Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues

Applications of optical property measurement for quality evaluation of agri-food products: a review

Spectroscopic techniques coupled with chemometric approaches have been widely used for quality evaluation of agricultural and food (agri-food) products due to the nondestructive, simple, fast, and easy characters. However, these techniques face the issues or challenges of relatively weak robustness, generalizability, and applicability in modeling and prediction because they measure the aggregate amount of light interaction with tissues, resulting in the combined effect of absorption and scattering of photons. Optical property measurement could separate absorption from scattering, providing new insights into more reliable prediction performance in quality evaluation, which is attracting increasing attention. In this review, a brief overview of the currently popular measurement techniques, in terms of light transfer principles and data analysis algorithms, is first presented. Then, the emphases are put on the recent advances of these techniques for measuring optical properties of agri-food products since 2000. Corresponding applications on qualitative and quantitative analyses of quality evaluation, as well as light transfer simulations within tissues, were reviewed. Furthermore, the leading groups working on optical property measurement worldwide are highlighted, which is the first summary to the best of our knowledge. Finally, challenges for optical property measurement are discussed, and some viewpoints on future research directions are also given.

Spectral Photoluminescent Parameters of Barley Seeds (Hordéum vulgáre) Infected with Fusarium ssp

We needed effective and sustainable technologies for better microbiological control of crops, including Fusarium. However, photoluminescent UV-Vis methods are potential for diagnosing plant diseases with Fusarium. It has not been sufficiently studied despite the application of these methods for other biological researches. The excitation spectrum of the seeds during infection shifts to the shorter wavelength and a new maximum appears in the region λ ≈ 232 nm. The photoluminescence of infected seeds increases with excitation by radiation of wavelengths λ_e,1  = 232 nm, λ_e,2  = 362 nm and λ_e,3  = 424 nm by 1.33-3.14 times, and λ_e,3  = 424 nm-decreases by 1.1 times. Statistical moments μ₃ and μ₄ , asymmetry and kurtosis change only with short-wave excitation. When analyzing the decomposition of the frequency spectrum into Gaussian curves, the most informative ratio is the ratio of right-handed and left-handed Gaussians under excitation λ_e,2  = 362 nm and λ_e,3  = 424 nm. The ratios of their maxima change during infection by 1.36-3.2 times, and for excitation by radiation λ_e,2 , the frequency boundaries of Gaussians change. The results of measurements and calculations provide a basis for the development of a method and device for photoluminescence diagnostics of fusarium seeds in UV-Vis ranges.

Influence of optical aberrations on depth-specific spatial frequency domain techniques

SIGNIFICANCE

Spatial frequency domain imaging (SFDI) and spatial frequency domain spectroscopy (SFDS) are emerging tools to non-invasively assess tissues. However, the presence of aberrations can complicate processing and interpretation.

AIM

This study develops a method to characterize optical aberrations when performing SFDI/S measurements. Additionally, we propose a post-processing method to compensate for these aberrations and recover arbitrary subsurface optical properties.

APPROACH

Using a custom SFDS system, we extract absorption and scattering coefficients from a reference phantom at 0 to 15 mm distances from the ideal focus. In post-processing, we characterize aberrations in terms of errors in absorption and scattering relative to the expected in-focus values. We subsequently evaluate a compensation approach in multi-distance measurements of phantoms with different optical properties and in multi-layer phantom constructs to mimic subsurface targets.

RESULTS

Characterizing depth-specific aberrations revealed a strong power law such as wavelength dependence from ∼40 to ∼10 % error in both scattering and absorption. When applying the compensation method, scattering remained within 1.3% (root-mean-square) of the ideal values, independent of depth or top layer thickness, and absorption remained within 3.8%.

CONCLUSIONS

We have developed a protocol that allows for instrument-specific characterization and compensation for the effects of defocus and chromatic aberrations on spatial frequency domain measurements.

Training generative adversarial networks for optical property mapping using synthetic image data

We demonstrate the training of a generative adversarial network (GAN) for the prediction of optical property maps (scattering and absorption) using spatial frequency domain imaging (SFDI) image data sets that are generated synthetically with a free open-source 3D modelling and rendering software, Blender. The flexibility of Blender is exploited to simulate 5 models with real-life relevance to clinical SFDI of diseased tissue: flat samples containing a single material, flat samples containing 2 materials, flat samples containing 3 materials, flat samples with spheroidal tumours and cylindrical samples with spheroidal tumours. The last case is particularly relevant as it represents wide-field imaging inside a tubular organ e.g. the gastro-intestinal tract. In all 5 scenarios we show the GAN provides an accurate reconstruction of the optical properties from single SFDI images with a mean normalised error ranging from 1.0-1.2% for absorption and 1.1%-1.2% for scattering, resulting in visually improved contrast for tumour spheroid structures. This compares favourably with the ∼10% absorption error and ∼10% scattering error achieved using GANs on experimental SFDI data. Next, we perform a bi-directional cross-validation of our synthetically-trained GAN, retrained with 90% synthetic and 10% experimental data to encourage domain transfer, with a GAN trained fully on experimental data and observe visually accurate results with an error of 6.3%-10.3% for absorption and 6.6%-11.9% for scattering. Our synthetically trained GAN is therefore highly relevant to real experimental samples but provides the significant added benefits of large training datasets, perfect ground-truths and the ability to test realistic imaging geometries, e.g. inside cylinders, for which no conventional single-shot demodulation algorithms exist. In the future, we expect that the application of techniques such as domain adaptation or training on hybrid real-synthetic datasets will create a powerful tool for fast, accurate production of optical property maps for real clinical imaging systems.

Cherenkov Luminescence in Tumor Diagnosis and Treatment: A Review

Malignant tumors rank as a leading cause of death worldwide. Accurate diagnosis and advanced treatment options are crucial to win battle against tumors. In recent years, Cherenkov luminescence (CL) has shown its technical advantages and clinical transformation potential in many important fields, particularly in tumor diagnosis and treatment, such as tumor detection in vivo, surgical navigation, radiotherapy, photodynamic therapy, and the evaluation of therapeutic effect. In this review, we summarize the advances in CL for tumor diagnosis and treatment. We first describe the physical principles of CL and discuss the imaging techniques used in tumor diagnosis, including CL imaging, CL endoscope, and CL tomography. Then we present a broad overview of the current status of surgical resection, radiotherapy, photodynamic therapy, and tumor microenvironment monitoring using CL. Finally, we shed light on the challenges and possible solutions for tumor diagnosis and therapy using CL.