Snapshot hyperspectral retinal imaging using compact spectral resolving detector array

Abstract

Hyperspectral retinal imaging captures the light spectrum from each imaging pixel. It provides spectrally encoded retinal physiological and morphological information, which could potentially benefit diagnosis and therapeutic monitoring of retinal diseases. The key challenges in hyperspectral retinal imaging are how to achieve snapshot imaging to avoid motions between the images from multiple spectral bands, and how to design a compact snapshot imager suitable for clinical use. Here, we developed a compact, snapshot hyperspectral fundus camera for rodents using a novel spectral resolving detector array (SRDA), on which a thin-film Fabry–Perot cavity filter was monolithically fabricated on each imaging pixel. We achieved hyperspectral retinal imaging with 16 wavelength bands (460 to 630 nm) at 20 fps. We also demonstrated false-color vessel contrast enhancement and retinal oxygen saturation (sO2) measurement through spectral analysis. This work could potentially bring hyperspectral retinal imaging from bench to bedside.

Bayer Filter Snapshot Hyperspectral Fundus Camera for Human Retinal Imaging

“…The spectral response of each channel can be found in our previous report. 27 More detailed information regarding this hyperspectral imaging detector can be found at Imec’s website. 28 …”

“…The method was described in our previous reports. 27 We first calculated the optical density (OD) spectra of blood vessels by $$\text{OD}=-{\text{log}}_{10}true(\frac{I_{\text{in}}}{I_{\text{out}}}true)$$ where I in is the spectrum of a vessel extracted from the hyperspectral cube and I out is the spectrum from the area adjacent to the vessel. We then used least squares method to fit the OD to the equation, $$\begin{array}{cc}right\text{OD}(\lambda )=& leftB-N\text{ln}(\lambda )\\ right& left+A\left[{\mu }_{\text{HbR}}(\lambda )+(1-{\text{sO}}_2){\mu }_{{\text{HbO}}_2}(\lambda )\right]\end{array}$$ where B and N represent wavelength-independent and wavelength-dependent optical scattering, respectively; λ is the optical wavelength; A is the coefficient related to the experimental geometry and vessel diameter; μ HbR (λ) and μ HbO 2 (λ) are the effective attenuation coefficients of the fully deoxygenated and oxygenated blood, respectively.…”

“…17,24,26 It can capture hyperspectral images with the ease and flexibility of a normal CMOS camera, allowing for simple integration to standard fundus cameras such as those commonly found in ophthalmology clinics. 27 …”

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

“…As shown in Figure 4, the four solid lines in different gray colors represent the actual reflectance curves of the four calibration tarps measured by the portable spectroradiometer. Bandwidths were calculated based on the full width at half maximum (FWHM) of the camera spectral response curves in each of the four spectral bands [41], which are also shown in Figure 4. Compared with scientific-grade cameras, consumer-grade cameras have wider spectral ranges for individual bands and more overlap between the bands.…”

Drusen diagnosis comparison between hyper-spectral and color retinal images

“…In the current study, in addition to standard RGB fundus images, we investigated another image modality, hyper-spectral retinal imaging. Using a prototype device, we acquired fundus reflectance images at 16 different wavelengths, as detailed by Li et al [8]. The benefit of hyper-spectral images are their ability to capture, non-invasively, a large spectral data set, with the potential to identify important biomarkers for diagnosis of AMD [9].…”