高灰度级高分辨率激光散斑血流实时成像研究

doi:10.16183/j.cnki.jsjtu.2021.466

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (5): 552-559.doi: 10.16183/j.cnki.jsjtu.2021.466

所属专题：《上海交通大学学报》2023年“生物医学工程”专题

高灰度级高分辨率激光散斑血流实时成像研究

张泽龙¹, 张颖超², 伍波², 董威¹(), 樊友本²

1.上海交通大学中英国际低碳学院,上海 200240
2.上海交通大学医学院附属第六人民医院甲乳疝外科; 上海交通大学甲状腺疾病诊治中心,上海 200233

收稿日期:2021-11-18 修回日期:2022-01-18 接受日期:2022-01-19 出版日期:2023-05-28 发布日期:2023-06-02
通讯作者: 董威 E-mail:wdong@sjtu.edu.cn.
作者简介:张泽龙(1996-),硕士生,从事生物医学光学成像与多相流研究.
基金资助:
中国科学院大学宁波生命与健康产业研究院合作资助项目(2019YJY0201)

Real-Time Laser Speckle Imaging of Blood Flow with High Gray Level and High Resolution

ZHANG Zelong¹, ZHANG Yingchao², WU Bo², DONG Wei¹(), FAN Youben²

1. China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China
2. Department of Thyroid-Breast-Hernia Surgery; Thyroid and Parathyroid Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China

Received:2021-11-18 Revised:2022-01-18 Accepted:2022-01-19 Online:2023-05-28 Published:2023-06-02
Contact: DONG Wei E-mail:wdong@sjtu.edu.cn.

摘要/Abstract

摘要：

激光散斑对比成像是一种大测量范围、实时、高分辨率的光学成像方法.现有研究表明,低灰度级(8位)低分辨率(752像素×480像素)照相机可以有效监测血液等散射介质流动,但散粒噪声大、有效成像区域小等缺点难以通过软件弥补,严重影响成像质量.使用高灰度级(16位)高分辨率(2 048像素×2 048像素)照相机监测血液流动会减慢单帧成像速度,并行计算能使图像处理时间减少1/3.研究借助动物血液流动实验,以成像速度与成像质量为评价标准,对比分析高灰度级高分辨率空间激光散斑对比成像与空间近似激光散斑对比成像(sLSCIa)、时间激光散斑对比成像(tLSCI)的结果.结果表明,高灰度级高分辨率空间激光散斑对比成像并行计算兼顾成像质量与成像速度,可以达到临床实时监测血液流动要求.

关键词: 激光散斑对比成像, 血流监测, 图像处理技术, 并行计算

Abstract:

Laser speckle contrast imaging (LSCI) is a large measurement ranging, real-time, high spatial resolution optical imaging method. Exisiting reserches show that the low gray level (8-bit) and low resolution (752×480 pixels) camera can effectively monitor the flow of scattering media such as blood, but the defects such as large noise of scattered particles and small effective imaging area are difficult to be compensated by software, which seriously affects the imaging quality. Monitoring blood flow with a camera with a high gray level (16-bit) and a high resolution (2 048×2 048 pixels) will slow down the imaging speed of a single frame, and parallel computing can reduce the image processing time by 1/3. With the aid of an animal blood flow experiment, the results of high-gray-level and high-resolution spatial laser speckle contrast imaging (sLSCI), spatial approximate laser speckle contrast imaging (sLSCIa), and temporal laser speckle contrast imaging (tLSCI) were compared and analyzed using imaging speed and imaging quality as evaluation criteria. The parallel computation of high-gray-level and high-resolution sLSCI takes into consideration both imaging quality and imaging speed, which can meet the requirements of clinical real-time monitoring of blood flow.

Key words: laser speckle contrast imaging (LSCI), blood flow monitoring, image processing technology, parallel computing

中图分类号:

Q334

张泽龙, 张颖超, 伍波, 董威, 樊友本. 高灰度级高分辨率激光散斑血流实时成像研究[J]. 上海交通大学学报, 2023, 57(5): 552-559.

ZHANG Zelong, ZHANG Yingchao, WU Bo, DONG Wei, FAN Youben. Real-Time Laser Speckle Imaging of Blood Flow with High Gray Level and High Resolution[J]. Journal of Shanghai Jiao Tong University, 2023, 57(5): 552-559.

图/表 9

图1

图2

图3

图4

表1

兔子死亡前后肠系膜血管各感兴趣区间K的平均值与信噪比

ROI编号	$K - l$	$K - d$	R_SN,l	R_SN,d
ROI₁	0.030 9	0.161 8	5.207 8	2.527 2
ROI₂	0.029 9	0.141 8	4.104 3	3.694 0
ROI₃	0.035 9	0.122 5	4.459 8	2.441 9

表1

图5

图6

表2

不同方法下各感兴趣区间$\bar{K}$的相关系数与SNR的平均值

参数	取值
参数	8位sLSCI	16位sLSCI	tLSCI	tLSCIv	sLSCIa
$K -$ 相关系数	0.910 4	0.970 7	1.000 0	0.915 4	0.915 4
SNR的平均值	4.861 9	6.037 1	5.781 4	5.902 8	1.318 8

