Background: Handheld laser speckle contrast imaging (LSCI) plays a vital role in clinical environments; however, motion artifacts (MA) can undermine the reliability of perfusion images. Existing methods for preventing and suppressing MA are often impractical or overly complex. While machine vision techniques show promise in enhancing medical imaging quality, their application in mitigating MA remains largely unexplored.
Objective: This study introduces an innovative linear regression-based method for motion artifact correction (MAC) in LSCI, validated through measurements on psoriasis patients [1].
Methods: We conducted paired handheld and mounted LSCI measurements on 14 psoriasis lesions using the handheld perfusion imager (HAPI), previously validated for eye safe and user friendly clinical investigation [2]. By delineating lesion boundaries for clinical assessment, the HAPI utilized a monochromatic camera for both speckle imaging and motion detection, thus simplifying hardware requirements [3]. We accurately estimated the relative displacements between the test object and the LSCI probe, enabling the application of MAC to the perfusion images by correlating the calculated speed with local perfusion.
Results: Linear regression of spatial perfusion and on-surface speed extrapolated the zero-speed point as the predicted motion artifact-corrected value for each location. Based on this correction, the discrepancy in mean perfusion between handheld and mounted modes significantly decreased (median error of 14.2 perfusion units (p.u.) on lesions before correction (p<0.0005), compared to 0.5 p.u. after correction (p=0.2)).
Conclusions: Our findings support the efficacy of handheld LSCI and validate our MAC approach in a psoriasis context. We address one of the two primary causes of MA—on-surface speeds—and successfully correct mean perfusion, assuming constant temporal perfusion at each site.
Significance: We present a practical, non-contact, marker-free technique for reliable handheld perfusion imaging, paving the way for enhanced clinical applications in dermatology, burn treatment, and plastic surgery [4].

References:
[1] A. Chizari, et al., “Mitigation of Motion Artifacts in Handheld Laser Speckle Contrast Imaging Illustrated on Psoriasis Lesions,” IEEE Trans Biomed Eng, pp. 1–9, 2024, doi: 10.1109/TBME.2024.3438375.
[2] M. J. Schaap et al., “Perfusion measured by laser speckle contrast imaging as a predictor for expansion of psoriasis lesions,” Skin Research and Technology, 2021, doi: 10.1111/srt.13098.
[3] A. Chizari, et al., “Handheld versus mounted laser speckle contrast perfusion imaging demonstrated in psoriasis lesions,” Sci Rep, vol. 11, no. 1, p. 16646, Aug. 2021, doi: 10.1038/s41598-021-96218-6.
[4] A. Rook, et al., “Handheld wireless laser speckle contrast imaging (LSCI) during DIEP flap breast reconstruction: a pilot study,” in Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics, J. S. Baba and G. L. Coté, Eds., SPIE, Mar. 2024, p. 18. doi: 10.1117/12.3001925.

Laser speckle contrast imaging (LSCI) is an optical technique for noninvasive assessment of microcirculatory blood flow. LSCI has a broad application in medicine including dermatology. Since laser speckles are the basis for this imaging modality, any external motions during a measurement from both patient and operator affect the blood flow images. This challenge is called motion artefacts (MA). Here, we propose a complete procedure for analysis of speckles, that is, pre-segmentation, segmentation, motion detection, spatial alignment, perfusion map calculation and MA suppression. The handheld perfusion imager (HAPI) operated in both handheld and mounted schemes, has been used for measurements on 14 psoriasis subjects. The advantage of HAPI is use of a single monochromatic camera for both speckle imaging and motion detection. We make use of the black marker dots (made by the clinical investigator to determine visual psoriasis lesion boundary) for calculation of two-dimensional displacements of HAPI during each measurement (i.e. on-surface displacements). These on-surface displacements are integrated to translate each speckle image back to the initial position at the start of the measurement (i.e. spatial alignment). Furthermore, in handheld measurements, MA corrected blood flow maps (also called perfusion maps) are formed by extrapolation of a linear fit from local perfusion versus detected speed to the zero speed, that is, a value ideally always lower than the local mean perfusion. We show that our MA suppression technique makes handheld perfusion maps more similar to the associated mounted perfusion maps in term image histograms and mean values.

We assessed the reliability of handheld laser speckle contrast perfusion imaging by evaluating mounted/handheld measurement pairs operated on psoriasis lesions in three steps. First, we made a denoised perfusion map per measurement based on spatial alignment of raw speckle frames and temporal averaging of perfusion frames. Second, we used the measured on-surface speed information to compensate the movement-induced perfusion by extrapolation of the local perfusion values to the value corresponds to zero on-surface speed. Third, we compared mounted/handheld measurement pairs based on perfusion inhomogeneity and increased perilesional perfusion criteria independent of the movement artefact compensation mentioned in the second step. We conclude that after proper post-processing, handheld LSCI measurements can be as reliable as mounted measurements in terms of geometrical distorting, but with challenges to be overcome for correcting perfusion values.