Navigating challenges and solutions in quantitative photoacoustic imaging

A three-dimensional modeling method for quantitative photoacoustic breast imaging with handheld probe

Quantitative visualization of nanoparticles in cells and tissues, while preserving the spatial information, is very challenging. A photoacoustic imaging technique to depict the presence and quantity of nanoparticles is presented. This technique is based on the dependence of the photoacoustic signal on both the nanoparticle quantity and the laser fluence. Quantitative photoacoustic imaging is a robust technique that does not require knowledge of the local fluence, but a relative change in the fluence. This eliminates the need for sophisticated methods or models to determine the energy distribution of light in turbid media. Quantitative photoacoustic imaging was first applied to nanoparticle-loaded cells, and quantitation was validated by inductively coupled plasma mass spectrometry. Quantitative photoacoustic imaging was then extended to xenograft tumor tissue sections, and excellent agreement with traditional histopathological analysis was demonstrated. Our results suggest that quantitative photoacoustic imaging may be used in many applications including the determination of the efficiency and effectiveness of molecular targeting strategies for cell studies and animal models, the quantitative assessment of photoacoustic contrast agent biodistribution, and the validation of in vivo photoacoustic imaging.

Methodological progress in the age of Artificial Intelligence (AI) is increasingly driven by large-scale, high-quality datasets. Medical imaging is no exception. As the amount of imaging data grows exponentially, the development and validation of data-driven al- gorithms critically depend on realistic, well-annotated datasets. Photoacoustic imaging (PAI), a hybrid modality combining optical contrast with ultrasound resolution, holds great promise for non-invasive, functional biomedical imaging with the potential for quantitative measurement of blood oxygen saturation. Prospective clinical applications range from cardiovascular imaging to early diagnosis and treatment monitoring in oncology. However, as a comparatively young modality, PAI faces a fundamental challenge: the absence of sufficiently large, annotated datasets for the development and validation of quantitative methods. This thesis addresses the data scarcity problem in quantitative PAI through a comprehensive pipeline for the generation and validation of synthetic spectral imaging data. First, the thesis reviews the current role of deep learning in PAI, identifying the generation of realistic simulated data and domain adaptation as key techniques to address data scarcity. Consequently, SIMPA is introduced, the open-source simulation and image processing for photonics and acoustics framework enabling large-scale and reproducible PA image simulation. To overcome the remaining domain gap between simulated and experimental data, a conditional invertible neural network-based method is proposed for unsupervised domain transfer. For empirical validation of data-driven PA methods, anatomically realistic tissue-mimicking phantoms are fabricated and characterized, enabling the creation of a comprehensive dataset, including experimental and simulated multispectral images. This resource allows, for the first time, rigorous benchmarking of quantitative methods such as oximetry. This thesis contributes not only technically through novel methods and validation strate- gies, but also promotes reproducibility and open science by making all software tools and datasets publicly available. In doing so, it lays the foundation for systematic and transpar- ent development and validation of data-driven methods in PAI and supports the broader translation of PAI into preclinical and clinical applications.

.Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding.Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities.Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI.Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis.Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.

Photoacoustic imaging (PAI) used for noninvasive imaging of biological tissue has been reported in many literature. However, there are still some disadvantages in the novel technique, such as the poor efficiency of imaging. In the current PAI, multiple excitation of laser and multiple acquisition of signal are necessary for image reconstruction. In this case, laser pulses may injure biological tissue due to energy accumulation. To popularize PAI in clinical applications, it is necessary to develop a new imaging approach to increase the efficiency of PAI. A spatial phase-controlled algorithm is presented for full-field three-dimensional (3D) image reconstruction. By using the algorithm, photoabsorption sources at different depths can be reconstructed using just one set of data acquired in single laser shot. Unfocused plane transducer array and parallel data-acquisition (PDA) equipment are used for real-time photoacoustic (PA) signal detection and acquisition. The spatial resolution of the 3D PAI system was analyzed. Two graphite rods at various positions in a simulation model and two bifurcate vessels in the ear of rabbit were imaged. In addition, the motion trace of one particle flowing at constant velocity was captured dynamically. Experimental results showed that spatial phase-controlled algorithm based on plane transducer array and PDA system was capable of static and dynamic 3D PAI. Spatial phase-controlled algorithm is introduced for 3D image reconstruction. The PA signals are collected by plane transducer array and PDA system in single pulse excitation. The acquired volumetric data are sufficient for 3D image reconstruction. Therefore, tissue can avoid the long-term exposure to light source and it is safer than the current PAI for in vivo imaging. With an increase in the repetition rate of laser pulse and speed of image display, the imaging method will realize real-time 3D imaging, which will be significant in clinical detection and medical diagnosis.

