激光散斑衬比血流成像关键技术及应用研究进展 下载: 1196次
Blood flow is an important parameter for measuring vital signs, and hemodynamic parameters are functional indicators of the microcirculatory system of the skin, brain, heart, liver, kidneys, and other organs. Therefore, dynamic blood flow monitoring has important application value and significance in clinical and basic life science fields, such as clinical diagnosis, intraoperative guidance, drug research, disease mechanism research, and neuroscience. Laser speckle contrast imaging (LSCI) is a full-field optical imaging technique that uses the spatial and temporal statistical properties of laser scattering intensity to monitor the blood flow of tissues in vivo. It uses simple equipment, is non-invasive, and has a fast imaging speed and high spatial resolution. Additionally, it does not require the injection of a contrast agent and can perform continuous measures for a long time. Consequently, it is widely used to measure microcirculatory blood flow parameters such as the vessel diameter, blood flow velocity, blood perfusion, and blood density in tissues and organs. It also can help doctors locate the lesion precisely with clear and accurate blood flow data,and then analyze the corresponding functional response and pathological mechanisms, which has become one of the most important tools for the clinical diagnosis of fundus diseases, skin diseases, brain diseases, and so on. In addition, it is also an important tool for basic life science research in drugs, cardiovascular and cerebrovascular diseases, and brain cognitive and behavioral sciences. Consequently, in-depth research on novel LSCI techniques with high imaging quality is valuable and significant for improving the quality of medical care and promoting the development of basic research in life science.
In the past decades, many researchers have conducted extensive researches on how to improve the quality of LSCI and expand the scope of LSCI applications, and they have had positive progress. For example, a few research groups like Luo Qingming and Li Pengcheng at Huazhong University of Science and Technology, and Tong Shanbao at Shanghai Jiao Tong University have worked on portable LSCI systems, high signal-to-noise ratio LSCI, and high resolution LSCI, which have promoted the development of LSCI in China. Researchers abroad like Boas at Boston University, Zakharov at the University of Fribourg, and Dunn at the University of Texas at Austin have worked on high-precision imaging using LSCI techniques, such as static scattered light correction and quantitative analysis of LSCI, which has also greatly promoted the development of key techniques and novel LSCI applications.
In this paper, we presented a systematic, comprehensive and integrated analysis, review and summary of the current researches about key techniques and applications of novel LSCI at home and abroad emphatically from the aspect of high signal-to-noise ratio LSCI, high-resolution LSCI, high-precision LSCI, large imaging depth LSCI, and novel LSCI systems based on the investigations of current literature. In this way, we can help researchers learn more about the frontier technologies of LSCI and understand the technical challenges we faced, and we can provide ideas with a reference value to promote the development of high-quality, highly practical, and innovative LSCI systems to meet the needs of clinical diagnosis and basic biomedical research. The review consisted of the following contents: First, the technical problems of measuring deep blood flow and achieving high resolution, high signal-to-noise ratio, and high precision have been systematically summarized, and the corresponding solutions are indicated. Subsequently, we review high signal-to-noise ratio LSCI techniques based on anisotropic filtering, eigenvalue-decomposition, and transformation domain collaborative filtering methods. Meanwhile, high-resolution LSCI for motion artifact, out-of-focus blur, and non-uniform light intensity correction are also summarized. Third, we elaborate the high-precision LSCI from the perspective of static scattered light correction, quantitative analysis, and novel LSCI algorithms. After summarizing the LSCI with a large imaging depth, we introduce the latest research on the novel LSCI system and its applications in the fields of cortical blood flow imaging, surgical and therapeutic procedures, and brain and cognitive-behavioral sciences. Finally, we discuss and look forward to the development of LSCI in the future.
In conclusion, LSCI has made qualitative leaps and developments in theory, imaging systems, computational methods, and clinical applications. The imaging quality of LSCI has been developed to have a high signal-to-noise ratio, high resolution, high accuracy, and large imaging depth. However, as the application scenarios of LSCI become more and more complex, which introduces greater challenges to the development of key techniques and application of LSCI. In the future, LSCI will be deeply integrated with emerging interdisciplinary fields such as biomedicine, optoelectronic information, artificial intelligence, and big data. In addition, new breakthroughs are expected in the following respects. (1) Quantitative analysis capacity. The capacity is still an important fundamental issue for LSCI in functional applications. (2) Combination of LSCI with new endoscopic technology (this will enable the noninvasive measurement of blood flow). (3) Miniaturization and integration. The development of new materials and electronic devices will certainly promote the miniaturization and integration of new LSCI systems. (4) Combination of LSCI with artificial intelligence. Artificial intelligence will further promote the development of LSCI technologies and their applications. (5) Combination with other imaging modalities (this will build a new model for LSCI-based multimodal clinical diagnostic applications). It is believed that LSCI will show a synergistic development trend in the future. We look forward to seeing the development of technologies and applications of LSCI.
