Model-based optical and acoustical compensation for photoacoustic tomography of heterogeneous mediums

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.

Towards in vivo photoacoustic human imaging: Shining a new light on clinical diagnostics

Multiscale visualization of human anatomical structures is revolutionizing clinical diagnosis and treatment. As one of the most promising clinical diagnostic techniques, photoacoustic imaging (PAI), or optoacoustic imaging, bridges the spatial-resolution gap between pure optical and ultrasonic imaging techniques, by the modes of optical illumination and acoustic detection. PAI can non-invasively capture multiple optical contrasts from the endogenous agents such as oxygenated/deoxygenated hemoglobin, lipid and melanin or a variety of exogenous specific biomarkers to reveal anatomy, function, and molecular for biological tissues in vivo, showing significant potential in clinical diagnostics. In 2001, the worldwide first clinical prototype of the photoacoustic system was used to screen breast cancer in vivo, which opened the prelude to photoacoustic clinical diagnostics. Over the past two decades, PAI has achieved monumental discoveries and applications in human imaging. Progress towards preclinical/clinical applications includes breast, skin, lymphatics, bowel, thyroid, ovarian, prostate, and brain imaging, etc., and there is no doubt that PAI is opening new avenues to realize early diagnosis and precise treatment of human diseases. In this review, the breakthrough researches and key applications of photoacoustic human imaging in vivo are emphatically summarized, which demonstrates the technical superiorities and emerging applications of photoacoustic human imaging in clinical diagnostics, providing clinical translational orientations for the photoacoustic community and clinicians. The perspectives on potential improvements of photoacoustic human imaging are finally highlighted.

Challenges and advances in two-dimensional photoacoustic computed tomography: a review

Significance

Photoacoustic computed tomography (PACT), a hybrid imaging modality combining optical excitation with acoustic detection, has rapidly emerged as a prominent biomedical imaging technique.

Aim

We review the challenges and advances of PACT, including (1) limited view, (2) anisotropy resolution, (3) spatial aliasing, (4) acoustic heterogeneity (speed of sound mismatch), and (5) fluence correction of spectral unmixing.

Approach

We performed a comprehensive literature review to summarize the key challenges in PACT toward practical applications and discuss various solutions.

Results

There is a wide range of contributions from both industry and academic spaces. Various approaches, including emerging deep learning methods, are proposed to improve the performance of PACT further.

Conclusions

We outline contemporary technologies aimed at tackling the challenges in PACT applications.

Computational wave-based photoacoustic imaging through an unknown thick aberrating layer

We introduce a physics-based computational reconstruction framework for non-invasive photoacoustic tomography through a thick aberrating layer. Our wave-based approach leverages an analytic formulation of diffraction to beamform a photoacoustic image, when the aberrating layer profile is known. When the profile of the aberrating layer is unknown, the same analytical formulation serves as the basis for an automatic-differentiation regularized optimization algorithm that simultaneously reconstructs both the profile of the aberrating layer and the optically absorbing targets. Results from numerical studies and proof-of-concept experiments show promise for fast beamforming that takes into account diffraction effects occurring in the propagation through thick, highly-aberrating layers.

Quantitative Photoacoustic Tomography Using Iteratively Refined Wavefield Reconstruction Inversion: A Simulation Study

The ultimate goal of photoacoustic tomography is to accurately map the absorption coefficient throughout the imaged tissue. Most studies either assume that acoustic properties of biological tissues such as speed of sound (SOS) and acoustic attenuation are homogeneous or fluence is uniform throughout the entire tissue. These assumptions reduce the accuracy of estimations of derived absorption coefficients (DeACs). Our quantitative photoacoustic tomography (qPAT) method estimates DeACs using iteratively refined wavefield reconstruction inversion (IR-WRI) which incorporates the alternating direction method of multipliers to solve the cycle skipping challenge associated with full wave inversion algorithms. Our method compensates for SOS inhomogeneity, fluence decay, and acoustic attenuation. We evaluate the performance of our method on a neonatal head digital phantom.

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.