表2

图7

参考文献 15

[16]	RAMIREZ-SAN-JUAN J C, MENDEZ-AGUILAR E, SALAZAR-HERMENEGILDO N, et al. Effects of speckle/pixel size ratio on temporal and spatial speckle-contrast analysis of dynamic scattering systems: Implications for measurements of blood-flow dynamics[J]. Biomedical Optics Express, 2013, 4(10): 1883-1889. doi: 10.1364/BOE.4.001883 URL
[17]	BANDYOPADHYAY R, GITTINGS A S, SUH S S, et al. Speckle-visibility spectroscopy: A tool to study time-varying dynamics[J]. Review of Scientific Instruments, 2005, 76(9): 093110. doi: 10.1063/1.2037987 URL
[1]	刘洪涛, 梁振宁, 胡文, 等. 基于局部特征点检测与匹配的微悬臂梁变形受力测量方法[J]. 上海交通大学学报, 2013, 47(12): 1842-1847.
	LIU Hongtao, LIANG Zhenning, HU Wen, et al. A method for deformation and force measurement of micro-cantilever based on local feature point detecting and matching[J]. Journal of Shanghai Jiao Tong University, 2013, 47(12): 1842-1847.
[2]	FERCHER A F, BRIERS J D. Flow visualization by means of single-exposure speckle photography[J]. Optics Communications, 1981, 37(5): 326-330. doi: 10.1016/0030-4018(81)90428-4 URL
[3]	TOWLE E L, RICHARDS L M, KAZMI S M S, et al. Comparison of indocyanine green angiography and laser speckle contrast imaging for the assessment of vasculature perfusion[J]. Neurosurgery, 2012, 71(5): 1023-1030. doi: 10.1227/NEU.0b013e31826adf88 pmid: 22843129
[4]	FREDRIKSSON I, LARSSON M. On the equivalence and differences between laser Doppler flowmetry and laser speckle contrast analysis[J]. Journal of Biomedical Optics, 2016, 21: 126018. doi: 10.1117/1.JBO.21.12.126018 URL
[5]	MANNOH E A, THOMAS G, SOLÓRZANO C C, et al. Intraoperative assessment of parathyroid viability using laser speckle contrast imaging[J]. Scientific Reports, 2017, 7: 14798. doi: 10.1038/s41598-017-14941-5 pmid: 29093531
[6]	MANNOH E A, PARKER L B, THOMAS G, et al. Development of an imaging device for label-free parathyroid gland identification and vascularity assessment[J]. Journal of Biophotonics, 2021, 14(6): e202100008.
[7]	HEEMAN W, STEENBERGEN W, DAM G V, et al. Clinical applications of laser speckle contrast imaging: A review[J]. Journal of Biomedical Optics, 2019, 24(8): 1-11. doi: 10.1117/1.JBO.24.8.080901 pmid: 31385481
[8]	李晨曦, 陈文亮, 蒋景英, 等. 激光散斑衬比血流成像技术研究进展[J]. 中国激光, 2018, 45(2): 92-101.
	LI Chenxi, CHEN Wenliang, JIANG Jingying, et al. Laser speckle contrast imaging on in vivo blood flow: A review[J]. Chinese Journal of Lasers, 2018, 45(2): 92-101.
[9]	RICHARDS G J, BRIERS J D. Capillary-blood-flow monitoring using laser speckle contrast analysis (LASCA): Improving the dynamic range[J]. Proceedings of SPIE, 1997, 2981: 160-171.
[10]	CHENG H Y, YAN Y M, DUONG T Q. Temporal statistical analysis of laser speckle images and its application to retinal blood-flow imaging[J]. Optics Express, 2008, 16(14): 10214-10219. doi: 10.1364/oe.16.010214 pmid: 18607429
[11]	MIAO P, REGE A, LI N, et al. High resolution cerebral blood flow imaging by registered laser speckle contrast analysis[J]. IEEE Transactions on Bio-Medical Engineering, 2010, 57(5): 1152-1157. doi: 10.1109/TBME.2009.2037434 pmid: 20142159
[12]	CHENG W M, ZHU X, CHEN X, et al. Manhattan distance-based adaptive 3D transform-domain collaborative filtering for laser speckle imaging of blood flow[J]. IEEE Transactions on Medical Imaging, 2019, 38(7): 1726-1735. doi: 10.1109/TMI.2019.2896007 pmid: 30714912
[13]	YUAN S. Sensitivity, noise and quantitative model of laser speckle contrast imaging[D]. USA: Medford Tufts University, 2008.
[14]	SONG L P, ELSON D S. Effect of signal intensity and camera quantization on laser speckle contrast analysis[J]. Biomedical Optics Express, 2013, 4(1): 89-104. doi: 10.1364/BOE.4.000089 pmid: 23304650
[15]	HULTMAN M, FREDRIKSSON I, LARSSON M, et al. A 15.6 frames per second 1-megapixel multiple exposure laser speckle contrast imaging setup[J]. Journal of Biophotonics, 2018, 11(2): e201700069.

高灰度级高分辨率激光散斑血流实时成像研究

Real-Time Laser Speckle Imaging of Blood Flow with High Gray Level and High Resolution

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