1 引言
血流是衡量生命体征的一项重要参数,血流动力学参数是反映皮肤、大脑、心脏、肝肾等器官微循环系统的功能性指标[1-4]。激光散斑衬比血流成像(LSCI)作为一种非扫描全场成像技术,具有设备简单、非侵入性、无需注入造影剂、成像速度快、分辨率高且可长时间连续测量等优势[5-7],已被广泛应用于视网膜、皮肤、脑部等组织和器官的血管直径、血流速度、血流灌注、血流密度等微循环血流参数的测量[8-11],为分析组织和器官的结构、功能和代谢,进而实现疾病诊断、术中监测、致病机理研究提供有效的技术手段[12-15]。LSCI技术能够提供清晰、准确的血流数据,帮助医生精准定位病灶,同时也是眼底疾病[12,16-17]、皮肤疾病[18-20]、脑认知与行为科学[21-23]等临床诊断和生命科学基础研究中的重要工具。因此,对新型LSCI成像技术以及高成像质量的LSCI技术展开深入研究,对于提高医疗质量和推动生物医学、生命科学等基础研究发展具有重要价值和现实意义。
为了推动LSCI技术的发展及应用,研究人员从基本原理、系统和功能应用等方面对LSCI技术进行了综述[5,20,24-28]。比如:文献[20]和文献[24]重点阐述了LSCI在血流监测领域的应用进展;Basak等[24]从理论、计算方法、分辨率、处理时间等方面对几种不同的激光散斑衬比度分析方法进行了详细比较,重点介绍了LSCI技术在皮肤、视网膜、脑皮层等组织血流成像应用上的研究进展;孔平等[25]综述了不同速度模型、曝光时间、散斑尺寸和系统设计对LSCI成像质量的影响;李晨曦等[27]从成像质量、定量分析、仪器设计等方面对激光散斑成像方法进行综述,并总结了LSCI在眼科、微循环、脑科学、皮肤科及术中监测等各个领域的应用进展;Heeman等[5]对LSCI在烧伤外科、皮肤病诊断与治疗、胃肠手术、神经外科手术等临床诊疗上的应用进行了综述;王淼等[28]围绕脑卒中、吸毒成瘾、阿尔茨海默病等脑疾病及其他脑科学应用场景,阐述了LSCI在脑科学基础性研究中的应用进展。通过文献资料调研发现,目前的研究主要集中在LSCI原理和功能性应用上,尚未对LSCI中的关键技术难题以及对应的解决思路与方案进行系统性综述,特别是对于高质量LSCI关键技术、新型LSCI系统及其应用的国内外研究现状缺乏系统、全面的分析和总结。
为了帮助研究人员更好地了解当前LSCI面临的技术难题,厘清相应问题的解决思路与技术方案,深入挖掘用户最新应用需求,开发出高质量、高实用性、创新性强的LSCI系统,本文重点围绕如何提高LSCI的分辨率、信噪比、血流估计精确度、成像深度以及开发新型LSCI应用系统等五个方面的关键技术及应用研究进展进行详细综述。首先简要介绍LSCI的基本组成和工作原理,在此基础上,指出实现深度血流成像和高分辨、高信噪比、高精度LSCI需要解决的关键问题以及对应的解决思路;接着,总结和分析了近年来国内外学者在提高LSCI信噪比、分辨率、血流估计精确度、成像深度与定量分析能力等关键技术上的研究进展;最后,介绍新型LSCI成像技术的最新研究成果以及新型LSCI在脑皮层血流成像、手术与治疗过程以及脑与认知行为科学等领域应用的新进展,并对LSCI未来的发展和研究方向进行总结和展望。
2 LSCI基本原理
2.1 基本原理
典型的LSCI系统主要包括光源、成像模块、图像采集模块及散斑图像处理模块[29-30]。如
研究人员[31-35]用散斑衬比度K来描述强度变化引起的散斑模糊程度,并由此推导了散射粒子的运动速度。
式中:
式中:
综上可知,散斑衬比度K值与速度成反比,在理想状态下K的取值范围为0~1。当散斑颗粒静止时,K=1;当散斑颗粒运动得很快时,K接近于0。在利用LSCI系统进行血流成像时,由CCD相机捕获的图像中主要包含血管和组织,其中:血管中包含了大量运动的血红细胞,即血管部分区域散射颗粒的运动速度大;而组织这部分区域不运动,散射颗粒的运动速度小,为0。由
2.2 经典衬比度分析方法
由上述LSCI基本原理可知,实现LSCI血流成像与监测的关键在于如何计算衬比度K,进而对运动颗粒的速度信息进行功能性表征,由此实现二维血流功能成像。激光散斑衬比分析法(LASCA)[32,39]是较早提出的衬比度K的计算方法。为了平衡血流成像的时间分辨率、空间分辨率和衬比值统计准确性之间的矛盾,研究人员以LASCA理论模型为基础,逐步衍生出了空间统计与时间统计相结合的经典衬比分析法。比如:2001年,Dunn等[41]提出了空间平均衬比分析法(SLASCA);2003年,骆清铭团队[42]提出了时间衬比分析法(LSTCA),提高了散斑衬比图像的空间分辨率;2007年,Le等[43]提出了时间平均衬比分析法(TLASCA);2008年,Duncan等[44]提出了时空联合衬比分析法(stLASCA)。
上述衬比度分析方法各有优缺点,如:LASCA[32,39]不需要扫描,速度快,但这种方法以牺牲空间分辨率为代价来获得较高的时间分辨率,不利于微小血管的血流检测;LSTCA[42]以牺牲时间分辨率为代价获得较高的空间分辨率;SLASCA[41]和TLASCA[43]以累加的方式分别将空间统计和时间统计结合,获得了折中的空间分辨率和时间分辨率;stLASCA[44]则是将时间维度和空间维度的像素点构成的三维集合看成是一个计算单元,在保证时间和空间分辨率的同时,获得了较高的信噪比和统计准确性。Luo研究团队[30]通过数值模拟和实验仿真等方法对上述几种衬比分析方法的统计精度进行了定量分析,结果表明stLASCA的效果最佳。
3 LSCI关键技术的研究进展
在过去的几十年中,国内外学者针对如何提高LSCI成像质量以及拓展LSCI应用范围展开了广泛研究。在国内,华中科技大学的骆清铭和李鹏程课题组[21,42,45-52]、上海交通大学的童善保课题组[53-63]、天津大学的李晨曦课题组[27,64]、中国科学院苏州生物医学工程技术研究所的杨西斌课题组[29,65-66]、上海理工大学的杨晖课题组[67-68]和佛山科学技术学院的曾亚光课题组[11,69-72]等在LSCI系统、关键技术及临床应用方面进行了大量的研究工作。在国外,波士顿大学的Boas课题组[16,41,73-78]、得克萨斯大学奥斯汀分校的Dunn课题组[16,41,76-77,79-83]、瑞士弗里堡大学的Zakharov课题组[84-85]、加州大学欧文分校贝克曼激光研究所的Choi课题组[86-95]、伦敦金斯顿大学的Briers课题组[32-35,38-39,96]等也进行了大量的研究,极大地推动了LSCI系统关键技术及应用等的发展。
