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Related Experiment Video

Updated: Sep 14, 2025

A Bright NIR-II Fluorescence Probe for Vascular and Tumor Imaging

A Bright NIR-II Fluorescence Probe for Vascular and Tumor Imaging

Published on: March 17, 2023

Deep system prior based graph convolution network for NIR-II fluorescence molecular tomography.

Beilei Wang¹, Shuangchen Li¹, Heng Zhang¹

¹School of Information Sciences and Technology, Northwest University, Xi'an, 710127, People's Republic of China; The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, People's Republic of China.

Computer Methods and Programs in Biomedicine

|July 20, 2025

Summary

This summary is machine-generated.

This study introduces a deep system prior based graph convolution network (DSPGN) for fluorescence molecular tomography (FMT). DSPGN improves tumor location accuracy and shape recovery in the second near-infrared window (NIR-II) imaging.

Keywords:

Deep learning Fluorescence molecular tomography Graph convolution network The second Near infrared window

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Published on: July 17, 2012

Area of Science:

Biomedical Imaging
Medical Physics
Computational Imaging

Background:

Fluorescence molecular tomography (FMT) is valuable for early-stage tumor detection.
Severe photon scattering in FMT leads to ill-posed inverse problems, hindering accuracy and morphological reconstruction.
Current FMT methods struggle to meet practical requirements for precise tumor imaging.

Purpose of the Study:

To enhance the efficiency and accuracy of FMT.
To improve the morphological performance of FMT reconstruction.
To address the ill-posed nature of FMT caused by photon scattering.

Main Methods:

Utilized second near-infrared (NIR-II) fluorescence imaging to reduce tissue scattering.
Developed a deep system prior based graph convolution network (DSPGN) for FMT.
Incorporated system spatial priors and graph structures into the reconstruction process.

Main Results:

DSPGN demonstrated superior performance in location accuracy compared to existing methods.
The proposed method significantly improved shape recovery capability in FMT.
Both numerical simulations and in vivo experiments validated DSPGN's effectiveness.

Conclusions:

DSPGN effectively recovers both location and morphology of fluorescence sources.
The method shows potential for advancing FMT applications in NIR-II imaging.
DSPGN offers a promising solution for overcoming FMT's inherent challenges.