A review of advances in imaging methodology in fluorescence molecular tomography

计算机科学 反问题 图像质量 人工智能 深度学习 维数之咒 正规化(语言学) 迭代重建 质量(理念) 机器学习 医学物理学 图像(数学) 医学 数学 物理 数学分析 量子力学
作者
Peng Zhang,Chenbin Ma,Fan Song,Guangda Fan,Yangyang Sun,Youdan Feng,Xibo Ma,Fei Liu,Guanglei Zhang
出处
期刊:Physics in Medicine and Biology [IOP Publishing]
卷期号:67 (10): 10TR01-10TR01 被引量:43
标识
DOI:10.1088/1361-6560/ac5ce7
摘要

Objective.Fluorescence molecular tomography (FMT) is a promising non-invasive optical molecular imaging technology with strong specificity and sensitivity that has great potential for preclinical and clinical studies in tumor diagnosis, drug development and therapeutic evaluation. However, the strong scattering of photons and insufficient surface measurements make it very challenging to improve the quality of FMT image reconstruction and its practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of high-quality FMT reconstructions.Approach.This review takes a comprehensive overview of advances in imaging methodology for FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the quality of FMT reconstruction are summarized. Notably, deep learning methods are discussed in detail to illustrate their advantages in promoting the imaging performance of FMT thanks to large datasets, the emergence of optimized algorithms and the application of innovative networks.Main results.The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combined with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and deep neural network-based methods, especially end-to-end deep networks, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction.Significance.This review aims to illustrate a variety of effective and practical methods for the reconstruction of FMT images that may benefit future research. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote, to a certain extent, the development of FMT and other methods of optical tomography.
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