LSCI作为一种非侵入性、无创的近红外光成像技术,虽然目前已经能够满足基本的临床应用,但是面对复杂的生物体结构和日益复杂的临床应用场景需求,还存在一些亟待解决的难题。笔者通过广泛调研近年来发表的LSCI相关文献发现,目前关于LSCI关键技术及应用的研究工作几乎都是围绕着以下几个方面展开的:1)如何提高成像信噪比;2)如何校正光强分布不均匀;3)如何校正运动伪影;4)如何校正失焦模糊;5)如何消除静态散射光;6)如何校正动态散斑衬比模型;7)如何提高定量分析能力;8)如何提高成像深度。
3.1 高信噪比LSCI技术
在LSCI成像过程中难免会引入各类噪声,比如偏移噪声和随机噪声[97],从而降低LSCI成像信噪比。LSCI的噪声来源主要包括:1)由生物组织的生理状态引入的生理噪声,如活体生物的呼吸、心跳和运动以及背景组织产生的静态散射光[97];2)成像设备引入的噪声,如激光器、CCD相机引入的系统噪声;3)统计噪声[97]。其中,呼吸、心跳、运动等生理噪声以及CCD等成像系统噪声会引起衬比度的改变,这些噪声被称为偏移噪声。由CCD相机噪声间接引起的统计噪声以及来自生物、光学、电子波动的非统计噪声,被称为随机噪声[97]。为了获得高信噪比的激光散斑衬比血流图像,研究人员以各向异性[98]、信号分解[64,72,99]、变换域滤波[47,69,100]等技术角度作为切入点,提出了相应的解决方案。
3.1.1 各向异性LSCI算法
经典的散斑衬比分析方法如LASCA、LSTCA和stLASCA等均以各向同性模型解决时空分辨率的问题。然而,血管中的血流是朝着一定方向流动的,基于该实际情况,Rege等[98]考虑了血红细胞运动方向对散斑衬比值的影响,提出了一种各向异性的LSCI散斑信号分析算法(aLSCI)。该算法的实现流程如
图 4. 不同算法的实验结果对比[98]。(a)tLSCI(时间衬比分析法);(b)sLSCI(空间衬比分析法);(c)stLSCI(时空联合衬比分析法);(d)savgtLSCI(空间平均衬比分析法);(e)tavgsLSCI(时间平均衬比分析法);(f)aLSCI;(g)不同算法的对比度噪声比
Fig. 4. Comparative experimental results of different algorithms[98]. (a) tLSCI algorithm; (b) sLSCI algorithm; (c) stLSCI algorithm; (d) savgtLSCI algorithm; (e) tavgsLSCI algorithm; (f) aLSCI algorithm; (g) contrast-to-noise ratio (CNR) of different algorithms
3.1.2 奇异值/特征值分解滤波的LSCI
2018年,Wang等[72]使用主成分分析方法提取不同维度的散斑图像信号,进而分离出了动静态散斑信号。Kulkarni等[99]提出了基于奇异值分解的动态散斑衬比成像方法。进一步,Li等[64]提出了基于特征分解的激光散斑信号统计方法,如
图 5. 基于特征值分解的LSCI滤波算法模型[64]( ′:原始信号; :去除白噪声后的图像; :静态散射光信号; :血流信号; :白噪声信号)
Fig. 5. LSCI filtering model based on eigenvalue-decomposition[64] ( : original speckle signal vector; : speckle signal vector after denoising; : static scattered light signal; : fluctuating blood signal; : white noise signal)
在Li等[64]研究的基础上,Wang课题组[100]进一步提出了将特征值分解与空间滤波相结合的方法,算法模型如
图 6. 基于特征值分解和空间滤波相结合的LSCI滤波算法[100]
Fig. 6. LSCI filtering algorithm based on eigenvalue-decomposition and filtering[100]
图 7. 对比实验结果[100]。(a)原始的眼底衬比图;(b)使用特征值分解和空间滤波处理后的眼底衬比图
Fig. 7. Comparative experimental results[100]. (a) Raw fundus contrast image; (b) fundus contrast image after eigenvalue-decomposition and spatial filtering
3.1.3 基于变换域协调滤波的LSCI方法
为了获得高信噪比的LSCI图像,部分学者提出了变换域滤波的方法,如:基于强度波动调制的散斑衬比分析方法[11,69-70],该方法通过频域滤波的方式分离动态散射信号和静态散射信号,从而实现LSCI血流成像功能;Li课题组[47]在块匹配处理思想和三维块匹配(BM3D)非局部均值滤波去噪算法[102]的基础上,提出了基于曼哈顿距离的自适应3D变换域协调滤波(MD-ABM3D)的LSCI散斑图像分析方法,该方法以曼哈顿距离为匹配条件,采用多重估计和加权聚合的方式对激光散斑血流图像进行处理。如
图 9. 不同去噪算法的实验结果[47]。(a)原始图,PSNR为18.5,MSSIM为0.46,R=0.813;(b)savg-tLSCI算法,PSNR为32.8,MSSIM为0.87,R=0.987;(c)NLM算法,PSNR为31.0,MSSIM为0.90,R=0.986;(d)BM3D算法,PSNR为35.8,MSSIM为0.92,R=0.993;(e)MD-ABM3D算法,PSNR为37.8,MSSIM为0.96,R=0.996;(f)参考图
Fig. 9. Output of different denoising algorithms[47]. (a) Original image, where PSNR is 18.5, MSSIM is 0.46, and R is 0.813; (b) savg-tLSCI algorithm, where PSNR is 32.8, MSSIM is 0.87, and R is 0.987; (c) NLM algorithm, PSNR is 31.0, MSSIM is 0.90, and R is 0.986; (d) BM3D algorithm, PSNR is 35.8, MSSIM is 0.92, and R is 0.993; (e) MD-ABM3D algorithm, PSNR is 37.8, MSSIM is 0.96, and R is 0.996; (f) reference image
