Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast

Deep tissue photoacoustic imaging with light and sound

Photoacoustic computed tomography (PACT) can harvest diffusive photons to image the optical absorption contrast of molecules in a scattering medium, with ultrasonically-defined spatial resolution. PACT has been extensively used in preclinical research for imaging functional and molecular information in various animal models, with recent clinical translations. In this review, we aim to highlight the recent technical breakthroughs in PACT and the emerging preclinical and clinical applications in deep tissue imaging.

3D Multiparametric Photoacoustic Computed Tomography of Primary and Metastatic Tumors in Living Mice

Photoacoustic computed tomography (PACT), an emerging imaging modality in preclinical cancer research, can provide multiparametric 3D information about structures, physiological functions, and pharmacokinetics. Here, we demonstrate the use of high-definition 3D multiparametric PACT imaging of both primary and metastatic tumors in living mice to noninvasively monitor angiogenesis, carcinogenesis, hypoxia, and pharmacokinetics. The high-definition PACT system with a 1024-element hemispherical ultrasound transducer array provides an isotropic spatial resolution of 380 μm, an effective volumetric field-of-view of 12.8 mm × 12.8 mm × 12.8 mm without scanning, and an acquisition time of <30 s for a whole mouse body. Initially, we monitor the structural progression of the tumor microenvironment (e.g., angiogenesis and vessel tortuosity) after tumor cell inoculation. Then, we analyze the change in oxygen saturation of the tumor during carcinogenesis, verifying induced hypoxia in the tumor's core region. Finally, the whole-body pharmacokinetics are photoacoustically imaged after intravenous injection of micelle-loaded IR780 dye, and the in vivo PACT results are validated in vivo and ex vivo by fluorescence imaging. By employing the premium PACT system and applying multiparametric analyses to subcutaneous primary tumors and metastatic liver tumors, we demonstrate that this PACT system can provide multiparametric analyses for comprehensive cancer research.

Monte Carlo-Based Optical Simulation of Optical Distribution in Deep Brain Tissues Using Sixteen Optical Sources

Optical-based imaging has improved from early single-location research to further sophisticated imaging in 2D topography and 3D tomography. These techniques have the benefit of high specificity and non-radiative safety for brain detection and therapy. However, their performance is limited by complex tissue structures. To overcome the difficulty in successful brain imaging applications, we conducted a simulation using 16 optical source types within a brain model that is based on the Monte Carlo method. In addition, we propose an evaluation method of the optical propagating depth and resolution, specifically one based on the optical distribution for brain applications. Based on the results, the best optical source types were determined in each layer. The maximum propagating depth and corresponding source were extracted. The optical source propagating field width was acquired in different depths. The maximum and minimum widths, as well as the corresponding source, were determined. This paper provides a reference for evaluating the optical propagating depth and resolution from an optical simulation aspect, and it has the potential to optimize the performance of optical-based techniques.

On the Importance of Low-Frequency Signals in Functional and Molecular Photoacoustic Computed Tomography

In photoacoustic computed tomography (PACT) with short-pulsed laser excitation, wideband acoustic signals are generated in biological tissues with frequencies related to the effective shapes and sizes of the optically absorbing targets. Low-frequency photoacoustic signal components correspond to slowly varying spatial features and are often omitted during imaging due to the limited detection bandwidth of the ultrasound transducer, or during image reconstruction as undesired background that degrades image contrast. Here we demonstrate that low-frequency photoacoustic signals, in fact, contain functional and molecular information, and can be used to enhance structural visibility, improve quantitative accuracy, and reduce spare-sampling artifacts. We provide an in-depth theoretical analysis of low-frequency signals in PACT, and experimentally evaluate their impact on several representative PACT applications, such as mapping temperature in photothermal treatment, measuring blood oxygenation in a hypoxia challenge, and detecting photoswitchable molecular probes in deep organs. Our results strongly suggest that low-frequency signals are important for functional and molecular PACT.

