When laser light illuminates a diffuse object, it produces a random interference effect known as a speckle pattern. If there is movement in the object, the speckles fluctuate in intensity. These fluctuations can provide information about the movement. A simple way of accessing this information is to image the speckle pattern with an exposure time longer than the shortest speckle fluctuation time scale—the fluctuations cause a blurring of the speckle, leading to a reduction in the local speckle contrast. Thus, velocity distributions are coded as speckle contrast variations. The same information can be obtained by using the Doppler effect, but producing a two-dimensional Doppler map requires either scanning of the laser beam or imaging with a high-speed camera: laser speckle contrast imaging (LSCI) avoids the need to scan and can be performed with a normal CCD- or CMOS-camera. LSCI is used primarily to map flow systems, especially blood flow. The development of LSCI is reviewed and its limitations and problems are investigated.

Model based data analysis of diffuse reflectance spectroscopy data enables the estimation of optical and structural tissue parameters. The aim of this study was to present an inverse Monte Carlo method based on spectra from two source-detector distances (0.4 and 1.2 mm), using a multilayered tissue model. The tissue model variables include geometrical properties, light scattering properties, tissue chromophores such as melanin and hemoglobin, oxygen saturation and average vessel diameter. The method utilizes a small set of presimulated Monte Carlo data for combinations of different levels of epidermal thickness and tissue scattering. The path length distributions in the different layers are stored and the effect of the other parameters is added in the post-processing. The accuracy of the method was evaluated using Monte Carlo simulations of tissue-like models containing discrete blood vessels, evaluating blood tissue fraction and oxygenation. It was also compared to a homogeneous model. The multilayer model performed better than the homogeneous model and all tissue parameters significantly improved spectral fitting. Recorded in vivo spectra were fitted well at both distances, which we previously found was not possible with a homogeneous model. No absolute intensity calibration is needed and the algorithm is fast enough for real-time processing.

Microcirculatory blood flow can be measured using a laser Doppler flowmetry (LDF) probe. However, the readings are affected by the tissue’s optical properties (absorption and scattering coefficients, µa and µs ) and probe geometry. In this study the influence of optical properties [µa?(0.053,0.23) mm-1,µs?(14.7,45.7) mm-1] on LDF perfusion and LDF sampling depth was evaluated for different fiber separations. In vitro measurements were made on a sophisticated tissue phantom with known optical properties that mimicked blood flow at different depths. Monte Carlo simulations were carried out to extend the geometry of the tissue phantom. A good correlation between measured and simulated data was found. The simulations showed that, for fixed flow at a discrete depth, the influence of µs or µa on LDF perfusion increased with an increase in flow depth and decreased with an increase in fiber separation. For a homogeneous flow distribution, however, the perfusion varied 40% due to variations in the optical properties, almost independent of the fiber separation (0.23–1.61 mm). Therefore, the effect in real tissue is likely to vary due to the unknown heterogeneous blood flow distribution. Further, the LDF sampling depth increased with a decrease in µs or µa and an increase in fiber separation. For fiber separation of 0.46 mm, the e-1 sampling depth ranged from 0.21 to 0.39 mm.

The tissue fraction of red blood cells (RBCs) and their oxygenation and speed-resolved perfusion are estimated in absolute units by combining diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF). The DRS spectra (450 to 850 nm) are assessed at two source–detector separations (0.4 and 1.2 mm), allowing for a relative calibration routine, whereas LDF spectra are assessed at 1.2 mm in the same fiber-optic probe. Data are analyzed using nonlinear optimization in an inverse Monte Carlo technique by applying an adaptive multilayered tissue model based on geometrical, scattering, and absorbing properties, as well as RBC flow-speed information. Simulations of 250 tissue-like models including up to 2000 individual blood vessels were used to evaluate the method. The absolute root mean square (RMS) deviation between estimated and true oxygenation was 4.1 percentage units, whereas the relative RMS deviations for the RBC tissue fraction and perfusion were 19% and 23%, respectively. Examples of in vivo measurements on forearm and foot during common provocations are presented. The method offers several advantages such as simultaneous quantification of RBC tissue fraction and oxygenation and perfusion from the same, predictable, sampling volume. The perfusion estimate is speed resolved, absolute (% $RBC×mm/s$), and more accurate due to the combination with DRS.

Diagnostic optical modalities detect and analyze backscattered photons from illuminated tissue. The common principle for these techniques is that the detected light contains information on tissue scattering and absorption from the interrogated tissue volume. To untangle this information, one needs to understand how the optical properties are related to photon propagation in tissue. Therapeutic modalities on the other hand, depositing light energy in tissue, are also influenced by the means for light delivery and tissue light transport governed by the optical properties.