Hyperspectral wide-field time domain single-pixel diffuse optical tomography platform

Direct 3-D printing of complex optical phantoms using dynamic filament mixing

We report a method to directly 3-D print complex heterogeneous optical phantoms with programmable tissue-mimicking absorption and scattering properties. The proposed approach utilizes commercially available multi-color mixing extruders and off-the-shelf polylactic acid filaments, making this technique low-cost and broadly accessible. We systematically characterized optical properties, including both absorption and reduced scattering coefficients, at a wide range of mixing ratios of gray, white, and translucent filaments and validated our hypothesis of a linear-mixing model between the filament mixing ratios and the resulting optical properties. Various techniques were used to design and fabricate sophisticated solid phantoms, including the design of color-purging towers, and the optimization of several printing parameters to improve print quality. To demonstrate the feasibility of this technique for generating anatomically complex phantoms with tunable optical absorption and scattering properties within tissue-relevant ranges, we designed and fabricated three heterogeneous optical phantoms. One of the presented phantoms was specifically designed to support quality assurance efforts in evaluating diffuse optics instruments and methodologies across various institutions. We have characterized the printed phantoms and observed an average error between 12%-15% compared to our linear-mixing model-predicted values. Future studies will target the usage of additional filament materials to expand potential imaging applications.

Fabrication of complex optical phantoms using on-the-fly multi-filament mixing 3-D printing

We report a method to directly 3-D print complex heterogeneous optical phantoms with programmable tissue-mimicking absorption and scattering properties. The proposed approach utilizes commercially available multi-color mixing extruders and off-the-shelf polylactic acid (PLA) filaments, making this technique low-cost and broadly accessible. We systematically characterized optical properties, including both absorption and reduced scattering coefficients, at a wide range of mixing ratios of gray, white and translucent filaments and validated our hypothesis of a linear-mixing model between the filament mixing ratios and the resulting optical properties. Various techniques were used to design and fabricate sophisticated solid phantoms, including the design of color-purging towers, and the optimization of several printing parameters to improve print quality. To demonstrate the feasibility of this technique for generating anatomically complex phantoms with tunable optical absorption and scattering properties within tissue-relevant ranges, we designed and fabricated three heterogeneous optical phantoms. One of the presented phantoms was specifically designed to support quality assurance efforts in evaluating diffuse optics instruments and methodologies across various institutions. We have characterized the printed phantoms and observed an average error between 12%-15% compared to our linear-mixing model predicted values.

Medium-adaptive compressive diffuse optical tomography

The low spatial resolution of diffuse optical tomography (DOT) has motivated the development of high-density DOT systems utilizing spatially-encoded illumination and detection strategies. Data compression methods, through the application of Fourier or Hadamard patterns, have been commonly explored for both illumination and detection but were largely limited to pre-determined patterns regardless of imaging targets. Here, we show that target-optimized detection patterns can yield significantly improved DOT reconstructions in both in silico and experimental tests. Applying reciprocity, we can further iteratively optimize both illumination and detection patterns and show that these simultaneously optimized source/detection patterns outperform predetermined patterns in simulation settings. In addition, we show media-adaptive measurement data compression methods enable wide-field DOT systems to recover highly complex inclusions inside optically-thick media with reduced background artifacts. Furthermore, using truncated optimized patterns shows an improvement of 2-4× in increased speed of data acquisition and reconstruction without significantly losing image quality. The proposed method can be readily extended for additional data dimensions such as spectrum and time.