Quantitative photoacoustic tomography: modeling and inverse problems

Significance

Quantitative photoacoustic tomography (QPAT) exploits the photoacoustic effect with the aim of estimating images of clinically relevant quantities related to the tissue's optical absorption. The technique has two aspects: an acoustic part, where the initial acoustic pressure distribution is estimated from measured photoacoustic time-series, and an optical part, where the distributions of the optical parameters are estimated from the initial pressure.

Aim

Our study is focused on the optical part. In particular, computational modeling of light propagation (forward problem) and numerical solution methodologies of the image reconstruction (inverse problem) are discussed.

Approach

The commonly used mathematical models of how light and sound propagate in biological tissue are reviewed. A short overview of how the acoustic inverse problem is usually treated is given. The optical inverse problem and methods for its solution are reviewed. In addition, some limitations of real-life measurements and their effect on the inverse problems are discussed.

Results

An overview of QPAT with a focus on the optical part was given. Computational modeling and inverse problems of QPAT were addressed, and some key challenges were discussed. Furthermore, the developments for tackling these problems were reviewed. Although modeling of light transport is well-understood and there is a well-developed framework of inverse mathematics for approaching the inverse problem of QPAT, there are still challenges in taking these methodologies to practice.

Conclusions

Modeling and inverse problems of QPAT together were discussed. The scope was limited to the optical part, and the acoustic aspects were discussed only to the extent that they relate to the optical aspect.

In vivo noninvasive systemic myography of acute systemic vasoactivity in female pregnant mice

Altered vasoactivity is a major characteristic of cardiovascular and oncological diseases, and many therapies are therefore targeted to the vasculature. Therapeutics which are selective for the diseased vasculature are ideal, but whole-body selectivity of a therapeutic is challenging to assess in practice. Vessel myography is used to determine the functional mechanisms and evaluate pharmacological responses of vascularly-targeted therapeutics. However, myography can only be performed on ex vivo sections of individual arteries. We have developed methods for implementation of spherical-view photoacoustic tomography for non-invasive and in vivo myography. Using photoacoustic tomography, we demonstrate the measurement of acute vascular reactivity in the systemic vasculature and the placenta of female pregnant mice in response to two vasodilators. Photoacoustic tomography simultaneously captures the significant acute vasodilation of major arteries and detects selective vasoactivity of the maternal-fetal vasculature. Photoacoustic tomography has the potential to provide invaluable preclinical information on vascular response that cannot be obtained by other established methods.

A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption

Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, has shown promise in early-stage clinical trials in a range of applications from inflammatory diseases to cancer. While the first PAI systems have recently received regulatory approvals, successful adoption of PAI technology into healthcare systems for clinical decision making must still overcome a range of barriers, from education and training to data acquisition and interpretation. The International Photoacoustic Standardisation Consortium (IPASC) undertook an community exercise in 2022 to identify and understand these barriers, then develop a roadmap of strategic plans to address them. Here, we outline the nature and scope of the barriers that were identified, along with short-, medium- and long-term community efforts required to overcome them, both within and beyond the IPASC group.

Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer

Significance

When developing a new quantitative optoacoustic computed tomography (OAT) system for diagnostic imaging of breast cancer, objective assessments of various system designs through human trials are infeasible due to cost and ethical concerns. In prototype stages, however, different system designs can be cost-efficiently assessed via virtual imaging trials (VITs) employing ensembles of digital breast phantoms, i.e., numerical breast phantoms (NBPs), that convey clinically relevant variability in anatomy and optoacoustic tissue properties.

Aim

The aim is to develop a framework for generating ensembles of realistic three-dimensional (3D) anatomical, functional, optical, and acoustic NBPs and numerical lesion phantoms (NLPs) for use in VITs of OAT applications in the diagnostic imaging of breast cancer.

Approach

The generation of the anatomical NBPs was accomplished by extending existing NBPs developed by the U.S. Food and Drug Administration. As these were designed for use in mammography applications, substantial modifications were made to improve blood vasculature modeling for use in OAT. The NLPs were modeled to include viable tumor cells only or a combination of viable tumor cells, necrotic core, and peripheral angiogenesis region. Realistic optoacoustic tissue properties were stochastically assigned in the NBPs and NLPs.

