光声显微成像技术的研究进展

光声成像作为一种兼具高光学对比度和大超声探测深度的新兴成像方法,突破了传统光学成像技术分辨率与成像深度相互制约的壁垒,获得了空前快速的发展,其中,光声显微成像技术继承了光声成像技术的优点,采用声学或光学聚焦的成像模式,可以实现高对比度、高分辨率的生物组织结构、分子与功能成像,在神经学、眼科、血管生物学和皮肤学等研究领域具有潜在应用价值。为此,首先介绍了光声成像技术的原理和分类,然后围绕光声显微成像(Photoacoustic microscopy, PAM)技术这一主题,重点综述了新型PAM技术的发展情况、PAM焦深（Depth of focus, DoF）延拓技术以及PAM的生物医学应用。最后,总结了PAM技术发展存在的挑战,并对未来发展方向进行了展望。     

生物组织光声成像技术综述

光声成像是一种低功率、非电离的成像方式,既具有声学方法对深层组织成像分辨率高的优点,又具有光学方法在功能成像、分子成像方面具有高对比度的优势。本文回顾了近年来,光声成像技术在生物医学领域的研究进展,介绍了光声成像的基本成像原理。以此为基础,本文介绍了光声成像的两种主要成像方案：光声断层成像和光声显微镜,并且讨论了光声成像在获取生物组织化学成分信息和微结构信息方面的优越性;最后,本文对光声成像技术的优点和应用前景进行了总结。     

开放式的高分辨率超声成像系统

在医学和生物学研究当中,对活体组织进行无创性成像具有重要意义。高分辨率超声成像技术,可以对细微组 织实现高空间分辨率的成像,已被广泛应用于皮肤、眼睛、心血管和小动物成像等生物医学领域。目前领域内的相关研 究需要不同的成像系统参数,例如要求不同的探头性能、数据采集策略、信号处理以及图像重建、显示和保存方法。因 此需要一种灵活和开放的超声成像系统,能让用户根据各种研究需求实现个性化设置并能全面获取原始实验数据。文章 提出了一种实时的、便携式设计的开放式超声成像系统,可支持定制的高分辨率超声成像研究。系统基于高速现场可 编程逻辑门阵列(FPGA)实现灵活多样的超声成像。用户可以轻松地根据个性化应用需求来调整系统结构。测试结果表 明,本系统能实现 B 超成像、编码激励成像、多普勒成像、血管内成像、多模态组合成像等,为高分辨率生物医学应用 研究提供了非常灵活的成像工具。     

生物医学图像信息分析的相关研究

生物医学图像信息分析,是当今的前沿研究课题。文章介绍了在生物医学图像信息分析方面开展的相关研究及其方法,主要包括生物医学显微三维成像技术、心室压力-容积关系的超声图像分析与辅助诊断研究、基于光栅投影三维成像与三维重建的美容整容的设计技术。     

高光谱图像在生物医学中的应用

高光谱成像（hyperspectral imaging,HSI）作为生物医学可视化的一种新兴技术,在生物医学领域的研究正逐渐受到关注。随着高光谱成像技术以及精准医学的迅速发展,将高光谱成像技术应用于近距离的医学诊断成为新的研究趋势。高光谱成像技术能同时获取生物组织的2维空间信息和1维光谱信息,覆盖可见光、红外和紫外等光谱范围,具有较高的光谱分辨率,可提供有关组织生理、形态和生化成分的诊断信息,为生物组织学研究提供更精细的光谱特征,进而为医学病理诊断提供更多辅助信息。本文介绍了高光谱成像技术的基本原理、高光谱显微成像系统的基本构成及特点。基于此,总结并阐述了高光谱成像技术在疾病诊断和手术指导中的应用进展,涉及其在癌症、心脏病、视网膜疾病、糖尿病足、休克、组织病理学和图像引导手术等方面的应用。综合分析了高光谱成像技术在生物医学领域应用的局限性,并提出了生物医学研究领域中该技术的未来发展方向。     

氢气微泡的制备及其超声成像研究

氢气是一种新型、简单、安全的选择性抗氧化剂。但氢气的水溶性低、难以实时追踪和定点可控释放限制了其在生物医学上的应用。微泡具有造影、载气及定点可控释放的特性。文章将氢气与全氟丙烷通过一定比例混合制备了一种新型的载氢气微泡。体外、体内超声成像实验发现载氢气微泡在磷酸盐缓冲液以及在大鼠左心室和肝脏中均有很好的超声成像性能。该研究为进一步利用氢气微泡来实现超声定点释放和治疗奠定了实验基础。     

