Optical Coherence Tomography: Basic Concepts and Applications in Neuroscience Research

Abstract

Optical coherence tomography is a micrometer-scale imaging modality that permits label-free, cross-sectional imaging of biological tissue microstructure using tissue backscattering properties. After its invention in the 1990s, OCT is now being widely used in several branches of neuroscience as well as other fields of biomedical science. This review study reports an overview of OCT's applications in several branches or subbranches of neuroscience such as neuroimaging, neurology, neurosurgery, neuropathology, and neuroembryology. This study has briefly summarized the recent applications of OCT in neuroscience research, including a comparison, and provides a discussion of the remaining challenges and opportunities in addition to future directions. The chief aim of the review study is to draw the attention of a broad neuroscience community in order to maximize the applications of OCT in other branches of neuroscience too, and the study may also serve as a benchmark for future OCT-based neuroscience research. Despite some limitations, OCT proves to be a useful imaging tool in both basic and clinical neuroscience research.

Figures

Figure 1

Schematic of a generic time domain OCT system (reprinted with permission from [4]).

Figure 2

In vivo imaging of mouse brain subcortical regions noninvasively through the thinned skull by deep focusing using 1.7 μm OCT. (a) A maximum intensity projection of a series of cross-sectional images shows subcortical structures, including the hippocampus proper. (b) Microvasculature in deep white matter regions is visualized using an OCT angiography method and a maximum intensity projection. Scale bars: 0.2 mm (reprinted with permission from [38]).

Figure 3

3D ODT images of CBFv in mouse somatosensory cortex due to acute cocaine exposure. (a) Dynamic changes of 3D CBF networks (FOV: 3 × 3 × 1.8 mm3) in mouse somatosensory cortex in response to acute cocaine (30 mg/kg, i.p.). (b) En face and cross-sectional projections to show the flow dynamics in different vessels (e.g., 1–6) after cocaine. (c) Time-lapse CBF change (ΔCBF%) to track the dynamics of different vascular compartments (e.g., arterioles and venules) in response to cocaine (reprinted from [47]).

Figure 4

Details of the Δp response in a stained squid nerve. (a) Action potential; (b) |Δp| response at ∼8 ms is given by averaging 90 trials (N = 90) and (c) 10 trials (N = 10). In (b) and (c), arrow indicates the turning point of the galvanometer, and pixel numbers are given on the x-axis and y-axis. Scale bars: horizontal, 10 μm; vertical, 100 μm. Signal traces in time (d, e) are given for selected pixels (lateral index, depth index), which are marked by the blue circles in (b), with averaging 90 and 10 trials. Action potential traces in blue are for guidance (reprinted with permission from [62]).

Figure 5

Details of the Δp response in an unstained squid nerve. (a) Action potential recording; (b) |Δp| image at ∼10.5 ms and (c) Δp traces of selected pixels from an unstained nerve with an averaging of 90 trials. Arrow in (b) indicates the turning point of the galvanometer mirror; scale bars: horizontal, 10 μm; vertical, 100 μm. Pixel coordinates in (c) are in the form of (lateral index, depth index), and the locations are marked by blue circles in (b). The blue trace is the action potential recording for the registering time (reprinted with permission from [62]).

Figure 6

High-resolution images of the internal retinal structure taken with optical coherence tomography (OCT). (a) Low-coherence infrared light is transmitted into the eye through use of an interferometer. (b) The infrared light is transmitted through the pupil and then penetrates through the nine transparent layers of the retina. (c) A fundus image from the optical coherence tomography (OCT) device showing the optic disc appropriately centered and surrounded by the target image circumference marker for analysis of the retinal nerve fiber layer (reprinted with permission from [64]).

Figure 7

Comparison between regions where AAA is relatively stronger or weaker. (a–c) DOMAG results for (a) basal, (b) during-MCAO, and (c) after-reperfusion conditions, respectively. Strong AAA area is marked with a yellow dashed box and weak AAA area with a blue dashed box. (d–f) OMAG comparison between strong and weak AAA ROIs for (d) basal, (e) during-MCAO, and (f) after-reperfusion conditions, respectively. (g–i) Red, green, and yellow dots correspond to MCA, ACA, and AAA T-junction sourced arterioles, respectively. Blue dots correspond to the diving arterioles that are at nondetectable level compared to basal condition. Cartoon representations of the lumen diameters of pial and penetrating arterioles for (g) basal, (h) during-MCAO, and (i) after-reperfusion conditions, respectively. Scale bar is 0.3 mm (reprinted with permission from [77]).

Figure 8

In vivo brain cancer imaging in a mouse with patient-derived high-grade brain cancer (GBM272). (a and b) Brain tissues were imaged in vivo in mice (n = 5) undergoing brain cancer resection. After imaging, the mice were sacrificed and their brains were processed for histology. Here, we show the representative results of a mouse brain at the cancer site before surgery (a) and at the resection cavity after surgery (b). (c) Corresponding histology for the resection cavity after surgery was also shown. (d and e) With the same mouse, control images were imaged at a seemingly healthy area on the contralateral, left side of the brain (d), with its corresponding histology (e). The red circle indicates cancer, gray circle indicates resection cavity, and square was the OCT FOV. 2D optical property maps were displayed using an attenuation threshold of 5.5 mm−1. C, cancer; W, noncancer white matter; M, noncancer meninges. Aliasing artifacts at the image boundaries, which were produced when dorsal structures from outside the OCT depth were folded back into the image, were cropped out of image. 3D volumetric reconstructions were overlaid with optical property maps on the top surface. Optical attenuation properties were averaged for each subvolume of 0.326 mm × 0.008 mm × 1.8 mm within the tissue block, with a step size of 0.033 mm in the x direction. Each histological image (c and e) represented a cross-sectional view of the tissue block: the image corresponds to a single perpendicular slice through the attenuation map, along the dotted lines in (b) and (d), respectively. Residual cancer cells were marked with black arrows and correspond to yellow/red regions on the attenuation maps (at the level of the dotted line). Scale bars, 0.2 mm (reprinted with permission from [22]).

Figure 9

Effect of ethanol on mouse fetal brain development at GD14.5. Left panel represents OCT images (a) and (b) in horizontal section, middle panel shows US images (c) and (d) in the coronal plane, and right panel illustrates H&E images (e) and (f) in sagittal section of fetal brains from both control and ethanol treated pregnant dams. Ethanol induced increase in ventricular dilation can be observed with all the imaging techniques used in the study and in all three planes of orientation. LV, lateral ventricles; US, ultrasound; OCT, optical coherence tomography; H&E, hematoxylin-eosin staining. Scale bar, 500 μm (reprinted with permission from [94]).

Figure 10

Colocalization of the neurons in layer II of EC for six different tissue samples: registered Nissl stain (a), OCT image (b), and the overlay of the segmented neurons (c): green for Nissl, red for OCT, and yellow for the overlap. Scale bar: 500 μm (reprinted with permission from [110]).

Figure 11

Major limitations of OCT technology.