Advances in photoacoustic imaging aided by nano contrast agents: special focus on role of lymphatic system imaging for cancer theranostics

Photoacoustic imaging (PAI) is a successful clinical imaging platform for management of cancer and other health conditions that has seen significant progress in the past decade. However, clinical translation of PAI based methods are still under scrutiny as the imaging quality and clinical information derived from PA images are not on par with other imaging methods. Hence, to improve PAI, exogenous contrast agents, in the form of nanomaterials, are being used to achieve better image with less side effects, lower accumulation, and improved target specificity. Nanomedicine has become inevitable in cancer management, as it contributes at every stage from diagnosis to therapy, surgery, and even in the postoperative care and surveillance for recurrence. Nanocontrast agents for PAI have been developed and are being explored for early and improved cancer diagnosis. The systemic stability and target specificity of the nanomaterials to render its theranostic property depends on various influencing factors such as the administration route and physico-chemical responsiveness. The recent focus in PAI is on targeting the lymphatic system and nodes for cancer diagnosis, as they play a vital role in cancer progression and metastasis. This review aims to discuss the clinical advancements of PAI using nanoparticles as exogenous contrast agents for cancer theranostics with emphasis on PAI of lymphatic system for diagnosis, cancer progression, metastasis, PAI guided tumor resection, and finally PAI guided drug delivery.

A frequency-domain model-based reconstruction method for transcranial photoacoustic imaging: A 2D numerical investigation

Phase aberration caused by the skull is a major barrier to achieving high quality photoacoustic images of human and non-human primates' brains. To address this issue, time-reversal methods have been used but they are computationally demanding and slow due to relying on solving the full-wave equation. The proposed approach is based on model-based image reconstruction in the frequency-domain to achieve near real-time image reconstruction. The relationship between an imaging region and transducer array elements can be mathematically described as a model matrix and the image reconstruction can be performed by pseudo-inverse of the model matrix. The model matrix is numerically calculated due to the lack of analytical solutions for transcranial ultrasound. However, this calculation only needs to be performed once for a given experimental setup and the same acoustic medium, and is an offline process not affecting the actual image reconstruction time. This non-iterative mode-based method demonstrates a substantial improvement in image reconstruction time, being approximately 18 times faster than the time-reversal method, all while maintaining comparable image quality.

Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast

In the past decade, photoacoustic (PA) imaging has attracted a great deal of popularity as an emergent diagnostic technology owing to its successful demonstration in both preclinical and clinical arenas by various academic and industrial research groups. Such steady growth of PA imaging can mainly be attributed to its salient features, including being non-ionizing, cost-effective, easily deployable, and having sufficient axial, lateral, and temporal resolutions for resolving various tissue characteristics and assessing the therapeutic efficacy. In addition, PA imaging can easily be integrated with the ultrasound imaging systems, the combination of which confers the ability to co-register and cross-reference various features in the structural, functional, and molecular imaging regimes. PA imaging relies on either an endogenous source of contrast (e.g., hemoglobin) or those of an exogenous nature such as nano-sized tunable optical absorbers or dyes that may boost imaging contrast beyond that provided by the endogenous sources. In this review, we discuss the applications of PA imaging with endogenous contrast as they pertain to clinically relevant niches, including tissue characterization, cancer diagnostics/therapies (termed as theranostics), cardiovascular applications, and surgical applications. We believe that PA imaging's role as a facile indicator of several disease-relevant states will continue to expand and evolve as it is adopted by an increasing number of research laboratories and clinics worldwide.

Wavelength-dependent error minimization for quantitative spectroscopic photoacoustic tomography with a ring-array system

PURPOSE

Photoacoustic tomography (PAT) is a non-invasive and high-resolution imaging technique that can provide functional and molecular information from the optical properties of pathological tissues, such as cancer. Spectroscopic PAT (sPAT) is capable of supplying information such as oxygen saturation (sO2), which is an important biological indicator for diseases such as cancer. However, the wavelength dependent nature of sPAT makes it challenging to provide accurate quantitative measurements of tissue oxygenation beyond shallow depths. We have previously reported the utility of combined ultrasound tomography and PAT to achieve optical and acoustic compensated PAT images at a single wavelength and for enhanced PAT images at larger depths. In this work we further explore the utility of the optical and acoustic compensation PAT algorithm to minimize the wavelength dependency in sPAT by showcasing improvements in spectral unmixing.

MATERIALS AND METHODS

Two optically and acoustically characterized heterogenous phantoms were manufactured to test the ability of the system and developed algorithm to minimize the wavelength-dependence driven error in sPAT spectral unmixing. The PA inclusions within each phantom were composed of a mixture of two sulfate dyes, copper sulfate (CuSO4) and nickel sulfate (NiSO4), with known optical spectra. Improvements between uncompensated and optically and acoustically compensated PAT (OAcPAT) were quantified as the relative percent error between the measured results and the ground truth.

RESULTS

The results of our phantom studies demonstrate that OAcPAT can significantly improve the accuracy of sPAT measurements in a heterogenous medium and especially at larger inclusions depths which can reach to up to 12% improvement in measurement errors. This significant improvement can play a vital role in reliability of future in-vivo biomarker quantifications.