快速在体光声计算层析图像重建方法

针对超声阵列式光声计算层析成像技术数据采集量大、成像速度慢的问题,为拓展该技术在血流动力学等领域的应用,提出一种基于主成分分析(PCA)的快速光声计算层析图像重建方法。该方法首先通过部分全采样数据,构建样本图像矩阵;然后,通过矩阵分解运算构建信号投影矩阵;最后,基于该投影矩阵在三倍欠采样条件下快速重建出高质量三维光声图像。在体小鼠背部血管成像实验表明:与传统反投影光声图像重建方法相比,基于主成分分析的光声图像重建方法可将数据采集规模降低约35%,三维图像重建速度提高约40%,实现了三倍欠采样条件下高精度光声图像的快速采集与重建。     

微 / 纳米机器人在生物医学中的应用进展

精密医学是现代生物医学发展的重要方向,而微/纳米机器人的出现推动了精密医学的发展。 这些小型化机器人通过自组装、电子束沉积和 3D 打印等方法制造,能够引发化学反应或在超声波、 光场、磁场等外部场以及微生物(细胞)的作用下运动。它们在生物医学中的用途广泛,能够通过装载药物颗粒、生物试剂和活细胞等来实现精准的货物输送;也可作为一种小尺寸的手术工具用于外科手 术,治疗疾病;还能检测生物体中的金属离子等物质以做好疾病初期的诊断;此外,还能通过光声、 磁共振等不同方式进行医学成像。在过去十年中,微/纳米机器人在这些方面的研究取得了一定的进 展,推动了现代医学的发展。     

一种高重复频率激发电路下的高速超声内窥成像系统

光学相干层析-血管内超声联合（Optical coherence tomography intravascular ultrasound, OCT-IVUS）成像技术能同时弥补光学相干涉成像的低成像深度与超声成像的低分辨率,能够较为全面地进行血管内的易损斑块识别,但受血管内超声（Intravascular ultrasound, IVUS）技术超声激发重复频率限制,OCT-IVUS成像难以在高帧率成像的同时获得高成像线数,从而影响显示分辨率。为提升IVUS成像速度,同时不降低图像显示的分辨率,尝试应用高重频超声激发技术的方法解决这一难题。本文设计了一种50 kHz的高重频超声激发电路,并在此设计基础上研制了一种50 f/s的高速超声内窥成像系统;进而对系统性能进行测试。激发电路高压脉冲测试以及信噪比（Signal noise ratio, SNR）测试结果表明：激发电路可用于25 MHz超声换能器的激发,具有较高的SNR;应用此激发电路所研制的超声内窥成像系统能够在不降低显示分辨率的前提下提高成像速度,该系统技术能有效检出易损斑块,促进OCT-IVUS的临床应用,对心血管疾病的早期发现、诊断和预防具有一定价值。     

基于图神经网络的自闭症辅助诊断研究综述

自闭症谱系障碍是一种复杂的神经系统发展障碍疾病,截至目前其病因尚不明确。图神经网络作为非欧几里得空间深度学习的重要分支,在处理图结构数据的相关任务中取得优异表现,为医学领域的成像和非成像模式的集成提供了可能,因此利用图神经网络进行自闭症等脑部疾病神经成像诊断逐渐成为研究热点。阐述传统机器学习方法在自闭症疾病预测中应用,介绍图神经网络的基本分类,按照图中节点与边关系的建模方法,从基于人群图和基于个体图两个角度对图神经网络在自闭症辅助诊断中的应用进行梳理和分析,并归纳现有诊断方法的优劣势。根据目前基于图神经网络的自闭症神经成像诊断的研究现状,总结了脑神经科学领域辅助诊断技术面临的主要挑战和未来研究方向,对于自闭症等脑部疾病辅助诊断的进一步研究具有指导意义和参考价值。     

光学分子影像关键技术及应用研究

光学分子影像成为近年来医学影像学研究中的新兴热点之一,其以靶向性的分子影像探针为先决条件,以新兴成像模态为发展特色,以多模态分子影像技术为核心内容,以分子影像手术导航为应用出口,在预临床和临床应用方面都取得了突出的进展。在分子影像探针方面,靶向性的光学分子影像探针在疾病的诊断和治疗研究与应用中得到越来越多的关注。契伦科夫成像作为一种新型的光学分子成像模态,可以实现肿瘤的高灵敏早期检测与精准定位。在多模态分子影像方面,以光学为核心有机融合结构与代谢信息的多模态分子成像技术成为医学影像技术发展的前沿,研发实现结构、功能和分子影像数据的同机获取的光学多模态分子影像成像设备,为生物医学领域的研究提供更精细、更全面的生理病理信息。在分子影像手术导航方面,光学分子影像作为一种非入侵式的成像技术,实现手术过程中对肿瘤及病灶组织边界的实时、精准定位,有效的为外科医生提供辅助,从而提高患者的生存率。总之,光学分子影像技术的不断发展,为疾病的精确诊断与个性化治疗提供新的手段。     