3.2 高分辨率LSCI技术
成像分辨率是LSCI的另一个重要技术参数。导致LSCI成像分辨率下降的主要因素可以归纳为以下几个方面:1)运动伪影。由于活体自身的呼吸、心脏跳动以及自由移动等因素,相机与拍摄物体之间产生了相对运动,从而产生运动伪影,使散斑图像模糊[45,61,103]。2)非均匀性分布。成像表面(比如大鼠头部)的曲面效应和CCD相机的非均匀特性,都会引起散斑强度的波动,使原始散斑图像数据具有不均匀特性,从而导致衬比度K值出现估计偏差[103-104]。3)失焦模糊。在实际应用中,由于生物组织表面并不是完全平坦的,如脑部、腹部等多呈曲面,从而导致在光学成像系统景深的限制下,成像视野中的血管可能不在同一焦平面上,最终导致图像产生失焦模糊。综上,运动伪影、光强分布不均匀以及失焦模糊等均会严重影响LSCI的成像分辨率。为了获得高分辨率的LSCI血流图像,研究人员提出了基于图像配准[45,103,105]、运动伪影校正[106]、波前编码[107-108]、失焦校正[46,109]、背景校正[104,110]的LSCI算法,以提升LSCI成像分辨率。下面对上述算法的研究进展进行详细介绍。
3.2.1 基于图像配准的LSCI运动伪影校正
图像配准是将不同获取时间、不同传感器、不同获取条件下同一场景或同一目标的两幅或者多幅图像进行匹配的过程[105]。针对运动伪影,国内外学者提出了基于图像配准技术的解决方案。比如:Miao等[61,103]提出了散斑衬比图像配准算法(rLASCA算法),如
图 11. rLASCA算法的实验结果[61]。(a)未配准的散斑衬比图像;(b)rLASCA算法配准后的散斑衬比图像;(c)图(a)中白色矩形框区域的放大图;(d)图(b)中白色矩形框区域的放大图;(e)白色矩形框区域的白光图
Fig. 11. Experimental results of rLASCA algorithm[61]. (a) Unregistered laser speckle contrast image; (b) laser speckle contrast image registered by rLASCA; (c) enlarged image of white rectangular box area in figure (a); (d) enlarged image of white rectangular box area in figure (b); (e) white light map of white rectangular box area
针对上述问题,Liu等[45]提出了基于非相干光的非刚体配准算法,算法如
图 12. 基于非相干光的非刚体配准算法[45]。(a)双模态照明装置;(b)算法模型
Fig. 12. Non-rigid registration algorithm based on non-coherent light[45]. (a) Experimental setup of dual-mode lighting system; (b) algorithm model
图 13. 刚性配准与非刚性配准结果的对比[45]。(a)未配准的血流图像;(b)血流图像的刚性配准结果;(c)血流图像的非刚性配准结果
Fig. 13. Comparison of rigid registration and non-rigid registration[45]. (a) Unregistered blood flow image; (b) blood flow image after rigid registration; (c) blood flow image after non-rigid registration
3.2.2 基于图像分解的LSCI运动伪影校正
针对运动伪影的校正,除了图像配准[45,61]、基于信息熵的衬比算法[59,111]等方法外,还有Guilbert等[106]通过引入图像分解的思想提出的基于图像分解的运动伪影校正模型。如
式中:
图 14. 基于图像分解的LSCI运动伪影的校正模型[106]。(a)校正模型;(b)选取回归参量;(c)回归拟合分析;(d)~(f)校正前后的衬比值
Fig. 14. Correction model for LSCI movement artifact based on image decomposition[106]. (a) Correction model; (b) selection of regression variance; (c) fitted by regression analysis; (d)-(f) contrast value before and after movement correction
3.2.3 基于多聚焦图像融合的LSCI校正
失焦模糊是由相机对焦失败或者成像视野内物体的厚度超过景深导致的,会对LSCI成像质量产生严重影响。当前解决失焦模糊的方法主要有波前编码[107-108,112]、基于血流轮廓峰度值分析的失焦测量和校正[109]以及多聚焦图像融合[46]等方法。波前编码成像技术虽然可以有效提高光学系统的景深,但是需要对成像系统的光路进行重新设计,这不仅会增加系统的搭建成本,还会增加成像系统的体积和复杂度[113]。基于多聚焦图像融合的技术主要通过数字图像处理的方式延拓景深,不需要对系统进行改造,具有更大的校正优势。多聚焦图像融合的思想是从同一视野下的多张不同焦平面的图像中提取出最清晰的区域或者像素,然后将它们重新组合成一幅新图像。2019年,Li课题组[46]设计了一种基于轮廓波变换的多尺度变换和多聚焦图像融合算法。该算法的模型如
图 15. 基于轮廓波变换和多聚焦图像融合算法的LSCI校正模型[46]
Fig. 15. LSCI correction model based on contourlet transform and multi-focus image fusion[46]
3.2.4 LSCI非均匀光强校正方法
在光学成像实验中通常采用多个透镜对激光束进行校正,以解决光强分布不均匀的问题,但是在激光散斑衬比成像实验中,成像表面(比如大鼠头部)的曲面效应以及CCD相机的非均匀响应特性,会引起散斑图像信号强度的波动。因此,记录到的原始散斑图像数据中依然存在着不均匀性的影响,导致最终计算得到的衬比度K值出现估计偏差[103-104]。
