Halftone spatial frequency domain imaging enables kilohertz high-speed label-free non-contact quantitative mapping of optical properties for strongly turbid media

Tutorial on methods for estimation of optical absorption and scattering properties of tissue

Significance

The estimation of tissue optical properties using diffuse optics has found a range of applications in disease detection, therapy monitoring, and general health care. Biomarkers derived from the estimated optical absorption and scattering coefficients can reflect the underlying progression of many biological processes in tissues.

Aim

Complex light-tissue interactions make it challenging to disentangle the absorption and scattering coefficients, so dedicated measurement systems are required. We aim to help readers understand the measurement principles and practical considerations needed when choosing between different estimation methods based on diffuse optics.

Approach

The estimation methods can be categorized as: steady state, time domain, time frequency domain (FD), spatial domain, and spatial FD. The experimental measurements are coupled with models of light-tissue interactions, which enable inverse solutions for the absorption and scattering coefficients from the measured tissue reflectance and/or transmittance.

Results

The estimation of tissue optical properties has been applied to characterize a variety of ex vivo and in vivo tissues, as well as tissue-mimicking phantoms. Choosing a specific estimation method for a certain application has to trade-off its advantages and limitations.

Conclusion

Optical absorption and scattering property estimation is an increasingly important and accessible approach for medical diagnosis and health monitoring.

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Significance

Current photoacoustic (PA) imaging modalities typically require either serial detection with a single-element transducer or parallel detections with an ultrasonic array, indicating a dilemma between system cost and imaging throughput. PA topography through ergodic relay (PATER) was recently developed to address this bottleneck. However, PATER requires object-specific calibration due to varied boundary condition and must be recalibrated through pointwise scanning for each object before measurements, which is time-consuming and severely limits practical application.

Aim

We aim to develop a new single-shot PA imaging technique that only requires a one-time calibration for imaging different objects using a single-element transducer.

Approach

We develop an imaging method, PA imaging through a spatiotemporal encoder (PAISE), to address the above issue. The spatial information is effectively coded into unique temporal features by the spatiotemporal encoder, which allows for compressive image reconstruction. An ultrasonic waveguide is proposed as a critical element to guide the PA waves from the object into the prism, which effectively accounts for the varied boundary condition of different objects. We further add irregular-shaped edges on the prism to introduce randomized internal reflections and further facilitate the scrambling of acoustic waves.

Results

The proposed technique is validated through comprehensive numerical simulations and experiments, and it is demonstrated that PAISE can successfully overcome the changed boundary condition and can image different samples given a single calibration.

Conclusions

The proposed PAISE technique is capable of single-shot widefield PA imaging with a single-element transducer and does not require sample-specific calibration, which successfully overcomes the major limitation of previous PATER technology.

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.

Quantitative spatial mapping of tissue water and lipid content using spatial frequency domain imaging in the 900- to 1000-nm wavelength region

Significance

Water and lipid are key participants of many biological processes, but there are few label-free, non-contact optical methods that can spatially map these components in-vivo. Shortwave infrared meso-patterned imaging (SWIR-MPI) is an emerging technique that successfully addresses this need. However, it requires a dedicated SWIR camera to probe the 900- to 1300-nm wavelength region, which hinders practical translation of the technology.

Aim

Compared with SWIR-MPI, we aim to develop a new technique that can dramatically reduce the cost in detector while maintaining high accuracy for the quantification of tissue water and lipid content.

Approach

By utilizing water and lipid absorption features in the 900- to 1000-nm wavelength region as well as optimal wavelength and spatial frequency combinations, we develop a new imaging technique based on spatial frequency domain imaging to quantitatively map tissue water and lipid content using a regular silicon-based camera.

Results

The proposed method is validated with a phantom study, which shows average error of 0.9 ± 1.2 % for water content estimation, and -0.4 ± 0.7 % for lipid content estimation, respectively. The proposed method is also demonstrated for ex vivo porcine tissue lipid mapping as well as in-vivo longitudinal water content monitoring.

Conclusions

The proposed technique enables spatial mapping of tissue water and lipid content with the cost in detector reduced by two orders of magnitude compared with SWIR-MPI while maintaining high accuracy. The experimental results highlight the potential of this technique for substantial impact in both scientific and industrial applications.

