Correcting for motion artifact in handheld laser speckle images

Abstract

Laser speckle imaging (LSI) is a wide-field optical technique that enables superficial blood flow quantification. LSI is normally performed in a mounted configuration to decrease the likelihood of motion artifact. However, mounted LSI systems are cumbersome and difficult to transport quickly in a clinical setting for which portability is essential in providing bedside patient care. To address this issue, we created a handheld LSI device using scientific grade components. To account for motion artifact of the LSI device used in a handheld setup, we incorporated a fiducial marker (FM) into our imaging protocol and determined the difference between highest and lowest speckle contrast values for the FM within each data set (Kbest and Kworst). The difference between Kbest and Kworst in mounted and handheld setups was 8% and 52%, respectively, thereby reinforcing the need for motion artifact quantification. When using a threshold FM speckle contrast value (KFM) to identify a subset of images with an acceptable level of motion artifact, mounted and handheld LSI measurements of speckle contrast of a flow region (KFLOW) in in vitro flow phantom experiments differed by 8%. Without the use of the FM, mounted and handheld KFLOW values differed by 20%. To further validate our handheld LSI device, we compared mounted and handheld data from an in vivo porcine burn model of superficial and full thickness burns. The speckle contrast within the burn region (KBURN) of the mounted and handheld LSI data differed by <4 % when accounting for motion artifact using the FM, which is less than the speckle contrast difference between superficial and full thickness burns. Collectively, our results suggest the potential of handheld LSI with an FM as a suitable alternative to mounted LSI, especially in challenging clinical settings with space limitations such as the intensive care unit.

Keywords: blood flow; coregistration; fiducial marker; handheld; image alignment; laser speckle imaging; wide-field imaging.

Fig. 1

Assembled handheld LSI device. (a) Fully assembled device and (b) device in a tripod-mounted setup.

Fig. 2

FM included into imaging protocol allows for motion artifact detection based on speckle contrast ($K$). (a) The 18% gray card used in our imaging protocol was placed in the lower left corner of all frames during data acquisition to allow for sorting based on motion artifact and image alignment when calculating average speckle contrast images. Here, the FM was placed on the surface of a flow phantom. The FM was identified and the speckle contrast value of the FM ($K_{\mathrm{FM}}$) was quantified and plotted in descending order for all images within each data set. (b) The handheld $K_{\mathrm{FM}}$ values plotted show a rapid decline within a representative sorted handheld data set. The $K_{\mathrm{FM}}$ mounted values plotted remain relatively stable across all images within a sorted mounted data set. (c) The table shows candidate values of $K_{\mathrm{FM},\mathrm{thres}}$, the percent difference between each of these values and the known $K_{\mathrm{FM}}$ value, and the number of images in the handheld data set that exceeds each of the $K_{\mathrm{FM},\mathrm{thres}}$ values.

Fig. 3

$K$ values of handheld and mounted setups from in vitro flow phantom experiments. (a) With an unsupervised approach, use of all 150 handheld LSI images resulted in an error of up to 20%. (b) When using the FM to select a subset of images with a minimum $K_{\mathrm{FM}}$ value to account for motion artifact and to realign images, the accuracy of handheld LSI improves, with errors of 8% and 5% for $K_{\mathrm{FM}}$ values of 0.40 and 0.45, respectively. (c) Bland–Altman plot of $K_{\mathrm{FM}}$ data collected using $K_{\mathrm{FM},\mathrm{thres}}=0.45$. We observed a systematic bias of $-0.0089$ between the two measurement approaches (95% confidence limits of $\text{agreement}=-0.023$ to 0.0047. (d) Bland–Altman plot of $K_{\mathrm{FM}}$ data collected using $K_{\mathrm{FM},\mathrm{thres}}=0.40$. We observed a systematic bias of $-0.014$ between the two measurement approaches (95% confidence limits of $\text{agreement}=-0.031$ to 0.0029).

Fig. 4

Mounted versus handheld speckle contrast of superficial and full thickness burns induced on a porcine model. For both the mounted and handheld data sets, we applied $K_{\mathrm{FM},\mathrm{thres}}=0.40$. (a, b) Average $K$ image of sequence of mounted LSI images of a full-thickness burn wound, (a) without and (b) with the use of the FM for alignment and thresholding. Within the image, the larger circular region is the burn region, and the smaller four circular regions are biopsy points taken over the course of the study. (c) Day 1 $K_{\mathrm{BURN}}$ data were obtained at $\sim 24\mathrm{h}$ postburn. The difference in $K_{\mathrm{BURN}}$ between thresholded mounted and handheld data sets was 4%, and the difference between superficial and full thickness burns was 27%. (d) Day 4 $K_{\mathrm{BURN}}$ data were acquired 4 days postburn, when the burn wounds have stabilized. The difference in $K_{\mathrm{BURN}}$ between thresholded mounted and handheld data sets was 1%, and the difference between superficial and full thickness burns was 32%. The uncorrected handheld data reported $K_{\mathrm{BURN}}$ values $\sim 20\%$ lower than those reported from handheld data corrected with the use of the FM.