针对曲面效应导致的衬比度误差,苗鹏[103]采用椭圆抛物面模型构建了激光散斑成像系统的非均匀光强数学模型,并用该模型分离出成像曲面效应和激光束导致的不均匀部分(以重建LSCI衬比图像),得到了校正后的精确的衬比度图像。
图 16. 不均匀光强校正前后的结果[103]。(a)受不均匀性影响的衬比图像;(b)重建后的衬比图像
Fig. 16. Experiment results before and after nonuniform intensity correction[103]. (a) Contrast image affected by nonuniformity; (b) reconstructed contrast image
针对CCD相机信号强度不均匀导致的衬比值误差,Song等[110]通过对光强概率密度分布进行校正,按照不同噪声模型提出了4种散斑模式的衬比值校正模型。
图 17. 非均匀校正实验结果[110]。(a)两种不同光照强度下的灰度散斑图像;(b)从上往下依次是高强度光照、低强度光照下的衬比图以及校正后的低强度光照衬比图;(c)低强度光照、高强度光照以及校正后低强度光照衬比图沿图(a)中横向红线的衬比值曲线;(d)校正后低强度光照衬比图沿图(a)中纵向黄线的衬比值曲线
Fig. 17. Experimental results of nonuniform correction[110]. (a) Grayscale speckle images at two different intensities; (b) from the top to the bottom: contrast maps at high intensity and low intensity and corrected contrast map at low intensity; (c) contrast profile along the red line marked in figure (a) of contrast maps at low intensity and high intensity and corrected contrast map at low intensity; (d) contrast profile along yellow line marked in figure (a) of corrected contrast map at low intensity
3.3 高精度LSCI技术
在进行皮肤血管或者脑皮层血流成像时,激光需要透过组织或者头骨等静态介质,所产生的静态散射光不仅会影响LSCI的信噪比,还会影响血流速度估计的精确度。Fredriksson团队[114]和Postnov等[74]的研究表明:散射粒子的各向异性、散射特性(单散射/多散射)和粒子运动模式(有序/无序)对电场自相关函数
3.3.1 LSCI静态散射光校正方法
2006年,Choi等[95]验证了LSCI技术测量的血流速度与真实血流速度之间存在线性关系,这说明激光散斑成像能够实现宽场域监测微血管网络的血流活性。但是,Fercher等[39]和Choi等[95]提出的相关模型使用电场去相关时间估计血流速度,并没有考虑活体血流成像过程中不同像素区域的局部散射特性总是在空间上发生变化,从而导致估计结果存在较大误差。2008年,Dunn课题组[83]提出了静态散射光的概念,证明了静态散射光会影响LSCI成像质量,增加血流估计误差。此外,散射粒子(物质)的各向异性、散射特性(单散射/多散射)和粒子运动模式(有序/无序)等都会对电场自相关函数产生影响[74,114,117],从而降低血流速度估计的精度。因此,为了提高血流估计精确度,Dunn课题组[83]将散射光分解为动态散射光和静态散射光,在充分考虑静态散射光、实验噪声、散斑均值、非遍历光等因素的影响下,使用多曝光成像技术(MESI)对自相关函数进行校正,建立动态散斑衬比模型,以获得更高精度的衬比值。2008年,Parthasarathy等[83]基于动态散斑衬比模型提出了动态散斑血流成像方法(dLSI)[84]。该方法使用两组相邻的序列帧图像估计静态散射光的影响占比系数
dLSI算法主要通过统计学的方式,利用参数的差减运算来消除静态散射光的影响。然而在实际应用中,生物组织和器官样本是分层和异质的,光子在不同层的迁移是有差异的,因此,静态散射光对LSCI成像的影响是复杂的。Rice等[90]结合蒙特卡罗模型、多曝光散斑成像和空间频率域成像等方案,研究了分层介质中散射光光子的运动特点,并通过建立校正系数模型来消除静态散射光的影响,最终提高了深部血流的成像质量。
此后,为了获得更高质量的LSCI,学者们纷纷开始关注散射特性、运动模式、各向异性等多种因素对散斑衬比模型的影响,并以动态散斑衬比模型为基础进行了校正,且校正主要围绕动态散射光占比、电场自相关函数
在动态散射光占比校正方面,2017年,Li课题组[51]基于动态散斑衬比模型提出了一种校准方法,用于计算动态散射光的占比,从而对前人的动态散斑衬比模型进行了优化。该校准方法通过短时曝光计算出参数
Zheng等[118]在Li课题组研究的基础上进一步对动态散射光占比
在电场自相关函数校正方面,Miao等[58]研究了局部散射特性对血流速度预估结果的影响,并提出了新的预估方法:对电场去相关时间
表 1. 动态散斑衬比值校正模型[115]
Table 1. Correction model of dynamic speckle contrast[115]
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2021年,Boas课题组[73]假设散斑图像不受静态散射光和系统因子
表 2. 不同散射特性和粒子运动模式的电场自相关函数[73]
Table 2. Electric field autocorrelation function for different scattering characteristics and particle motion models[73]
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重新对衬比值计算表达式进行了推导,得到了
在LSCI物理模型校正方面,2020年,Boas课题组[74]提出一个更加严谨的动态散斑成像模型(DLSI)。如
3.3.2 定量LSCI技术