CONCLUSIONS

Utilizing UST for model-based optical and acoustic compensation of PAT images was proposed by our group previously. In this work, we further demonstrated the efficacy of the developed algorithm in sPAT by minimizing the error caused by the tissue's optical heterogeneity on improving spectral unmixing, which is a major limiting factor in reliability of sPAT measurements. Such synergistic combination of UST and PAT provides a window of opportunity to achieve bias-free quantitative sPAT measurements, which plays an important role in future pre-clinical and clinical utility of PAT.

Real-time assessment of high-intensity focused ultrasound heating and cavitation with hybrid optoacoustic ultrasound imaging

High-intensity focused ultrasound (HIFU) enables localized ablation of biological tissues by capitalizing on the synergistic effects of heating and cavitation. Monitoring of those effects is essential for improving the efficacy and safety of HIFU interventions. Herein, we suggest a hybrid optoacoustic-ultrasound (OPUS) approach for real-time assessment of heating and cavitation processes while providing an essential anatomical reference for accurate localization of the HIFU-induced lesion. Both effects could clearly be observed by exploiting the temperature dependence of optoacoustic (OA) signals and the strong contrast of gas bubbles in pulse-echo ultrasound (US) images. The differences in temperature increase and its rate, as recorded with a thermal camera for different HIFU pressures, evinced the onset of cavitation at the expected pressure threshold. The estimated temperatures based on OA signal variations were also within 10-20 % agreement with the camera readings for temperatures below the coagulation threshold (∼50 °C). Experiments performed in excised tissues as well as in a post-mortem mouse demonstrate that both heating and cavitation effects can be effectively visualized and tracked using the OPUS approach. The good sensitivity of the suggested method for HIFU monitoring purposes was manifested by a significant increase in contrast-to-noise ratio within the ablated region by > 10 dB and > 5 dB for the OA and US images, respectively. The hybrid OPUS-based monitoring approach offers the ease of handheld operation thus can readily be implemented in a bedside setting to benefit several types of HIFU treatments used in the clinics.

Quantitative photoacoustic tomography with light fluence compensation based on radiance Monte Carlo model

Objective. Photoacoustic tomography (PAT) is a rapidly evolving imaging modality that provides images with high contrast and spatial resolution showing the optical properties of biological tissues. The photoacoustic pressure is proportional to the product of the optical absorption coefficient and the local light fluence. The essential challenge in reconstructing quantitative images representing spatially varying absorption coefficients is the unknown light fluence. In addition, optical attenuation induces spatial variations in the light fluence, and the heterogeneity of the fluence determines the limits of reconstruction quality and depth.Approach.In this work, a reconstruction enhancement scheme is proposed to compensate for the variation in the light fluence in the absorption coefficient recovery. The inverse problem of the radiance Monte Carlo model describing light transport through the tissue is solved by using an alternating optimization strategy. In the iteration, the absorption coefficients and photon weights are alternately updated.Main results.The method provides highly accurate quantitative images of absorption coefficients in simulations, phantoms, andin vivostudies. The results show that the method has great potential for improving the accuracy of absorption coefficient recovery compared to conventional reconstruction methods that ignore light fluence variations. Comparison with state-of-the-art fluence compensation methods shows significant improvements in root mean square error, normalized mean square absolute distance, and structural similarity metrics.Significance.This method achieves high precision quantitative imaging by compensating for nonuniform light fluence without increasing the complexity and operation of the imaging system.

Sound out the impaired perfusion: Photoacoustic imaging in preclinical ischemic stroke

Acoustically detecting the optical absorption contrast, photoacoustic imaging (PAI) is a highly versatile imaging modality that can provide anatomical, functional, molecular, and metabolic information of biological tissues. PAI is highly scalable and can probe the same biological process at various length scales ranging from single cells (microscopic) to the whole organ (macroscopic). Using hemoglobin as the endogenous contrast, PAI is capable of label-free imaging of blood vessels in the brain and mapping hemodynamic functions such as blood oxygenation and blood flow. These imaging merits make PAI a great tool for studying ischemic stroke, particularly for probing into hemodynamic changes and impaired cerebral blood perfusion as a consequence of stroke. In this narrative review, we aim to summarize the scientific progresses in the past decade by using PAI to monitor cerebral blood vessel impairment and restoration after ischemic stroke, mostly in the preclinical setting. We also outline and discuss the major technological barriers and challenges that need to be overcome so that PAI can play a more significant role in preclinical stroke research, and more importantly, accelerate its translation to be a useful clinical diagnosis and management tool for human strokes.