While in standard photoacoustic imaging the propagation of sound waves is modeled by the standard wave equation, our approach is based on a generalized wave equation with variable sound speed and material density, respectively. In this paper we present an approach for photoacoustic imaging, which in addition to the recovery of the absorption density parameter, the imaging parameter of standard photoacoustics, also allows us to reconstruct the spatially varying sound speed and density, respectively, of the medium. We provide analytical reconstruction formulas for all three parameters based in a linearized model based on single plane illumination microscopy (SPIM) techniques.

We present a photoacoustic tomography-guided diffuse optical tomography approach using a hand-held probe for detection and characterization of deeply-seated targets embedded in a turbid medium. Diffuse optical tomography guided by coregistered ultrasound, MRI, and x ray has demonstrated a great clinical potential to overcome lesion location uncertainty and to improve light quantification accuracy. However, due to the different contrast mechanisms, some lesions may not be detectable by a nonoptical modality but yet have high optical contrast. Photoacoustic tomography utilizes a short-pulsed laser beam to diffusively penetrate into tissue. Upon absorption of the light by the target, photoacoustic waves are generated and used to reconstruct, at ultrasound resolution, the optical absorption distribution that reveals optical contrast. However, the robustness of optical property quantification of targets by photoacoustic tomography is complicated because of the wide range of ultrasound transducer sensitivity, the orientation and shape of the targets relative to the ultrasound array, and the uniformity of the laser beam. We show in this paper that the relative optical absorption map provided by photoacoustic tomography can potentially guide the diffuse optical tomography to accurately reconstruct target absorption maps.

Integrating photoacoustic (PA) and ultrasound (US) into a handheld probe to perform PA/US dual-modal imaging has been widely studied over the past few years. However, optical fluence decreases quickly in deeper tissue due to light scattering and absorption, which would significantly affect the quantitative PA imaging. In this paper, we performed a fluence compensation for a PA imaging study of human breast. The comparison of PA/US image with and without optical fluence compensation demonstrated that the fluence compensation could effectively improve imaging quality for handheld probe.

Knowledge of the local optical fluence in biological tissue is of fundamental importance for biomedical optical techniques to achieve quantification. We report a method to noninvasively measure the local optical fluence in optically inhomogeneous scattering media. The concept is based on two aspects: the local tagging of light using ultrasonic modulation and the photon path reversibility principle. Our method has advantages over known computational-based fluence mapping techniques, for its purely experimental nature and without the requirement of prior knowledge of the optical properties of the medium. We provide a theoretical formalism and validation of the method with experiments in tissue-like phantoms. Further, we combine our method with photoacoustic imaging and compensate the photoacoustic signals for fluence variations in optically inhomogeneous media.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

An end-to-end deep neural network, ResU-net, is developed for quantitative photoacoustic imaging. A residual learning framework is used to facilitate optimization and to gain better accuracy from considerably increased network depth. The contracting and expanding paths enable ResU-net to extract comprehensive context information from multispectral initial pressure images and, subsequently, to infer a quantitative image of chromophore concentration or oxygen saturation (sO2). According to our numerical experiments, the estimations of sO2 and indocyanine green concentration are accurate and robust against variations in both optical property and object geometry. An extremely short reconstruction time of 22ms is achieved.