正如前文所述,静态散射光校正技术存在一定不足,比如多曝光成像需要采集一组一定曝光时间范围内的散斑数据,增加了数据采集时间。对动态散斑衬比模型进行校正需要在大的频域范围内测量动态散射光,然后对散射特性和粒子运动模式进行分离和量化。这些方法需要建立数学模型才能对定量测量结果进行校正,对数据处理和计算能力有很高要求,且耗时耗力。此外,静态散射光、散射特性、粒子运动模式、各向异性等多种因素的影响会使血流测量产生误差,导致LSCI技术只能测量血流的相对速度,无法测量其绝对速度,故而只能用来进行定性分析。鉴于此,部分学者从提高LSCI技术的定量分析能力出发,对高精度定量LSCI成像算法展开了研究。
2014年,Tong课题组[63]提出了基于频域激光散斑成像(FDLSI)的血流成像定量分析方法,用于测量血流的绝对速度。2019年,Buijs等[120]提出了一种基于快速傅里叶变换(FFT)的数据处理算法,该算法使用FFT获得功能谱密度函数,在频域上量化散斑波动,从而实现散斑图像的快速定量分析。2021年,Durr课题组[121]提出了基于空间频域成像技术的LSCI(si-SFDI),si-SFDI实验装置如
图 21. 空间频域成像LSCI[121]。(a)si-SFDI实验装置;(b)si-SFDI算法流程
Fig. 21. Spatial frequency domain imagingLSCI[121]. (a) Experimental setup of si-SFDI; (b) processing flow of si-SFDI
将LSCI技术与其他成熟的光学成像/检测技术相结合是实现LSCI定量分析的另一种思路。多普勒分析是一种定量测量方法,Mizeva等[122]基于小波分析研究了LSCI与激光多普勒血流仪(LDF)信号之间的相关关系,探讨了将LDF谱分解技术用于定量分析LSCI数据的可行性。Qureshi等[10]提出了光学散斑图像测速技术(OSIV)。该技术将粒子图像测速技术[123-124]与LSCI技术相结合,实现了宽视场下对血红细胞速度的定量测量。OSIV成像装置如
对应的算法处理过程如
2022年,Lee等[116]针对基于低分辨率大视场成像技术的血流测量存在信号丢失和错误的现象,利用LSCI成像系统的视场可伸缩特性,设计了一种多尺度激光散斑成像系统(msLSCI)。msLSCI通过两个不同分辨率的相机镜头,依次获得小视场高分辨率的局部血流分布图像和大视场低分辨率的整体血流分布图像,解决了视场与分辨率之间相互矛盾的问题,能够有效获取低焦成像模式下的隐藏信号,实现了大视野高精度的信号分析,提高了血流定量测量与分析的准确性。最近,研究人员在提升LSCI定量分析能力的同时,进一步探索了其在功能性分析方面的能力。Dunn课题组[80]探讨了光子在血管中的散射次数与血管深度之间的关系。Miao课题组[55]基于Wishart随机矩阵从散斑图像中分别提取出单散射和多散射信号,通过量化单散射和多散射信号在不同深度组织血流中的响应情况,实现了浅层和深层血流的功能性分析,提高了LSCI对功能和病理定向分析的能力,拓展了其应用范围。
此外,还有学者提出了一些新的散斑衬比图像计算方法,以实现综合提高LSCI技术的信噪比、分辨率和血流估计精确度等。如佛山科学技术学院的曾亚光等[69]根据动态散射信号和静态散射信号在频域分布上的差异,提出了强度波动效应理论。他们首先对序列时间散斑图像中的每个像素点按照时间轴构建时变信号,并用时频变换进行分析,然后使用低通滤波器提取动态散射光分量ID和静态散射光分量IS,最后利用动态散射光ID和静态散射光IS的光强之比定义参量M并以此来构建散斑衬比图。在强度波动效应理论的基础上,曾亚光课题组先后提出了基于强度波动调制的激光散斑血流衬比成像方法(LSI-IFM)[69]、激光散斑深度调制血流衬比方法(LSI-MD)[70]和激光散斑瞬时深度调制血流衬比成像方法(LSI-IMD)[11]。其中,LSI-IMD考虑了红细胞与背景组织之间的吸收差异,能够更加精确地测量血流速度。此外,通过对血红细胞经过的两个相关位置的协方差进行分析,LSI-IMD能够对血流速度和血流方向等进行定性和定量分析,实现功能性血流成像。
LSCI技术的另一个重要创新性进展是把信息论的知识应用到这项技术的研究中。Miao等[59]采用基于平衡熵的动态散斑分析方法来估计血流运动速度,并证实了熵、曝光时间和血流速度之间存在更加简单的线性关系,该方法能够有效减小噪声的影响,是一种更加准确和稳定的血流估计方法。Liu等[125]提出了基于二维熵的LSCI算法(E-LSCI算法),该算法通过引入二维熵来计算衬比值,将二维熵作为衬比值校准的基线。该方法基于二维熵的稳定性可以获得更高准确率和分辨率的衬比值。Kim等[111]提出了一种基于信息论的样本熵的衬比分析方法,如
图 24. 基于样本熵的衬比分析方法及部分实验结果[111]。(a)基于样本熵的衬比分析方法;(b)部分实验结果
Fig. 24. Sample entropy-based laser speckle contrast analysis method and partial experimental results[111]. (a) Sample entropy-based laser speckle contrast analysis method; (b) partial experimental results
综上所述,目前关于高精度LSCI成像算法的研究主要专注于动态散射模型的校正和LSCI定量分析能力的提升,其核心问题是如何建立一个精确的动态散射模型。越来越多的学者开始尝试对LSCI技术重新建立物理模型,比如Miao课题组[53-54]利用随机矩阵理论来分析光在随机介质中的传输过程,建立了新的动态散射模型,用于分离单散射和多散射信号。此外,一些新型的LSCI成像算法,如以强度波动效应为理论基础的衬比分析方法[11,69-70]和基于信息熵的新型LSCI成像算法[59,111,125],分别将数字信号、信息论和光学信息有机结合,提高了LSCI的信噪比、分辨率和血流估计精确度。这种学科交叉的思维对于LSCI计算方法的研究具有重要的启发意义。
3.4 大成像深度LSCI技术
LSCI技术通常需要透过厚厚的组织皮层来获取血流信息,而近红外光的穿透能力有限,无法对深部血流清晰成像。为了提高LSCI技术的成像深度,拓展其在脑科学、临床诊断和手术辅助等领域的应用,学者们分别从照明方式、探测方式以及成像方式等方面优化LSCI的成像深度,提出了多曝光成像[83,126]、线光源扫描照明的横向激光散斑血流分析方法[127]、结构光照明方法[90]、散斑衬比光学层析方法(SCOT)[128-129]、内窥镜式LSCI[50,88]、光透明颅骨窗技术[48]、透射式成像[130-131]等大成像深度LSCI技术,以提高和优化LSCI的成像深度。