Pixel-wise reconstruction of tissue absorption coefficients in photoacoustic tomography using a non-segmentation iterative method

In Photoacoustic Tomography (PAT), the acquired image represents a light energy deposition map of the imaging object. For quantitative imaging, the PAT image is converted into an absorption coefficient (μ a ) map by dividing the light fluence (LF). Previous methods usually assume a uniform tissueμ a distribution, and consequently degrade the LF correction results. Here, we propose a simple method to reconstruct the pixel-wiseμ a map. Our method is based on a non-segmentation-based iterative algorithm, which alternately optimizes the LF distribution and theμ a map. Using simulation data, as well as phantom and animal data, we implemented our algorithm and compared it to segmentation-based correction methods. The results show that our method can obtain accurate estimation of the LF distribution and therefore improve the image quality and feature visibility of theμ a map. Our method may facilitate efficient calculation of the concentration distributions of endogenous and exogenous agents in vivo.

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

SIGNIFICANCE

Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect.

AIM

Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium.

APPROACH

Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets.

RESULTS

The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction.

CONCLUSIONS

The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

Two-step proximal gradient descent algorithm for photoacoustic signal unmixing

Photoacoustic microscopy uses multiple wavelengths to measure concentrations of different absorbers. The speed of sound limits the shortest wavelength switching time to sub-microseconds, which is a bottleneck for high-speed broad-spectrum imaging. Via computational separation of overlapped signals, we can break the sound-speed limit on the wavelength switching time. This paper presents a new signal unmixing algorithm named two-step proximal gradient descent. It is advantageous in separating multiple wavelengths with long overlapping and high noise. In the simulation, we can unmix up to nine overlapped signals and successfully separate three overlapped signals with 12-ns delay and 15.9-dB signal-to-noise ratio. We apply this technique to separate three-wavelength photoacoustic images in microvessels. In vivo results show that the algorithm can successfully unmix overlapped multi-wavelength photoacoustic signals, and the unmixed data can improve accuracy in oxygen saturation imaging.

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

SIGNIFICANCE

In three-dimensional (3D) functional optoacoustic tomography (OAT), wavelength-dependent optical attenuation and nonuniform incident optical fluence limit imaging depth and field of view and can hinder accurate estimation of functional quantities, such as the vascular blood oxygenation. These limitations hinder OAT of large objects, such as a human female breast.

AIM

We aim to develop a measurement-data-driven method for normalization of the optical fluence distribution and to investigate blood vasculature detectability and accuracy for estimating vascular blood oxygenation.

APPROACH

The proposed method is based on reasonable assumptions regarding breast anatomy and optical properties. The nonuniform incident optical fluence is estimated based on the illumination geometry in the OAT system, and the depth-dependent optical attenuation is approximated using Beer-Lambert law.

RESULTS

Numerical studies demonstrated that the proposed method significantly enhanced blood vessel detectability and improved estimation accuracy of the vascular blood oxygenation from multiwavelength OAT measurements, compared with direct application of spectral linear unmixing without optical fluence compensation. Experimental results showed that the proposed method revealed previously invisible structures in regions deeper than 15 mm and/or near the chest wall.

CONCLUSIONS

The proposed method provides a straightforward and computationally inexpensive approximation of wavelength-dependent effective optical attenuation and, thus, enables mitigation of the spectral coloring effect in functional 3D OAT imaging.

Ultrasound and Photoacoustic Imaging of Breast Cancer: Clinical Systems, Challenges, and Future Outlook

Presently, breast cancer diagnostic methods are dominated by mammography. Although drawbacks of mammography are present including ionizing radiation and patient discomfort, not many alternatives are available. Ultrasound (US) is another method used in the diagnosis of breast cancer, commonly performed on women with dense breasts or in differentiating cysts from solid tumors. Handheld ultrasound (HHUS) and automated breast ultrasound (ABUS) are presently used to generate reflection images which do not contain quantitative information about the tissue. This limitation leads to a subjective interpretation from the sonographer. To rectify the subjective nature of ultrasound, ultrasound tomography (UST) systems have been developed to acquire both reflection and transmission UST (TUST) images. This allows for quantitative assessment of tissue sound speed (SS) and acoustic attenuation which can be used to evaluate the stiffness of the lesions. Another imaging modality being used to detect breast cancer is photoacoustic tomography (PAT). Utilizing much of the same hardware as ultrasound tomography, PAT receives acoustic waves generated from tissue chromophores that are optically excited by a high energy pulsed laser. This allows the user to ideally produce chromophore concentration maps or extract other tissue parameters through spectroscopic PAT. Here, several systems in the area of TUST and PAT are discussed along with their advantages and disadvantages in breast cancer diagnosis. This overview of available systems can provide a landscape of possible intersections and future refinements in cancer diagnosis.