Photoacoustic imaging is a new biomedical imaging technology developed in recent years. It has the advantages of high resolution and sensitivity to the functional characteristics of biological tissues. However, whether for photoacoustic tomography or photoacoustic microscopy imaging, the imaging resolution depends on the frequency and bandwidth of the received ultrasonic signal. This leads to a large amount of data being collected under the Nyquist's law. So storage medium and DSP processor are under unprecedented pressure. The problem of large amount of data is usually solved by using compression. Therefore, the combination of compressed sensing theory and photoacoustic imaging can not only restore images with high quality, but also reduce the amount of data as much as possible. It saves storage space and the time of further data processing. This paper introduces signal sparsity and measurement matrix of compressed sensing theory briefly. The virtual photoacoustic imaging of blood vessels is carried out in the simulation environment constructed by the k-wave toolbox of MATLAB. The collected data are restored by using the gradient projection for sparse reconstruction. The results show that high quality photoacoustic imaging images can be reconstructed while a small amount of data is stored. The performance of the simulation platform is verified. And it is of great significance to solve the problem of high data volume of photoacoustic imaging.

Photoacoustic (PA) imaging is a hybrid imaging technology which combines the best of optical (contrast) and ultrasound (resolution) imaging. PA imaging uses intrinsic contrast agents in the body like blood (hemoglobin), melanin, etc. But the contrast from the intrinsic contrast agents might not be sufficient for different applications. So, external contrast agents are needed for improving the contrast of PA images. Organic dyes, inorganic dyes, and nanomaterials can be used as photoacoustic contrast agents. The major issue with using external contrast agent is that they often need FDA approval to be used for in-vivo studies. Availability of FDA approved contrast agents for PA imaging is very limited. In this work, we present the feasibility of using food and food-based dyes as photoacoustic contrast agents. We use commonly used foods like coffee, tea, chocolate and food colorants as contrast agents. We use alpinion’s E-CUBE dual mode ultrasound and photoacoustic imaging system with mobile Nd:YAG laser pumped by OPO laser to demonstrate the efficiency of the contrast agents. The contrast agents were compared with methylene blue. Out of the many different agents we tested, coffee, chocolate and few other dyes proved as efficient photoacoustic contrast agents. We performed photoacoustic spectroscopy to identify at which wavelength the dye performed best. We also tested the contrast agents for imaging sentinel lymph nodes in rats and the results are very similar to methylene blue. This will enable the transition of photoacoustic imaging to the clinics more easily.

The International Photoacoustic Standardisation Consortium (IPASC) emerged from SPIE 2018, established to drive consensus on photoacoustic system testing. As photoacoustic imaging (PAI) matures from research laboratories into clinical trials, it is essential to establish best-practice guidelines for photoacoustic image acquisition, analysis and reporting, and a standardised approach for technical system validation. The primary goal of the IPASC is to create widely accepted phantoms for testing preclinical and clinical PAI systems. To achieve this, the IPASC has formed five working groups (WGs). The first and second WGs have defined optical and acoustic properties, suitable materials, and configurations of photoacoustic image quality phantoms. These phantoms consist of a bulk material embedded with targets to enable quantitative assessment of image quality characteristics including resolution and sensitivity across depth. The third WG has recorded details such as illumination and detection configurations of PAI instruments available within the consortium, leading to proposals for system-specific phantom geometries. This PAI system inventory was also used by WG4 in identifying approaches to data collection and sharing. Finally, WG5 investigated means for phantom fabrication, material characterisation and PAI of phantoms. Following a pilot multi-centre phantom imaging study within the consortium, the IPASC settled on an internationally agreed set of standardised recommendations and imaging procedures. This leads to advances in: (1) quantitative comparison of PAI data acquired with different data acquisition and analysis methods; (2) provision of a publicly available reference data set for testing new algorithms; and (3) technical validation of new and existing PAI devices across multiple centres.

Optoacoustic (OA) imaging is an emerging technology that combines the high optical contrast of tissues with the high spatial resolution of ultrasound. Taking full advantage of OA imaging requires a better understanding of OA wave propagation in light-absorbing media. Current simulation methods are mainly based on simplified conditions such as thermal confinement, negligible viscosity, and homogeneous acoustic properties throughout the image object. In this study a new numerical approach is proposed based on a finite-difference time-domain (FDTD) method to solve the general OA equations, comprising the continuity, Navier-Stokes, and heat-conduction equations. The FDTD code was validated using a benchmark problem that has an approximate analytical solution. OA experiments were also conducted and data were in good agreement with those predicted by the FDTD method. Characteristics of simulated OA waveforms and OA images were discussed. The simulator was also employed to study wavefront distortion in OA breast imaging.