多曝光激光散斑成像(MESI)就是在不同曝光时间下采集散斑图像。2008年,Dunn课题组[83]使用
图 25. 多曝光激光散斑成像[83]。(a)MESI系统;(b)单曝光成像和MESI下τc的百分比偏差
Fig. 25. Multi-exposure laser speckle imaging[83]. (a) Multi-exposure speckle imaging system; (b) percentage deviation in under single exposure model and MESI
2012年,He等[127]提出了一种基于非宽场照明的激光散斑对比分析方法。该方法的成像系统如
图 26. 非宽场照明的横向激光散斑对比分析方法[127]。(a)线性扫描照明的横向激光散斑成像系统;(b)图像处理流程;(c)~(e)传统衬比分析方法、使用常数加权的横向散斑衬比分析方法、使用深度灵敏度曲线加权的横向散斑衬比分析方法获得的血流图像
Fig. 26. Lateral speckle contrast analysis method combined with non-wide field illumination[127]. (a) Schematic of LSCI experimental setup based on line beam scanning illumination; (b) image processing flow; (c)-(d) blood flow images obtained by traditional contrast analysis method, lateral speckle contrast analysis methods weighted with constant and depth sensitivity curves, respectively
2013年,Lee课题组[132]提出一种基于扩散理论的散斑衬比分析方法(DSCA)。如
2021年,Zhu团队[130]提出了透射式激光散斑血流成像系统(TR-LSCI)。如
图 28. 激光散斑血流成像系统[130]。(a)TR-LSCI成像系统;(b)传统的反射式成像系统
Fig. 28. LSCI system for blood flow[130]. (a) TR-LSCI system; (b) conventional reflective-detected LSCI system
4 新型LSCI系统及应用
随着LSCI在信噪比、分辨率、测量精度、成像深度等关键技术方面取得新进展,一系列新型LSCI应用系统应运而生,以适应复杂多样的生物体结构和应用场景。如
图 29. 新型LSCI系统及其应用研究进展
Fig. 29. Novel LSCI systems and their advances in application and research
4.1 便携式LSCI系统
传统的激光散斑血流成像系统部件独立,体积庞大,不能便携。近年来,研究人员使用ARM(advanced RISC machines)[134]、数字信号处理器(DSP)[135]、可编辑门阵列(FPGA)[136]、图形处理器(GPU)[57]等嵌入式处理器和技术开发了便携式LSCI系统[137]。2010年,华中科技大学骆清铭课题组[135]设计了一种基于DSP的便携式LSCI成像系统。如
图 30. 基于DSP的便携式LSCI系统[135]。(a)便携式LSCI系统示意图;(b)硬件框架图;(c)软件架构图
Fig. 30. Portable LSCI based on DSP[135]. (a) Schematic illustration of portable LSCI system; (b) block diagram of hardware framework; (c) block diagram of software framework
2011年,Jiang等[136]充分利用FPGA集成度高、编程灵活、适用范围广的优势,设计了一款基于FPGA架构的便携式LSCI系统。如
GPU具有强大的并行计算能力,这对于图像数据的实时处理具有重要作用。2008年,华中科技大学骆清铭课题组[52]在计算机中加入GPU单元,解决了散斑图像处理比较耗时的问题。该方法利用GPU可编程的优势和高浮点处理能力,实现了高分辨率的实时血流可视化。2011年,Choi课题组[92]提出了一种轻量化便携式集成方法(使用NVIDIA的CUDA框架,在计算机的GPU单元上进行激光散斑图像处理),并将该方法集成到LabVIEW中。该方法充分利用了CUDA框架的灵活性和兼容性,实现了LSCI产品形态的完整性,解决了传统的GPU都是集成在台式机或者笔记本计算机中而不利于携带的问题。此外,Chen等[57]研发了基于嵌入式GPU系统的高性能便携式LSCI系统原型,如
4.2 内窥式LSCI系统
由于近红外光的穿透能力有限,传统的LSCI系统仅能反映浅表血流信息,探测深度小于1 mm[41],对于人体深部组织和器官血流信息的观测无能为力[138]。内窥镜技术的发展为进一步突破成像深度的限制提供了可能,LSCI系统与内窥镜/内窥技术相结合,使得LSCI图像采集模块可以深入到组织内部。
Regan等[88]设计了一个光纤探针并将它与LSCI系统结合,如
Zheng等[14]设计了
图 34. 腹腔镜LSCI双显示成像系统[14]。(a)腹腔镜LSCI成像系统;(b)插入腹腔镜;(c)手持式操作过程;(d)LSCI肠成像;(e)LSCI胆囊成像;(f)LSCI肠系膜成像
Fig. 34. Dual-display laparoscopic laser speckle contrast imaging (LSCI) system[14]. (a) Laparoscopic LSCI system; (b) inserted laparoscopy; (c) handheld operation; (d) LSCI bowel imaging; (e) LSCI gallbladder imaging; (e) LSCI mesentery imaging
4.3 头戴式LSCI系统
