Microcirculation

The perfusion of a tissue depends on the number, distribution, and diameters of the capillaries in combination with blood viscosity and driving pressure across the capillaries. There are two main hemodynamic principles governing how oxygen in red blood cells reaches the tissue cells; the first is the convection based on red blood cell flow, and the second is the diffusion distance oxygen must travel from the red blood cells in the capillaries to the parenchymal cells [19 (link)]. Convection is quantified by measurement of flow in the microvessels, and diffusion is quantified by the density of the perfused microvessels (functional capillary density).
Subsequent image analysis was performed using microvascular density (total or perfused vessel density) and microvascular perfusion (proportion of perfused vessels and microcirculatory flow index) parameters in line with international consensus [22 (link)]. Software assisted analysis (AVA 3.0; Automated Vascular Analysis, Academic Medical Center, University of Amsterdam) was used on the images [20 (link)]. The analysis of the microvascular density was restricted to vessels with a diameter <20 μm.
The total vessel density (TVD; mm/mm²) was determined using the AVA software. A semiquantitative analysis previously validated [23 (link)] but assisted by the AVA software was performed in individual vessels that distinguished among no flow (0), intermittent flow (1), sluggish flow (2), and continuous flow (3). A value was assigned for each vessel. The overall score, called the microvascular flow index (MFI), is the average of the individual values [24 (link)]. The proportion of perfused vessels (PPV) was calculated as the number of vessels with flow values of 2 and 3 divided by the total number of vessels. Perfused vessel density (PVD) was determined as the total vessel density multiplied by the fraction of perfused vessels [22 (link)]. Analyses of all images were done off-line and blinded to the investigators.

In the present study, a commercially available LSFG system (LSFG-NAVI; Softcare Co., Ltd., Fukuoka, Japan) was used to measure ocular blood flow at the ONH. The principles of LSFG have been previously described in detail.[27 (link)–31 (link)] In short, when a rough surface is illuminated with a coherent light source (e.g. a laser) the backscattered light gives the appearance of a consistent scatter pattern (i.e. the speckle pattern). Moving particles (e.g. corpuscular blood components) within the field of view cause a distinct variation in the speckle pattern in the form of a decrease in the speckle contrast and the speckle variation. After acquisition of the speckle pattern with a digital camera, these fluctuations in the speckle pattern can be analyzed in order to generate flow information. The LSFG device used in the present study consists of a fundus camera equipped with a diode laser with a wavelength of 830 nm and a digital charge-coupled device camera (750 x 360 pixels). The primary output parameter of LSFG, mean blur rate (MBR), constitutes a measure of relative blood flow velocity and is expressed in arbitrary units (AU). A total of 118 images are continuously acquired at a rate of 30 frames per second with an exposure time of 1/500 seconds over a time period of approximately 4 seconds. Accompanying analysis software (LSFG Analyzer, Version 3.1.58; Softcare Co., Ltd.) automatically detected the beginning and the end of the cardiac cycles recorded within the 4 seconds acquisition time. Images corresponding to the identical phases of the cardiac cycle were normalized to one image sequence depicting a complete cardiac cycle. The average signal intensity within each of the 750 x 360 pixels over the entire cardiac cycle was calculated in order to render the so called “composite map” depicting the distribution of mean blood flow during one cardiac cycle in the ocular fundus (Fig 1a). In this color-coded map, the ONH area is to be manually delineated by positioning an ellipsoid region of interest at the ONH margin using the image analysis software. In the present study, identical position and size of the band was maintained in all subsequent scans of the same subject using the “follow up scan” function of the software. After image acquisition, vessel and tissue areas within the ONH area were automatically detected by the software using the so-called “vessel extraction” function (Fig 1b and 1c). Thereby, a threshold for MBR signal intensity is automatically calculated by using digital cross-section analysis in order to discriminate between visible surface vessels and ONH tissue areas. Thus, MBR can be either determined for the total ONH area (referred to as MA, “mean MBR of all area”) or, separately, for vessel (MV, “mean MBR of vascular area”) and tissue areas (MT, “mean MBR of tissue area”).
In addition, the LSFG Analyzer software provides numerous parameters characterizing the shape of the MBR waveform during one cardiac cycle (“pulse-waveform analysis”) for assessment of the dynamics of ocular blood flow.[32 (link)–34 (link)] These additional parameters can be separately calculated for MV (corresponding to the large vessels within the ONH area) or for MT (corresponding to the ONH microvasculature). Definitions and equations for the calculation of these pulse-waveform parameters is provided in detail in Fig 2. In the present study, all pulse-waveform analyses were based on the pulse waveform obtained for the ONH microcirculation (MT).

Patients were given TACE using cytotoxic drugs as determined by a local multi-disciplinary team in accordance with the recommendations of the European/American Association for Liver Disease guidelines [18 (link), 19 (link)].
Conventional TACE was performed through femoral access under moderate sedation using the Seldinger technique [20 (link)]. To cause embolization of the tumour microcirculation, cytotoxic drugs or chemotherapeutic agents suspended in lipiodol were administrated into the tumour-feeding artery with a dose ranging from 5 to 30 mL depending on the location, the size, and the number of lesions. If necessary, gelatin sponge particles (150–350 μm) were injected to block the blood until the flow was static.

Sublingual microcirculation was analyzed using sidestream dark field (SDF) imaging (Microscan, MicroVision, Amsterdam, The Netherlands). In brief, imaging of sublingual microcirculation was performed at the site of interest. SDF imaging utilizes a concentric arrangement of light-emitting diodes (LEDs) to enhance contrast and to reduce blurry imaging [36 (link)]. The resulting image is a gray and white video with dark moving speckles representing RBC flow. The light from the device is absorbed within the RBCs, causing the dark and high-contrast spheres [37 (link)]. The De Backer score, the diameter distribution, and the velocities were determined using the Automated Vascular Analysis software (AVA, Microscan, MicroVision, Amsterdam, The Netherlands). For each volunteer, videomicroscopy recordings of three to six sublingual sites were analyzed. The sites analyzed were determined based on the video quality obtained, as patients found it difficult to hold their tongue still. Furthermore, to avoid data inaccuracies, the operator maintained constant pressure on the Microscan handheld device, which requires dexterity and practice. The video of each site was approximately 10 s long, and the most stable sequence of frames was extracted for analyses.
The De Backer score is a measurement of the vessel density and is determined by counting the number of vessels crossing arbitrary horizontal and vertical lines [20 (link)] as shown in Figure 4. It is calculated by dividing the number of vessels crossing the line by the length of the line.
The microvessels were classified by size and were placed in 14 groups from 2–4 µm to 28–30 µm in diameter. The proportion of vessels in each group size was characterized by the proportion of cumulative length. The proportion of cumulative length is defined by the AVA software used as the sum of the lengths of the vessels in the given group size divided by the total length of all the vessels of all sizes included. For each individual, the data from the different recordings were cumulated.
The local velocity in each vessel was determined using AVA software based on kymographs. Kymographs are plots representing spatial position as a function of the time used to quantify velocity along a determined path [38 (link)]. Kymographs were plotted for each detected vessel along the centerline [8 (link)]. The resulting velocity vectors were individually visually validated by the operator. For each subject, the velocities were averaged for each vessel group size.