science:

Biomedical Imaging
Medical Physics
Computational Biology

Background:

Traditional fluorescence molecular tomography struggles with clustered tumor cells, limiting 3D reconstruction accuracy.
Existing methods using tumor sparsity priors do not account for the spatial structure of tumor cell clusters.

Purpose of the Study:

To develop and validate an improved reconstruction method for fluorescence molecular tomography.
To enhance 3D tumor imaging by integrating local spatial structure and group sparsity.

Main Methods:

Developed the adaptive group least angle regression elastic net (AGLEN) method.
Integrated elastic net regularization with least angle regression, incorporating local spatial correlation and group sparsity.
Employed iterative residual vector and median smoothing for robust optimization.

Main Results:

AGLEN demonstrated superior performance over state-of-the-art methods in numerical simulations and in vivo mouse models (liver and melanoma tumors).
The method showed improved accuracy across various light source configurations and distances, and robustness to Gaussian noise (5-25%).
AGLEN accurately imaged tumor expression of cell death ligand-1, a potential biomarker for immunotherapy response.

Conclusions:

The AGLEN method offers a significant advancement in 3D fluorescence molecular tomography reconstruction.
This technique provides more accurate tumor imaging, crucial for guiding personalized cancer therapies like immunotherapy.