Wide-field illumination diffuse optical tomography within a framework of single-pixel time-domain spatial frequency domain imaging

We present a wide-field illumination time-domain (TD) diffusion optical tomography (DOT) for three-dimensional (3-D) reconstruction within a shallow region under the illuminated surface of the turbid medium. The methodological foundation is laid on the single-pixel spatial frequency domain (SFD) imaging that facilitates the adoption of the well-established time-correlated single-photon counting (TCSPC)-based TD detection and generalized pulse spectrum techniques (GPST)-based reconstruction. To ameliorate the defects of the conventional diffusion equation (DE) in the forward modeling of TD-SFD-DOT, mainly the low accuracy in the near-field region and in profiling early-photon migration, we propose a modified model employing the time-dependent δ-P1 approximation and verify its improved accuracy in comparison with both the Monte Carlo and DE-based ones. For a simplified inversion process, a modified GPST approach is extended to TD-SFD-DOT that enables the effective separation of the absorption and scattering coefficients using a steady-state equivalent strategy. Furthermore, we set up a single-pixel TD-SFD-DOT system that employs the TCSPC-based TD detection in the SFD imaging framework. For assessments of the reconstruction approach and the system performance, phantom experiments are performed for a series of scenarios. The results show the effectiveness of the proposed methodology for rapid 3-D reconstruction of the absorption and scattering coefficients within a depth range of about 5 mean free pathlengths.

Design and characterization of a time-domain optical tomography platform for mesoscopic lifetime imaging

We report on the system design and instrumental characteristics of a novel time-domain mesoscopic fluorescence molecular tomography (TD-MFMT) system for multiplexed molecular imaging in turbid media. The system is equipped with a supercontinuum pulsed laser for broad spectral excitation, based on a high-density descanned raster scanning intensity-based acquisition for 2D and 3D imaging and augmented with a high-dynamical range linear time-resolved single-photon avalanche diode (SPAD) array for lifetime quantification. We report on the system's spatio-temporal and spectral characteristics and its sensitivity and specificity in controlled experimental settings. Also, a phantom study is undertaken to test the performance of the system to image deeply-seated fluorescence inclusions in tissue-like media. In addition, ex vivo tumor xenograft imaging is performed to validate the system's applicability to the biological sample. The characterization results manifest the capability to sense small fluorescence concentrations (on the order of nanomolar) while quantifying fluorescence lifetimes and lifetime-based parameters at high resolution. The phantom results demonstrate the system's potential to perform 3D multiplexed imaging thanks to spectral and lifetime contrast in the mesoscopic range (at millimeters depth). The ex vivo imaging exhibits the prospect of TD-MFMT to resolve intra-tumoral heterogeneity in a depth-dependent manner.

Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications

SIGNIFICANCE

Deep learning (DL) models are being increasingly developed to map sensor data to the image domain directly. However, DL methodologies are data-driven and require large and diverse data sets to provide robust and accurate image formation performances. For research modalities such as 2D/3D diffuse optical imaging, the lack of large publicly available data sets and the wide variety of instrumentation designs, data types, and applications leads to unique challenges in obtaining well-controlled data sets for training and validation. Meanwhile, great efforts over the last four decades have focused on developing accurate and computationally efficient light propagation models that are flexible enough to simulate a wide variety of experimental conditions.

AIM

Recent developments in Monte Carlo (MC)-based modeling offer the unique advantage of simulating accurately light propagation spatially, temporally, and over an extensive range of optical parameters, including minimally to highly scattering tissue within a computationally efficient platform. Herein, we demonstrate how such MC platforms, namely "Monte Carlo eXtreme" and "Mesh-based Monte Carlo," can be leveraged to generate large and representative data sets for training the DL model efficiently.

APPROACH

We propose data generator pipeline strategies using these platforms and demonstrate their potential in fluorescence optical topography, fluorescence optical tomography, and single-pixel diffuse optical tomography. These applications represent a large variety in instrumentation design, sample properties, and contrast function.

RESULTS

DL models trained using the MC-based in silico datasets, validated further with experimental data not used during training, show accurate and promising results.

CONCLUSION

Overall, these MC-based data generation pipelines are expected to support the development of DL models for rapid, robust, and user-friendly image formation in a wide variety of applications.

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.

