Real-time optical properties and oxygenation imaging using custom parallel processing in the spatial frequency domain

Extended Kalman filtering for enhancing a cyclic pattern-updating scheme of single-pixel dynamic fluorescence spatial frequency domain imaging

A single-pixel imaging technique applied to fluorescence spatial frequency domain imaging (f-SFDI) brings many positive benefits, but its low frame rate will also lead to severe quantitative degradation when dynamically imaging a vibrant target. This work presents a novel, to the best of our knowledge, single-pixel imaging approach that combines the extended Kalman filtering (EKF) and a cyclic one-pattern updating for an enhanced dynamic f-SFDI. The cyclic one-pattern updating scheme enables the dynamic imaging at a high frame rate, and on this basis, the imaging process of an intensity temporally varying target (assuming no structure motion in the scene) is dynamically modelled, and accordingly, the surface intensities and images at each sampling time point simultaneously estimated via the EKF. Simulation and phantom validations demonstrate that the method can improve the quantitative accuracy of the results. An in vivo experiment performed on two mice for dynamic monitoring of photosensitizer doses in a photodynamic therapy further demonstrates the clinical feasibility of the proposed method.

Development of a platform for broadband, spectra-fitted, tissue optical phantoms

SIGNIFICANCE

Current methods of producing optical phantoms are incapable of accurately capturing the wavelength-dependent properties of tissue critical for many optical modalities.

AIM

We aim to introduce a method of producing solid, inorganic phantoms whose wavelength-dependent optical properties can be matched to those of tissue over the wavelength range of 370 to 950 nm.

APPROACH

The concentration-dependent optical properties of 20 pigments were characterized and used to determine combinations that result in optimal fits compared to the target properties over the full spectrum. Phantoms matching the optical properties of muscle and nerve, the diffuse reflectance of pale and melanistic skin, and the chromophore concentrations of a computational skin model with varying oxygen saturation ( StO 2 ) were made with this method.

RESULTS

Both optical property phantoms were found to accurately mimic their respective tissues' absorption and scattering properties across the entire spectrum. The diffuse reflectance phantoms were able to closely approximate skin reflectance regardless of skin type. All three computational skin phantoms were found to have emulated chromophore concentrations close to the model, with an average percent error for the StO 2 of 4.31%.

CONCLUSIONS

This multipigment phantom platform represents a powerful tool for creating spectrally accurate tissue phantoms, which should increase the availability of standards for many optical techniques.

Measurement of tissue optical properties in a wide spectral range: a review [Invited]

A distinctive feature of this review is a critical analysis of methods and results of measurements of the optical properties of tissues in a wide spectral range from deep UV to terahertz waves. Much attention is paid to measurements of the refractive index of biological tissues and liquids, the knowledge of which is necessary for the effective application of many methods of optical imaging and diagnostics. The optical parameters of healthy and pathological tissues are presented, and the reasons for their differences are discussed, which is important for the discrimination of pathologies and the demarcation of their boundaries. When considering the interaction of terahertz radiation with tissues, the concept of an effective medium is discussed, and relaxation models of the effective optical properties of tissues are presented. Attention is drawn to the manifestation of the scattering properties of tissues in the THz range and the problems of measuring the optical properties of tissues in this range are discussed. In conclusion, a method for the dynamic analysis of the optical properties of tissues under optical clearing using an application of immersion agents is presented. The main mechanisms and technologies of optical clearing, as well as examples of the successful application for differentiation of healthy and pathological tissues, are analyzed.

Deep learning in macroscopic diffuse optical imaging

SIGNIFICANCE

Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis.

AIM

We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI).

APPROACH

First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography.

RESULTS

The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships.

CONCLUSIONS

The heavily validated capability of DL's use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient's bedside.

Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning

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.

Macroscopic fluorescence lifetime topography enhanced via spatial frequency domain 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.