Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning

Abstract

Significance: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality.

Aim: To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images.

Approach: An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation.

Results: Speed-resolved perfusion was estimated in the three speed intervals 0 to 1 mm / s , 1 to 10 mm / s , and > 10 mm / s , with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures.

Conclusions: The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique.

Keywords: artificial neural networks; blood flow; microcirculation; multi-exposure laser speckle contrast imaging.

© 2023 The Authors.

Figures

Fig. 1

Normalized histograms of skin parameter distributions in the tissue models. The x-axes are scaled to remove the 1% upper tail.

Fig. 2

Examples of RBC speed distributions cRBC(v), for three different mean speeds as presented in the legend. The distributions were normalized to have a total probability of 1.

Fig. 3

The top row shows perfusion predicted by the ANN trained on the restricted tissue model compared to true perfusion [%RBC × mm/s]. The second row shows the same for the main model. The data were divided into 50 bins, each with 2000 data points, based on the true perfusion (x-axis). The black lines are the mean predicted perfusion in the bins, and the blue areas indicate the average deviation from the mean in each bin. The red dashed line is the ideal theoretical prediction. Summary metrics weighted mean absolute percentage error (wMAPE) for speed-resolved perfusion, mean absolute percentage error (MAPE) for total perfusion, as well as R2, is presented for both models. The histograms show the distribution of true perfusion in the dataset.

Fig. 4

Predicted single-exposure perfusion PSE(T) [PU] evaluated against true total perfusion (%RBC × mm/s). To allow comparison between the arbitrary perfusion units and absolute perfusion units, the predicted values were normalized to have the same average as true perfusion in the displayed range. As in Fig. 3, the histograms show the distribution of true perfusion in the dataset.

Fig. 5

Relative change in perfusion estimates due to a change in (a) blood tissue fraction and (b) mean blood speed. Changes are relative to perfusion estimates at (a) 0.55% and (b) 1 mm/s, respectively. Each point is the median change in 5000 tissue models randomly selected from the test dataset. An ideal method should follow the dashed line. Note the log-log scales.

Fig. 6

Relative change in perfusion estimates due to a change in (a) reduced scattering coefficient, (b) vessel diameter, and (c) epidermis thickness, indicating the largest sources of error in the absolute value of the estimated perfusion. Changes are relative to (a) 1.6  mm−1, (b) 0.055 mm, and (c) 0.205 mm, respectively. Each point is the median change in 5000 tissue models randomly selected from the test dataset. An ideal method should follow the dashed line.

Fig. 7

Intensity image of the forearm presented in the occlusion-release experiment. The large black ROI was used to automatically determine the color scale for the perfusion images in Figs. 8 and 9, and for studying spatial variations in the perfusion. The red and blue ROIs were used to extract the time traces in Figs. 10 and 11 in a high-flow and low-flow region, respectively.

Fig. 8

In-vivo measurement of a forearm during an occlusion-release provocation. Columns represent the three phases of the provocation; baseline, occlusion, and reperfusion. Rows represent the three speed components and total perfusion. The color scale in each image was selected for all images to be visually comparable, using three times the mean perfusion in the large ROI in Fig. 7. Perfusion scales in the unit %RBC × mm/s are displayed in the colorbar below each image. The high perfusion area in the upper left corner of the images is an artifact due to a low intensity. Similarly, the visually high perfusion at the edges of the arm in the occlusion images is due to the low perfusion scale in combination with the low intensity.

Fig. 9

Perfusion estimates based on single-exposure LSCI using the inverse contrast model described in Eq. (6), for exposure times 1, 8, and 64 ms. The color scale in each image was selected for all images to have the same apparent mean, based on the perfusion in the large ROI in Fig. 7. Perfusion values in arbitrary units are presented in the colorbars below each image.

Fig. 10

Speed-resolved perfusion in (a) a low-flow ROI and (b) high-flow ROI during the occlusion-release provocation (baseline 0 to 5 min, occlusion 5 to 10 min, reperfusion 10 to 15 min). Care was taken to place the ROIs (see Fig. 7) outside any large vessels visible in the perfusion and intensity images. Zoom plots show 12 s during each of the three phases of the provocation.

Fig. 11

Single-exposure perfusion PSE(T) at 1, 8, and 64 ms exposure time, for the same ROIs as Fig. 9. The data were baseline-normalized to enable comparison between the different exposure times.