Macroscopic Fluorescence Lifetime Imaging for Monitoring of Drug-Target Engagement

Precision medicine promises to improve therapeutic efficacy while reducing adverse effects, especially in oncology. However, despite great progresses in recent years, precision medicine for cancer treatment is not always part of routine care. Indeed, the ability to specifically tailor therapies to distinct patient profiles requires still significant improvements in targeted therapy development as well as decreases in drug treatment failures. In this regard, preclinical animal research is fundamental to advance our understanding of tumor biology, and diagnostic and therapeutic response. Most importantly, the ability to measure drug-target engagement accurately in live and intact animals is critical in guiding the development and optimization of targeted therapy. However, a major limitation of preclinical molecular imaging modalities is their lack of capability to directly and quantitatively discriminate between drug accumulation and drug-target engagement at the pathological site. Recently, we have developed Macroscopic Fluorescence Lifetime Imaging (MFLI) as a unique feature of optical imaging to quantitate in vivo drug-target engagement. MFLI quantitatively reports on nanoscale interactions via lifetime-sensing of Förster Resonance Energy Transfer (FRET) in live, intact animals. Hence, MFLI FRET acts as a direct reporter of receptor dimerization and target engagement via the measurement of the fraction of labeled-donor entity undergoing binding to its respective receptor. MFLI is expected to greatly impact preclinical imaging and also adjacent fields such as image-guided surgery and drug development.

The Use of Supercontinuum Laser Sources in Biomedical Diffuse Optics: Unlocking the Power of Multispectral Imaging

Optical techniques based on diffuse optics have been around for decades now and are making their way into the day-to-day medical applications. Even though the physics foundations of these techniques have been known for many years, practical implementation of these technique were hindered by technological limitations, mainly from the light sources and/or detection electronics. In the past 20 years, the developments of supercontinuum laser (SCL) enabled to unlock some of these limitations, enabling the development of system and methodologies relevant for medical use, notably in terms of spectral monitoring. In this review, we focus on the use of SCL in biomedical diffuse optics, from instrumentation and methods developments to their use for medical applications. A total of 95 publications were identified, from 1993 to 2021. We discuss the advantages of the SCL to cover a large spectral bandwidth with a high spectral power and fast switching against the disadvantages of cost, bulkiness, and long warm up times. Finally, we summarize the utility of using such light sources in the development and application of diffuse optics in biomedical sciences and clinical applications.

High compression deep learning based single-pixel hyperspectral macroscopic fluorescence lifetime imaging in vivo

Single pixel imaging frameworks facilitate the acquisition of high-dimensional optical data in biological applications with photon starved conditions. However, they are still limited to slow acquisition times and low pixel resolution. Herein, we propose a convolutional neural network for fluorescence lifetime imaging with compressed sensing at high compression (NetFLICS-CR), which enables in vivo applications at enhanced resolution, acquisition and processing speeds, without the need for experimental training datasets. NetFLICS-CR produces intensity and lifetime reconstructions at 128 × 128 pixel resolution over 16 spectral channels while using only up to 1% of the required measurements, therefore reducing acquisition times from ∼2.5 hours at 50% compression to ∼3 minutes at 99% compression. Its potential is demonstrated in silico, in vitro and for mice in vivo through the monitoring of receptor-ligand interactions in liver and bladder and further imaging of intracellular delivery of the clinical drug Trastuzumab to HER2-positive breast tumor xenografts. The data acquisition time and resolution improvement through NetFLICS-CR, facilitate the translation of single pixel macroscopic flurorescence lifetime imaging (SP-MFLI) for in vivo monitoring of lifetime properties and drug uptake.

Single snapshot imaging of optical properties using a single-pixel camera: a simulation study

We present the effects of using a single-pixel camera approach to extract optical properties with the single-snapshot spatial frequency-domain imaging method. We acquired images of a human hand for spatial frequencies ranging from 0.1 to 0.4  mm  -  1 with increasing compression ratios using adaptive basis scan wavelet prediction strategy. In summary, our findings indicate that the extracted optical properties remained usable up to 99% of compression rate at a spatial frequency of 0.2  mm  -  1 with errors of 5% in reduced scattering and 10% in absorption.