The development of real-time, wide-field and quantitative diffuse optical imaging methods to visualize functional and structural biomarkers of living tissues is a pressing need for numerous clinical applications including image-guided surgery. In this context, Spatial Frequency Domain Imaging (SFDI) is an attractive method allowing for the fast estimation of optical properties using the Single Snapshot of Optical Properties (SSOP) approach. Herein, we present a novel implementation of SSOP based on a combination of deep learning network at the filtering stage and Graphics Processing Units (GPU) capable of simultaneous high visual quality image reconstruction, surface profile correction and accurate optical property (OP) extraction in real-time across large fields of view. In the most optimal implementation, the presented methodology demonstrates megapixel profile-corrected OP imaging with results comparable to that of profile-corrected SFDI, with a processing time of 18 ms and errors relative to SFDI method less than 10% in both profilometry and profile-corrected OPs. This novel processing framework lays the foundation for real-time multispectral quantitative diffuse optical imaging for surgical guidance and healthcare applications. All code and data used for this work is publicly available at www.healthphotonics.org under the resources tab.
Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (μ a ) and reduced scattering (μ 0 s ) on a pixelby-pixel basis. Measurements of μ a at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, threewavelength SFDI system capable of quantifying μ a and μ 0 s as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system.Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website.Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of μ a and μ 0 s with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h. Results:The openSFDI systems had an error of 0 AE 6% in μ a and −2 AE 3% in μ 0 s , compared to a commercial SFDI system. Bland-Altman analysis revealed the limits of agreement between the two systems to be AE0.004 mm −1 for μ a and −0.06 to 0.1 mm −1 for μ 0 s . The openSFDI system had low drift with an average standard deviation of 0.0007 mm −1 and 0.05 mm −1 in μ a and μ 0 s , respectively. Conclusion:The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging.
We report on a macroscopic fluorescence lifetime imaging (MFLI) topography computational framework based around machine learning with the main goal of retrieving the depth of fluorescent inclusions deeply seated in bio-tissues. This approach leverages the depth-resolved information inherent to time-resolved fluorescence data sets coupled with the retrieval of in situ optical properties as obtained via spatial frequency domain imaging (SFDI). Specifically, a Siamese network architecture is proposed with optical properties (OPs) and time-resolved fluorescence decays as input followed by simultaneous retrieval of lifetime maps and depth profiles. We validate our approach using comprehensive in silico data sets as well as with a phantom experiment. Overall, our results demonstrate that our approach can retrieve the depth of fluorescence inclusions, especially when coupled with optical properties estimation, with high accuracy. We expect the presented computational approach to find great utility in applications such as optical-guided surgery.
Quantitative diffuse optical imaging has the potential to provide valuable functional information about tissue status, such as oxygen saturation or blood content to healthcare practitioners in real time. However, significant technical challenges have so far prevented such tools from being deployed in the clinic. Toward achieving this goal, prior research introduced methods based on spatial frequency domain imaging (SFDI) that allow real-time (within milliseconds) wide-field imaging of optical properties but at a single wavelength. However, for this technology to be useful to clinicians, images must be displayed in terms of metrics related to the physiological state of the tissue, hence interpretable to guide decision-making. For this purpose, recent developments introduced multispectral SFDI methods for rapid imaging of oxygenation parameters up to 16 frames per seconds (fps). We introduce real-time, wide-field, and quantitative blood parameters imaging using spatiotemporal modulation of light. Using this method, we are able to quantitatively obtain optical properties at 100 fps at two wavelengths (665 and 860 nm), and therefore oxygenation, oxyhemoglobin, and deoxyhemoglobin, using a single camera with, at most, 4.2% error in comparison with standard SFDI acquisitions.
Enagnon Aguénounon, Foudil Dadouche, Wilfried Uhring, Sylvain Gioux, "Single snapshot of optical properties image quality improvement using anisotropic two-dimensional windows filtering," J.Abstract. Imaging methods permitting real-time, wide-field, and quantitative optical mapping of biological tissue properties offer an unprecedented range of applications for clinical use. Following the development of spatial frequency domain imaging, we introduce a real-time demodulation method called single snapshot of optical properties (SSOPs). However, since this method uses only a single image to generate absorption and reduced scattering maps, it was limited by a degraded image quality resulting in artifacts that diminished its potential for clinical use. We present filtering strategies for improving the image quality of optical properties maps obtained using SSOPs. We investigate the effect of anisotropic two-dimensional filtering strategies for spatial frequencies ranging from 0.1 to 0.4 mm −1 directly onto N ¼ 10 hands. Both accuracy and image quality of the optical properties are quantified in comparison with standard, multiple image acquisitions in the spatial frequency domain. Overall, using optimized filters, mean errors in predicting optical properties using SSOP remain under 8.8% in absorption and 7.5% in reduced scattering, while significantly improving image quality. Overall this work contributes to advance real-time, wide-field, and quantitative diffuse optical imaging toward clinical evaluation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.