Cluster Analysis of Biomedical Image Time-Series

In this paper, we present neural network clustering by deterministic annealing as a powerful strategy for self-organized segmentation of biomedical image time-series data identifying groups of pixels sharing common properties of local signal dynamics. After introducing the theoretical concept of minimal free energy vector quantization and related clustering techniques, we discuss its potential to serve as a multi-purpose computer vision strategy to image time-series analysis and visualization for many fields of medicine ranging from biomedical basic research to clinical assessment of patient data. In particular, we present applications to (i) functional MRI data analysis for human brain mapping, (ii) dynamic contrast-enhanced perfusion MRI for the diagnosis of cerebrovascular disease, and (iii) magnetic resonance mammography for the analysis of suspicious lesions in patients with breast cancer. This wide scope of completely different medical applications illustrates the flexibility and conceptual power of neural network vector quantization in this context. Although there are obvious methodological similarities, each application requires specific careful consideration w.r.t. data preprocessing, postprocessing and interpretation. This challenge can only be managed by close interdisciplinary cooperation of medical doctors, engineers, and computer scientists. Hence, this field of research can serve as an example for lively cross-fertilization between computer vision and related research.     

DeepPhase: Learning phase contrast signal from dual energy X-ray absorption images

Due to the high hardware complexity and low dose efficiency of existing X-ray phase contrast imaging, the biomedical and clinical applications of this novel imaging technique have been hindered. This study proposes a deep learning method, named DeepPhase, to extract differential phase contrast (DPC) image from two dual-energy absorption images. It obviates the need of dedicated DPC imaging devices such as Talbot–Lau gratings and is compatible with diagnostic-level dual-energy X-ray imaging hardware. Given two dual-energy absorption images for an object, all we need to produce its DPC image at a certain energy is a well-trained DeepPhase network. Results demonstrate that, compared with conventional Talbot–Lau interferometry, DeepPhase achieves high-quality DPC imaging at multiple dual-energy combinations and low radiation dose.     

Histology image analysis for carcinoma detection and grading

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.     

A multilayer lateral-flow microfluidic device for particle separation

Particle separation technology plays an important role in a wide range of applications as a critical sample preprocessing step for analysis. In this work, we proposed and fabricated a multilayer lateral-flow particle filtration and separation device based on polydimethylsiloxane molding and transfer bonding techniques. Particle separation capability was demonstrated by 4.5-um polystyrene bead filtration and cancer cell (SK-BR-3) retrieving. This device exhibits higher throughput compared with most active particle separation methods and is less vulnerable to membrane clogging problem. This novel multilayer particle filtration and separation device is expected to find applications in biomedical, environmental and microanalysis fields.     

A variational approach to vesicle membrane reconstruction from fluorescence imaging

Biological applications like vesicle membrane analysis involve the precise segmentation of 3D structures in noisy volumetric data, obtained by techniques like magnetic resonance imaging (MRI) or laser scanning microscopy (LSM). Dealing with such data is a challenging task and requires robust and accurate segmentation methods. In this article, we propose a novel energy model for 3D segmentation fusing various cues like regional intensity subdivision, edge alignment and orientation information. The uniqueness of the approach consists in the definition of a new anisotropic regularizer, which accounts for the unbalanced slicing of the measured volume data, and the generalization of an efficient numerical scheme for solving the arising minimization problem, based on linearization and fixed-point iteration. We show how the proposed energy model can be optimized globally by making use of recent continuous convex relaxation techniques. The accuracy and robustness of the presented approach are demonstrated by evaluating it on multiple real data sets and comparing it to alternative segmentation methods based on level sets. Although the proposed model is designed with focus on the particular application at hand, it is general enough to be applied to a variety of different segmentation tasks.     

Applications of protein microarrays for biomarker discovery

The search for new biomarkers for diagnosis, prognosis, and therapeutic monitoring of diseases continues in earnest despite dwindling success at finding novel reliable markers. Some of the current markers in clinical use do not provide optimal sensitivity and specificity, with the prostate cancer antigen (PSA) being one of many such examples. The emergence of proteomic techniques and systems approaches to study disease pathophysiology has rekindled the quest for new biomarkers. In particular the use of protein microarrays has surged as a powerful tool for large-scale testing of biological samples. Approximately half the reports on protein microarrays have been published in the last two years especially in the area of biomarker discovery. In this review, we will discuss the application of protein microarray technologies that offer unique opportunities to find novel biomarkers.