Univ. Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621 Lyon, France.
Department of Information Engineering, University of Florence, 50139 Florence, Italy.
Sensors (Basel). 2021 May 12;21(10):3366. doi: 10.3390/s21103366.
Multispectral photoacoustic imaging is a powerful noninvasive medical imaging technique that provides access to functional information. In this study, a set of methods is proposed and validated, with experimental multispectral photoacoustic images used to estimate the concentration of chromophores. The unmixing techniques used in this paper consist of two steps: (1) automatic extraction of the reference spectrum of each pure chromophore; and (2) abundance calculation of each pure chromophore from the estimated reference spectra. The compared strategies bring positivity and sum-to-one constraints, from the hyperspectral remote sensing field to multispectral photoacoustic, to evaluate chromophore concentration. Particularly, the study extracts the endmembers and compares the algorithms from the hyperspectral remote sensing domain and a dedicated algorithm for segmentation of multispectral photoacoustic data to this end. First, these strategies are tested with dilution and mixing of chromophores on colored 4% agar phantom data. Then, some preliminary in vivo experiments are performed. These consist of estimations of the oxygen saturation rate (sO2) in mouse tumors. This article proposes then a proof-of-concept of the interest to bring hyperspectral remote sensing algorithms to multispectral photoacoustic imaging for the estimation of chromophore concentration.
多光谱光声成像是一种强大的无创医学成像技术,可提供功能信息。在本研究中,提出并验证了一组方法,使用实验多光谱光声图像估计色团的浓度。本文使用的解混技术包括两个步骤:(1)自动提取每种纯色团的参考光谱;(2)从估计的参考光谱中计算每种纯色团的丰度。比较策略从高光谱遥感领域到多光谱光声,带来了正性和和为一约束,以评估色团浓度。特别是,该研究从高光谱遥感领域提取端元,并将算法与专门用于多光谱光声数据分割的算法进行比较。首先,这些策略在彩色 4%琼脂幻影数据上的色团稀释和混合中进行了测试。然后,进行了一些初步的体内实验。这些实验包括估计小鼠肿瘤中的氧饱和度(sO2)。本文提出了将高光谱遥感算法引入多光谱光声成像以估计色团浓度的概念验证。