光声分子影像技术在生物医学中具有广泛的应用。实现分子探针浓度的准确解析，对于相关疾病的研究具有重要意义。然而，活体探测时，来源于生物组织的信号与探针的信号混叠，增加了准确解析的难度。当前改善的方法：或需要使用多波长进行探测，导致解析速度慢而且过程复杂；或使用单波长的方法，但需要依托于一类特殊探针的开关功能，导致普适性不足。鉴于此，提出了一种基于Grüneisen弛豫非线性光声效应的单波长浓度解析方法，并分别对色素染料与罗丹明6G探针两类样品开展了解析实验。结果表明，本文提出的方法获得的解析结果比线性单波长方法的解析结果误差更小，并且具有较好的普适性，为光声分子影像的浓度解析方法提供了新的思路。

Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.

肿瘤是严重威胁人类健康和社会发展的重大公共卫生问题之一。针对肿瘤的治疗，外科手术切除仍是最普遍、最理想的策略。目前，外科医师在术中主要通过肉眼观察、超声等方法确定肿瘤边界、残余病灶以及微小转移病灶，然而，这些方式存在手术切缘肿瘤易残留、难以实时发现微小转移灶等问题，导致肿瘤术后复发、转移率高，严重影响病人的预后和远期生存。荧光手术导航的快速发展为解决这一问题提供了新的技术支撑。本文聚焦于荧光手术导航用荧光探针，重点对荧光手术导航系统和各类有机、无机近红外二区荧光分子探针进行系统阐述，分析其临床转化与应用面临的瓶颈问题，探讨可能的解决思路，以期为国内外相关领域的研究提供思路与参考。

Noninvasive molecular imaging makes the observation and comprehensive understanding of complex biological processes possible. Photoacoustic imaging (PAI) is a fast evolving hybrid imaging technology enabling in vivo imaging with high sensitivity and spatial resolution in deep tissue. Among the various probes developed for PAI, genetically encoded reporters attracted increasing attention of researchers, which provide improved performance by acquiring images of a PAI reporter gene's expression driven by disease-specific enhancers/promoters. Here, we present a brief overview of recent studies about the existing photoacoustic reporter genes (RGs) for noninvasive molecular imaging, such as the pigment enzyme reporters, fluorescent proteins and chromoproteins, photoswitchable proteins, including their properties and potential applications in theranostics. Furthermore, the challenges that PAI RGs face when applied to the clinical studies are also examined.

光声成像兼具光学成像对比度高和超声成像在深层生物组织中分辨率高等优点,是近年来迅速发展起来的一种生物医学成像模态。光声显微成像(PAM)是光声成像的一种重要实现方式,利用其可以无创提供活体生物组织结构和功能信息的优点,研究人员已开展了临床前和临床应用研究。为了使不同领域的研究人员了解这一快速发展的成像技术,本文综述了光声显微成像的发展现状、最新技术和研究进展。文章首先介绍了PAM的基本原理和典型的系统实现,然后概述了包括空间分辨率、成像深度、扫描方式、信号探测手段和多模态成像等方面的重要研究进展,接着阐述了PAM在生物医学领域的应用现状,最后总结了其未来发展面临的挑战。

生物发光断层成像(BLT)是一种非侵入、高灵敏度的光学分子影像技术,可以通过探测生物体表面的光信号重建出生物体内部光源的三维分布情况。由于光在组织中传播时,散射占据主导作用,导致BLT重建问题的病态性,给光源重建带来巨大的挑战。在BLT重建中,基于光源稀疏分布的特征,稀疏正则化方法相比于传统的L2范数正则化取得了显著进展。更进一步,由于生物发光光源的分布具有的空间聚集特征,利用该特征将有助于进一步提高BLT重建的准确性。相比于传统的针对求解域中所有未知量进行稀疏重建的算法,探索了利用块稀疏进行生物发光断层成像重建的可行性,首先通过对系统矩阵进行相关系数分析将求解域划分成一系列数据块,然后利用块稀疏贝叶斯算法对生物发光光源的分布进行三维重建。通过仿真实验与小鼠活体实验,并与传统稀疏重建算法L1-LS进行了比较,结果表明该方法可以有效缓解BLT重建问题的病态性,抑制噪声,并且可提高重建结果的准确性。

Photoacoustic imaging (PAI) breaks through the optical diffusion limit by making use of the PA effect. By converting incident photons into ultrasonic waves, PAI combines high contrast of optical imaging and high spatial resolution in depth tissue of ultrasound imaging in a single imaging modality. This imaging modality has now shown potential for molecular imaging, which enables visualization of biological processes with systemically introduced functional nanoparticles. In the current review, the potentials of different optical nanoprobes as PAI contrast agents were elucidated and discussed.