Laser speckle contrast imaging (LSCI) is a non-invasive method for assessment of microcirculatory blood flow. The technique is based on analysis of speckle patterns to build 2D maps of perfusion with high spatial and temporal resolution. A drawback of the method is that it is highly sensitive to motion artifacts since the perfusion estimates are based on quantification of the motion blurring in the images. The camera is today limited to a bulky stand for good measurements, but even as it is fixed, it does not ensure that the patient is completely still. In many clinical settings, it would be advantageous to have a more flexible camera and to be able to detect if an image is influenced by external motion. Multi-exposure laser speckle contrast imaging (MELSCI) is an extension to LSCI that utilizes the contrast from multiple exposure times. The gain in information has paved way for more accurate perfusion estimates. The technique has been limited due to its computational complexity, but recently a real time system has been developed. The goals of this thesis was twofold, firstly find a quantifiable measure of motion artifacts to be able to evaluate and compare LSCI and MELSCI. Secondly, propose an algorithm that detects movements in LSCI recordings. Motion artifacts in LSCI and MELSCI were investigated by developing a setup where repeatable movements could be made. Measurements of a hand influenced by motions of different speeds and directions were acquired and the relative difference between motion and static states were calculated and compared for the two systems. The relative difference of the MELSCI measurements were lower for all speeds above 0.57 mm/s, indicating more robustness to motion artifacts. A detection algorithm using image registration to calculate the instantaneous speed in each frame of the recording was developed. The method successfully detects movements perpendicular to the camera and shows that the intensity images of an LSCI recording can be used to give a direct indication of when movement has occurred.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-315038 |
| Date | January 2022 |
| Creators | Amphan, Dennis |
| Publisher | KTH, Skolan för kemi, bioteknologi och hälsa (CBH) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-CBH-GRU ; 2022:105 |
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