Shedding light on biology and healthcare-preface to the special issue on Biomedical Optics

This special issue collects 20 excellent papers, spanning NIR II imaging, high-speed imaging, adaptive wavefront shaping, label-free imaging, ultrasensitive detection, polarization optics, photodynamic therapy, and preclinical applications. [Image: see text]

Marker-Free, Molecule Sensitive Mapping of Disturbed Falling Fluid Films Using Raman Imaging

Technical liquid flow films are the basic arrangement for gas fluid transitions of all kinds and are the basis of many chemical processes, such as columns, evaporators, dryers, and different other kinds of fluid/fluid separation units. This publication presents a new method for molecule sensitive, non-contact, and marker-free localized concentration mapping in vertical falling films. Using Raman spectroscopy, no label or marker is needed for the detection of the local composition in liquid mixtures. In the presented cases, the film mapping of sodium sulfate in water on a plain surface as well as an added artificial streaming disruptor with the shape of a small pyramid is scanned in three dimensions. The results show, as a prove of concept, a clear detectable spectroscopic difference between air, back plate, and sodium sulfate for every local point in all three dimensions. In conclusion, contactless Raman scanning on falling films for liquid mapping is realizable without any mechanical film interaction caused by the measuring probe. Surface gloss or optical reflections from a metallic back plate are suppressed by using only inelastic light scattering and the mathematical removal of background noise.

Ultracompact Deep Neural Network for Ultrafast Optical Property Extraction in Spatial Frequency Domain Imaging (SFDI)

Spatial frequency domain imaging (SFDI) is a powerful, label-free imaging technique capable of the wide-field quantitative mapping of tissue optical properties and, subsequently, chromophore concentrations. While SFDI hardware acquisition methods have advanced towards video-rate, the inverse problem (i.e., the mapping of acquired diffuse reflectance to optical properties) has remained a bottleneck for real-time data processing and visualization. Deep learning methods are adept at fitting nonlinear patterns, and may be ideal for rapidly solving the SFDI inverse problem. While current deep neural networks (DNN) are growing increasingly larger and more complex (e.g., with millions of parameters or more), our study shows that it can also be beneficial to move in the other direction, i.e., make DNNs that are smaller and simpler. Here, we propose an ultracompact, two-layer, fully connected DNN structure (each layer with four and two neurons, respectively) for ultrafast optical property extractions, which is 30×–600× faster than current methods with a similar or improved accuracy, allowing for an inversion time of 5.5 ms for 696 × 520 pixels. We further demonstrated the proposed inverse model in numerical simulations, and comprehensive phantom characterization, as well as offering in vivo measurements of dynamic physiological processes. We further demonstrated that the computation time could achieve another 200× improvement with a GPU device. This deep learning structure will help to enable fast and accurate real-time SFDI measurements, which are crucial for pre-clinical, clinical, and industrial applications.

High-resolution three-dimensional blood flow tomography in the subdiffuse regime using laser speckle contrast imaging

SIGNIFICANCE

Visualizing high-resolution hemodynamics in cerebral tissue over a large field of view (FOV), provides important information in studying disease states affecting the brain. Current state-of-the-art optical blood flow imaging techniques either lack spatial resolution or are too slow to provide high temporal resolution reconstruction of flow map over a large FOV.

AIM

We present a high spatial resolution computational optical imaging technique based on principles of laser speckle contrast imaging (LSCI) for reconstructing the blood flow maps in complex tissue over a large FOV provided that the three-dimensional (3D) vascular structure is known or assumed.

APPROACH

Our proposed method uses a perturbation Monte Carlo simulation of the high-resolution 3D geometry for both accurately deriving the speckle contrast forward model and calculating the Jacobian matrix used in our reconstruction algorithm to achieve high resolution. Given the convex nature of our highly nonlinear problem, we implemented a mini-batch gradient descent with an adaptive learning rate optimization method to iteratively reconstruct the blood flow map. Specifically, we implemented advanced optimization techniques combined with efficient parallelization and vectorization of the forward and derivative calculations to make reconstruction of the blood flow map feasible with reconstruction times on the order of tens of minutes.

RESULTS

We tested our reconstruction algorithm through simulation of both a flow phantom model as well as an anatomically correct murine cerebral tissue and vasculature captured via two-photon microscopy. Additionally, we performed a noise study, examining the robustness of our inverse model in presence of 0.1% and 1% additive noise. In all cases, the blood flow reconstruction error was <2  %   for most of the vasculature, except for the peripheral vasculature which suffered from insufficient photon sampling. Descending vasculature and deeper structures showed slightly higher sensitivity to noise compared with vasculature with a horizontal orientation at the more superficial layers. Our results show high-resolution reconstruction of the blood flow map in tissue down to 500  μm and beyond.

CONCLUSIONS

We have demonstrated a high-resolution computational imaging technique for visualizing blood flow map in complex tissue over a large FOV. Once a high-resolution structural image is captured, our reconstruction algorithm only requires a few LSCI images captured through a camera to reconstruct the blood flow map computationally at a high resolution. We note that the combination of high temporal and spatial resolution of our reconstruction algorithm makes the solution well-suited for applications involving fast monitoring of flow dynamics over a large FOV, such as in functional neural imaging.