Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow

Abstract

Although multiple intraoperative cerebral blood flow (CBF) monitoring techniques are currently available, a quantitative method that allows for continuous monitoring and that can be easily integrated into the surgical workflow is still needed. Laser speckle contrast imaging (LSCI) is an optical imaging technique with a high spatiotemporal resolution that has been recently demonstrated as feasible and effective for intraoperative monitoring of CBF during neurosurgical procedures. This study demonstrates the impact of retrospective motion correction on the quantitative analysis of intraoperatively acquired LSCI images. LSCI images were acquired through a surgical microscope during brain tumor resection procedures from 10 patients under baseline conditions and after a cortical stimulation in three of those patients. The patient’s electrocardiogram (ECG) was recorded during acquisition for postprocess correction of pulsatile artifacts. Automatic image registration was retrospectively performed to correct for tissue motion artifacts, and the performance of rigid and nonrigid transformations was compared. In baseline cases, the original images had $25\%\pm27\%$ noise across 16 regions of interest (ROIs). ECG filtering moderately reduced the noise to $20\%\pm21\%$, while image registration resulted in a further noise reduction of $15\%\pm4\%$. Combined ECG filtering and image registration significantly reduced the noise to $6.2\%\pm2.6\%$ ($p<0.05$). Using the combined motion correction, accuracy and sensitivity to small changes in CBF were improved in cortical stimulation cases. There was also excellent agreement between rigid and nonrigid registration methods (15/16 ROIs with $\lt$3% difference). Results from this study demonstrate the importance of motion correction for improved visualization of CBF changes in clinical LSCI images.

Type
Journal article
Publication
Neurophotonics

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