Modeling combined ultrasound and photoacoustic imaging: Simulations aiding device development and artificial intelligence

Combined ultrasound and photoacoustic (USPA) imaging has attracted several pre-clinical and clinical applications due to its ability to simultaneously display structural, functional, and molecular information of deep biological tissue in real time. However, the depth and wavelength dependent optical attenuation and the unknown optical and acoustic heterogeneities limit the USPA imaging performance in deep tissue regions. Novel instrumentation, image reconstruction, and artificial intelligence (AI) methods are currently being investigated to overcome these limitations and improve the USPA image quality. Effective implementation of these approaches requires a reliable USPA simulation tool capable of generating US based anatomical and PA based molecular contrasts of deep biological tissue. Here, we developed a hybrid USPA simulation platform by integrating finite element models of light (NIRFast) and ultrasound (k-Wave) propagations for co-simulation of B-mode US and PA images. The platform allows optimization of different design parameters for USPA devices, such as the aperture size and frequency of both light and ultrasound detector arrays. For designing tissue-realistic digital phantoms, a dictionary-based function has been added to k-Wave to generate various levels of ultrasound speckle contrast. The feasibility of modeling US imaging combined with optical fluence dependent multispectral PA imaging is demonstrated using homogeneous as well as heterogeneous tissue phantoms mimicking human organs (e.g., prostate and finger). In addition, we also demonstrate the potential of the simulation platform to generate large scale application-specific training and test datasets for AI enhanced USPA imaging. The complete USPA simulation codes together with the supplementary user guides have been posted to an open-source repository (https://github.com/KothapalliLabPSU/US-PA_simulation_codes).

Detection of collagen by multi-wavelength photoacoustic analysis as a biomarker for bone health assessment

Collagen is an important biomarker of osteoporosis progression. Noninvasive, multispectral, photoacoustic (PA) techniques use pulsed laser light to induce PA signals to facilitate the visualization of chemical components that are strongly related to tissue health. In this study, the feasibility of multi-wavelength PA (MWPA) measurement of the collagen in bone, using the wavelength range of 1300-1800 nm, was investigated. First, the feasibility of this approach for detecting the collagen content of bone was demonstrated by means of numerical simulation. Then, ex vivo experiments were conducted on both animal and human bone specimens with different bone densities using the MWPA method. The relative collagen content was extracted and compared with the results of micro-computed tomography (micro-CT) and histology. The results showed that the "relative collagen content" parameter obtained using the MWPA approach correlated well with the bone volume ratio obtained from micro-CT images and histological analysis results. This study highlights the potential of the proposed PA technique for determining the collagen content of bones as a biomarker for bone health assessment.

Endocavity ultrasound and photoacoustic system for fetal and maternal imaging: design, implementation, and ex-vivo validation

Purpose: Transvaginal ultrasound (TVUS) is a widely used real-time and non-invasive imaging technique for fetal and maternal care. It can provide structural and functional measurements about the fetal brain, such as blood vessel diameter and blood flow. However, it lacks certain biochemical estimations, such as hemoglobin oxygen saturation ( SO 2 ), which limits its ability to indicate a fetus at risk of birth asphyxia. Photoacoustic (PA) imaging has been steadily growing in recognition as a complement to ultrasound (US). Studies have shown PA imaging is capable of providing such biochemical estimations as SO 2 at relatively high penetration depth (up to 30 mm). Approach: In this study, we have designed and developed a multi-modal (US, PA, and Doppler) endocavity imaging system (ECUSPA) around a commercialized TVUS probe (Philips ATL C9-5). Results: The integrated system was evaluated through a set of in-vitro, ex-vivo, and in-vivo studies. Imaging of excised sheep brain tissue demonstrated the system's utility and penetration depth in transfontanelle imaging conditions. The accuracy of using the spectroscopic PA imaging (sPA) method to estimate SO 2 was validated by comparing sPA oximetry results with the gold standard measurements indicated by a blood gas analyzer. The ability of US and Doppler to measure moving blood volume was evaluated in-vivo. Spectral unmixing capabilities were tested using fluorophores within sheep brains. Conclusion: The developed system is a high resolution (about 200    μ m at 30 mm depth), real-time (at 30 Hz), and quantitative ( SO 2 estimation error < 10 % ) imaging tool with a total diameter less than 30 mm, making it suitable for intrapartum applications such as fetal and maternal diagnostics.