在脑认知与行为科学等基础研究领域,一些行为实验往往需要捕获自由移动情况下的小鼠头部脑血流。针对这种应用场景需求,Miao等[60]开发了一种头戴式LSCI图像采集装置,如
4.4 多模态LSCI系统
多模态是新型LSCI技术发展的另一个方向。2010年,Srienc等[8]提出了将LSCI技术与激光扫描共聚焦显微镜相结合的血流监测方法。使用空间限制光刺激视网膜,然后用该组合成像系统监测视网膜血管的直径、血流量和血流速度,分析血管的扩张程度,进而研究视网膜的功能性充血机制。2012年,Qin等[139]提出了双波长激光散斑血流成像与光学显微血管造影相结合的多模态成像系统。该系统能够确定血流量、血红蛋白浓度以及血液脉管系统形态学特征的相对变化,可用于监测烧伤组织的血流动力学和研究形态脉管系统的反应。Pan等[23]采用脑电测量LSCI(ECoG-LSCI)集成系统研究了外周电刺激对缺血性脑卒中的治疗效果。如
图 37. 接受电刺激前后大鼠右脑前肢体感皮层卒中区域的激光散斑血流随时间的变化情况[23]
Fig. 37. Speckle contrast images for rCBF upon electrical stimulation in forelimb- and hindlimb-stimulated groups at serial time points[23]
2022年,Feng等[17]提出了一种集视网膜多光谱成像、视网膜血氧测定和LSCI于一体的多模态眼功能成像技术,如
针对术中应用场景,Kim课题组[141]将可见光成像、LSCI、高光谱成像(HSI)等多种光学成像技术相结合,构建了一个多模态的实时手术导航系统,如
5 结束语
在过去的几十年中,LSCI技术在理论分析、成像系统、散斑图像计算/分析方法以及临床应用上的研究都取得了巨大飞跃,特别是在如何实现高信噪比、高分辨率、高精度、大成像深度等LSCI关键技术的研究方面取得了突破性进展。随着生物医学、临床诊疗、生命科学等基础领域和应用研究领域的应用场景越来越复杂和多元化,成像需求由体表扩展到体内,成像对象的状态由静止变为移动,面向多元应用场景的新型LSCI系统应运而生。新型LSCI系统的发展反过来又进一步推动了其应用研究,LSCI的应用领域从最初的眼部延拓到皮肤、脑皮层、肝肾肠胃等组织和器官微循环血流系统,同时也从最初的临床辅助诊断工具逐渐发展成为智能手术系统的一部分,并逐渐在生命科学、药物研发、脑科学等基础性研究中发挥着更重要的作用。
LSCI技术涵盖了生物医学、光学、计算机、电子信息、物理、数学等多个领域的知识,具有很强的多学科交叉、融合的特点。未来LSCI技术的发展必将进一步与生物医学、光电信息、人工智能、大数据等新兴学科深度融合,并有望在以下几个方面取得新突破:
1)定量分析能力。实现定量分析是LSCI功能性应用的前提,现有的LSCI技术本质上还只能用于定性分析,虽然多曝光成像[83]、DLSI[74]、动态散斑衬比校正模型[51,73,115]、多尺度分解融合[116]等在一定程度上推动了该技术向定量分析方向发展,但它们都基于原有的模型进行误差校正,向血流的绝对速度逼近,并没有从本质上改变激光散斑血流成像技术的定量分析能力。如何建立一个能定量分析的数学模型用于衬比值计算是一个非常具有挑战性的难题。
2)与新型内窥技术相结合(这将使基于LSCI的无创活体血流功能成像成为可能)。近红外光成像技术的成像深度受限,光纤内窥镜的出现在一定程度上帮助人们突破了这个极限,但传统的内窥镜用于组织血流成像依然是有创的。美国约翰斯·霍普金斯大学开发出了一种新型的内窥镜探针,该探针有望实现无创活检[142]。随着内窥镜技术的发展,相信在不久的将来,基于新型内窥技术和LSCI的无创活体血流成像必将成为可能。
3)系统小型化和集成化。新型材料和电子器件的发展必将推动新型LSCI系统小型化与集成化应用。新型柔性电子材料和器件的出现推动了穿戴式医疗设备的发展,使得LSCI系统更加便携和高效,这不仅有利于临床诊疗上的应用,也将进一步促进LSCI在动物行为学研究中的应用,进而推动LSCI技术在神经科学领域,特别是在认知功能障碍相关疾病的动物模型评估、生理机制研究等方面的应用[143-144]。
4)与人工智能相结合。人工智能将进一步促进LSCI技术及其应用的发展。随着人工智能技术的发展,将人工智能与LSCI技术结合是未来发展的重要趋势。利用神经网络模型和深度学习算法的性能优势,能够从散斑图像中提取更多的细节和信息,提升LSCI技术对各种应用场景的信息获取能力和功能分析能力,拓宽其应用的广度和深度。然而,当前深度学习算法的模块化程度较强,在LSCI中的应用大都是横向研究,如何从纵向角度将其与LSCI技术耦合,是亟待研究的问题。
5)与其他成像模式相结合,构建基于LSCI的多模态临床诊断应用新模式。科学上的突破和创新越来越多地依赖多学科的交叉和融合,将LSCI技术与其他光声成像、生物成像、计算成像技术相结合,将促进生物医学的研究和发展。不同的成像方式具有其各自的优点和不足,如:LSCI、光学相干断层成像术(OCT)[145-147]具有较高的时空分辨率,但穿透能力弱;正电子发射型计算机断层显像(PET)[148]、电子计算机断层扫描(CT)[149]、核磁共振成像(MRI)[150]虽然对人体有伤害,但是穿透能力强,成像深度大;传统的医学影像技术,如MRI、CT等,只能提供宏观尺度的结构信息,无法提供微观结构和生物学功能信息;OCT、LSCI、激光多普勒血流成像以及分子成像技术能够提供微观结构和分子的功能信息。采他山之石以攻玉,纳百家之长以厚己。将LSCI技术与光声成像、生物成像、计算成像、分子成像等成像模式相结合,通过多模态成像的方式获得更多维度的机体信息,有望打破LSCI的技术壁垒,进一步拓宽LSCI技术的应用领域。
一枝独秀不是春,百花齐放春满园。相信LSCI未来将呈现一干多支的发展趋势,在技术上协同发展,在应用上百家齐放!
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Article Outline
翟林君, 傅玉青, 杜永兆. 激光散斑衬比血流成像关键技术及应用研究进展[J]. 中国激光, 2023, 50(9): 0907106. Linjun Zhai, Yuqing Fu, Yongzhao Du. Advances in Laser Speckle Contrast Imaging: Key Techniques and Applications[J]. Chinese Journal of Lasers, 2023, 50(9): 0907106.