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Intraoperative video-rate hemodynamic response assessment in human cortex using snapshot hyperspectral optical imaging.

ABSTRACT: Using light, we are able to visualize the hemodynamic behavior of the brain to better understand neurovascular coupling and cerebral metabolism. In vivo optical imaging of tissue using endogenous chromophores necessitates spectroscopic detection to ensure molecular specificity as well as sufficiently high imaging speed and signal-to-noise ratio, to allow dynamic physiological changes to be captured, isolated, and used as surrogate of pathophysiological processes. An optical imaging system is introduced using a 16-bands on-chip hyperspectral camera. Using this system, we show that up to three dyes can be imaged and quantified in a tissue phantom at video-rate through the optics of a surgical microscope. In vivo human patient data are presented demonstrating brain hemodynamic response can be measured intraoperatively with molecular specificity at high speed.

SUBMITTER: Pichette J

PROVIDER: S-EPMC5061108 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Intraoperative video-rate hemodynamic response assessment in human cortex using snapshot hyperspectral optical imaging.

Pichette Julien J Laurence Audrey A Angulo Leticia L Lesage Frederic F Bouthillier Alain A Nguyen Dang Khoa DK Leblond Frederic F

Neurophotonics 20161012 4

Using light, we are able to visualize the hemodynamic behavior of the brain to better understand neurovascular coupling and cerebral metabolism. <i>In vivo</i> optical imaging of tissue using endogenous chromophores necessitates spectroscopic detection to ensure molecular specificity as well as sufficiently high imaging speed and signal-to-noise ratio, to allow dynamic physiological changes to be captured, isolated, and used as surrogate of pathophysiological processes. An optical imaging system ...[more]

PMID: 27752519

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Project description:IntroductionThe eye offers potential for the diagnosis of Alzheimer's disease (AD) with retinal imaging techniques being explored to quantify amyloid accumulation and aspects of neurodegeneration. To assess these changes, this proof-of-concept study combined hyperspectral imaging and optical coherence tomography to build a classification model to differentiate between AD patients and controls.MethodsIn a memory clinic setting, patients with a diagnosis of clinically probable AD (n = 10) or biomarker-proven AD (n = 7) and controls (n = 22) underwent non-invasive retinal imaging with an easy-to-use hyperspectral snapshot camera that collects information from 16 spectral bands (460-620 nm, 10-nm bandwidth) in one capture. The individuals were also imaged using optical coherence tomography for assessing retinal nerve fiber layer thickness (RNFL). Dedicated image preprocessing analysis was followed by machine learning to discriminate between both groups.ResultsHyperspectral data and retinal nerve fiber layer thickness data were used in a linear discriminant classification model to discriminate between AD patients and controls. Nested leave-one-out cross-validation resulted in a fair accuracy, providing an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [0.60-0.89]). Inner loop results showed that the inclusion of the RNFL features resulted in an improvement of the area under the receiver operating characteristic curve: for the most informative region assessed, the average area under the receiver operating characteristic curve was 0.70 (95% confidence interval [0.55, 0.86]) and 0.79 (95% confidence interval [0.65, 0.93]), respectively. The robust statistics used in this study reduces the risk of overfitting and partly compensates for the limited sample size.ConclusionsThis study in a memory-clinic-based cohort supports the potential of hyperspectral imaging and suggests an added value of combining retinal imaging modalities. Standardization and longitudinal data on fully amyloid-phenotyped cohorts are required to elucidate the relationship between retinal structure and cognitive function and to evaluate the robustness of the classification model.

Dataset Information

Intraoperative video-rate hemodynamic response assessment in human cortex using snapshot hyperspectral optical imaging.

Publications

Intraoperative video-rate hemodynamic response assessment in human cortex using snapshot hyperspectral optical imaging.

Similar Datasets

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