Results

To advance optoacoustic and optical imaging research, 84 datasets have been released; these consist of anatomical, functional, optical, and acoustic NBPs and the corresponding simulated multi-wavelength optical fluence, initial pressure, and OAT measurements. The generated NBPs were compared with clinical data with respect to the volume of breast blood vessels and spatially averaged effective optical attenuation. The usefulness of the proposed framework was demonstrated through a case study to investigate the impact of acoustic heterogeneity on OAT images of the breast.

Conclusions

The proposed framework will enhance the authenticity of virtual OAT studies and can be widely employed for the investigation and development of advanced image reconstruction and machine learning-based methods, as well as the objective evaluation and optimization of the OAT system designs.

Periodic-net: an end-to-end data driven framework for diffuse optical imaging of breast cancer from noisy boundary data

Significance

The machine learning (ML) approach plays a critical role in assessing biomedical imaging processes especially optical imaging (OI) including segmentation, classification, and reconstruction, intending to achieve higher accuracy efficiently.

Aim

This research aims to develop an end-to-end deep learning framework for diffuse optical imaging (DOI) with multiple datasets to detect breast cancer and reconstruct its optical properties in the early stages.

Approach

The proposed Periodic-net is a nondestructive deep learning (DL) algorithm for the reconstruction and evaluation of inhomogeneities in an inverse model with high accuracy, while boundary measurements are calculated by solving a forward problem with sources/detectors arranged uniformly around a circular domain in various combinations, including 16 × 15 , 20 × 19 , and 36 × 35 boundary measurement setups.

Results

The results of image reconstruction on numerical and phantom datasets demonstrate that the proposed network provides higher-quality images with a greater amount of small details, superior immunity to noise, and sharper edges with a reduction in image artifacts than other state-of-the-art competitors.

Conclusions

The network is highly effective at the simultaneous reconstruction of optical properties, i.e., absorption and reduced scattering coefficients, by optimizing the imaging time without degrading inclusions localization and image quality.

In vivo quantitative photoacoustic evaluation of the liver and kidney pathology in tyrosinemia

Hereditary tyrosinemia type Ⅰ (HT1) is a severe autosomal recessive inherited metabolic disease, which can result in severe damage of liver and kidney. Photoacoustic imaging (PAI) uses pulsed laser light to induce ultrasonic signals to facilitate the visualization of lesions that are strongly related to disease progression. In this study, the structural and functional changes of liver and kidney in HT1 was investigated by cross-scale PAI. The results showed that the hepatic lobule and renal tubule were severely damaged during HT1 progression. The hemoglobin content, vessel density, and liver function reserve were decreased. The metabolic half-life of indocyanine green declined from 59.8 s in health to 262.6 s in the advanced stage. Blood oxygen saturation was much lower than that in health. This study highlights the potential of PAI for in vivo evaluation of the liver and kidney lesions in HT1.

Adaptive dual-speed ultrasound and photoacoustic computed tomography

Full-ring dual-modal ultrasound and photoacoustic computed tomography has unique advantages of nearly isotropic spatial resolution, complementary contrast, deep penetration, and full-view detection. However, the imaging quality may be deteriorated by the inaccurate sound speed estimation. Automatic determining and compensation for sound speed has been a long-standing problem in image reconstruction. Here, we present new adaptive dual-speed ultrasound and photoacoustic computed tomography (ADS-USPACT) to address this challenge. The system features full-view coverage (360°), high-speed dual-modal imaging (10-Hz), automated dual sound speed correction, and synergistic high imaging quality. To correct the sound speed, we develop a two-compartment method that can automatically segment the sample boundary and search for the optimal sound speed based on the rich ultrasonic pulse-echo signals. The method does not require the operator's intervention. We validate this technique in numerical simulation, phantom study, and in vivo experiments. The ADS-USPACT represents significant progress in dual-modal imaging.