Robust transformed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mi>l</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> metric for fluorescence molecular tomography

Yuan, Ye; Yi, Huangjian; Kang, Dizhen; Yu, Jingjing; Guo, Hongbo; He, Xuelei; He, Xiaowei

doi:10.1016/j.cmpb.2023.107503

Computer Methods and Programs in Biomedicine

2023

DOI: 10.1016/j.cmpb.2023.107503

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Robust transformed l1 metric for fluorescence molecular tomography

Ye Yuan

Huangjian Yi

Dizhen Kang

et al.

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Cited by 5 publications

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Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

Wei,

Guo,

Zhao

et al. 2024

Journal of Biophotonics

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Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide‐and‐conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

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Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

Wei,

Guo,

Zhao

et al. 2024

Journal of Biophotonics

Self Cite

View full text Add to dashboard Cite

show abstract

ROMP-DCP: Dictionary learning via regularized orthogonal matching pursuit and difference of convex programming for robust fluorescence molecular tomography

Su,

Gao,

Chen

et al. 2025

Biomedical Signal Processing and Control

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Deep learning on medical image analysis

Wang,

Zhang

2024

CAAI Trans on Intel Tech

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Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features and patterns from extensive datasets. The paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre‐trained models. The paper also discusses how transfer learning has been applied to different areas within medical image analysis. This comprehensive overview aims to assist researchers, clinicians, and policymakers by providing detailed insights, helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.

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Robust transformed l1 metric for fluorescence molecular tomography

Cited by 5 publications

References 40 publications

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor

ROMP-DCP: Dictionary learning via regularized orthogonal matching pursuit and difference of convex programming for robust fluorescence molecular tomography

Deep learning on medical image analysis

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