Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

Photoacoustic Reporter Gene Imaging And Optical Coherence Computed Tomography

Advances in imaging technologies have always been the major driving forces for the evolution of biomedical research. Compared with other modalities, optical imaging possesses several prominent merits. Because light interacts with tissue at the microscopic level through many distinct physical mechanisms, optical methods allow sensitive exploration of various aspects of the life down to the single-molecule level. From the technical perspective, optical systems utilize safe non-ionizing radiation, could be implemented at relatively low cost, also have the potential to be miniaturized for portable or endoscopic applications. As a result, optical imaging tools are playing an increasingly important role in both laboratorial research and clinical practice. Among them, photoacoustic imaging: PAI) and optical coherence tomography: OCT) are the two fastest growing branches. PAI measures the laser-induced acoustic wave, and produces high-resolution images of the optically absorbing features of tissue at multiple length-scales. OCT detects singly backscattered photons, and enables real-time high-resolution in vivo biopsy of tissue up to an optical transport mean-free-path. My doctoral research is focused on developing three novel optical imaging techniques based on the spirits of PAI and OCT. In the first part of this study, we established a new paradigm to visualize gene expression in vivo based on optical absorption. In the post-genomic era, we are now being challenged to develop novel molecular imaging methods to identify the functions of genes. PAI can detect specific molecules according to their characteristic absorption spectra, thus is a promising candidate for molecular imaging of gene expression. The full potential of photoacoustic molecular imaging still remains to be explored. For the first time, we demonstrated imaging gene expression by PAI in living mice and rats, using a chromogenic lacZ/X-gal reporter gene system. We demonstrated the expression of the lacZ reporter gene can be detected by PAI as deep as 5 cm inside tissue. In addition, we showcased that PAI could follow gene expression from the microscopic to the macroscopic level. This work represents one of the pioneering efforts to extend photoacoustic methods for molecular imaging. In the second part of this study, we developed a novel multimodal microscope, called the integrated photoacoustic and optical coherence microscope: iPOM), which combines PAI and OCT in a single imaging platform. PAI is predominantly sensitive to optical absorption, while OCT exploits optical scattering. By combining their naturally complementary imaging contrasts, iPOM can provide comprehensive information about biological tissue. We designed and built a reflection-mode prototype of iPOM, which fuses optical-resolution photoacoustic microscopy with spectral-domain optical coherence tomography. The potential applications of iPOM in studying cutaneous and ocular microcirculation, and tissue engineering were demonstrated. Finally, we invented a new optical tomography, named optical coherence computed tomography: optical CCT), which overcomes several major limitations of OCT. OCT relies on singly backscattered photons to obtain high-resolution images. Its image quality degrades fast with the increase of the depth, because the multiply scattered photons quickly become dominant at a penetration larger than 500 &mum. As a result, OCT can only effectively penetrate ~1 mm into highly scattering tissue like skin. In addition, OCT is mainly sensitive to optical scattering, which does not reflect the molecular content of tissue directly. Optical CCT measures both singly and multiply scattered photons using a low-coherence interferometer. We make use of both types of photons by adopting a model-based reconstruction algorithm. The light-tissue interaction model was established using the time-resolved Monte Carlo method. The optical properties of the tissue were reconstructed from measurements by solving the inverse radiative transport problem under the first Born approximation. As a result, optical CCT could image deeper than OCT, and provide extra molecule-specific contrasts, such as optical absorption. We designed and built the first optical CCT system. In experiments, absorbing inclusions of 100 &mum diameter were imaged with consistent quality through a 2.6-mm-thick: equivalent to ~3 transport mean-free-paths) tissue-mimicking phantom

Optical dosimetry tools and Monte Carlo based methods for applications in image guided optical therapy in the brain

Purpose: The long-term goal of this research is to determine the feasibility of using near infra-red light to stimulate drug release in metastatic lesions within the brain. In this work, we focused on developing the tools needed to quantify and verify photon fluence distribution in biological tissue. To accomplish this task, an optical dosimetry probe and Monte Carlo based simulation code were fabricated, calibrated and developed to predict light transport in heterogeneous tissue phantoms of the skull and brain. Empirical model (EM) of photon transport using CT images as input were devised to provide real-time calculations capable of being translated to preclinical and clinical applications. Methods and Materials: A GPU based 3D Monte Carlo code was customized to simulate the photon transport within head phantoms consisting of skull bone, white and gray matter with differing laser beam properties, including flat, Gaussian, and super-Gaussian profiles that are converging, parallel, or diverging. From these simulations, the local photon fluence and tissue dosimetric distribution was simulated and validated through the implementation of a novel titanium-based optical dosimetry probe with an isotropic acceptance and 1.5mm diameter. Empirical models (EM) of photon transport were devised and calibrated to MC simulated data to provide 3D fluence and optical dosimetric maps in real-time developed around on a voxel-based convolution technique. Optical transmission studies were performed using human skull bone samples to determine the optical transmission characteristics of heterogeneous bone structures and the effectiveness of the Monte Carlo in simulating this heterogeneity. These tools provide the capability to develop and optimize treatment plans for optimal release of pharmaceuticals to metastatic breast cancer in the brain. Results: At the time of these experiments, the voxel-based CUDA MC code implemented and further developed in this study had not been validated by measurement. A novel optical dosimetry probe was fabricated and calibrated to measure the absolute photon fluence (mW/mm2) in phantoms resembling white matter, gray matter and skull bone and compared to 3D Monte Carlo simulated data. The TiO2-based dosimetry probe was shown to have superior linearity and isotropicity of response to previous Nylon based probes, and was better suited to validate the Monte Carlo using localized 3D measurement (\u3c 25% systematic error for white matter, gray matter and skull bone phantoms along illumination beam axis up to a depth of 2cm in homogeneous tissue and 3.8cm in heterogeneous head phantom). Next, the transport parameters of the empirical algorithm was calibrated using the 3D Monte Carlo and EMs and validated by optical dosimetry probe measurements (with error of 10.1% for White Matter, 45.1% for Gray Matter and 22.1% for Skull Bone phantoms) along illumination beam axis. Conclusions: The design and validation of the Monte Carlo, the optical dosimetry probe and the Empirical algorithm increases the clinical feasibility of optical therapeutic planning to narrow down the complex possibilities of illumination conditions, further compounded by the heterogeneous structure of the brain, such as varying skull thicknesses and densities. Our ultimate goal is to design a fast Monte Carlo based optical therapeutic protocol to treat brain metastasis. The voxelated nature of the MC and EM provides the necessary 3D photon distribution to within 25% error to guide future clinical studies involving optically triggered drug release

Micromachined Scanning Devices for 3D Acoustic Imaging

Acoustic imaging (including ultrasound and photoacoustic imaging) refers to a class of imaging methods that use high-frequency sound (ultrasound) waves to generate contrast images for the interrogated media. It provides 3D spatial distribution of structural, mechanical, and even compositional properties in different materials. To conduct 3D ultrasound imaging, 2D ultrasound transducer arrays followed by multi-channel high-frequency data acquisition (DAQ) systems are frequently used. However, as the quantity and density of the transducer elements and also the DAQ channels increase, the acoustic imaging system becomes complex, bulky, expensive, and also power consuming. This situation is especially true for 3D imaging systems, where a 2D transducer array with hundreds or even thousands of elements could be involved. To address this issue, the objective of this research is to achieve new micromachined scanning devices to enable fast and versatile 2D ultrasound signal acquisition for 3D image reconstruction without involving complex physical transducer arrays and DAQ electronics. The new micromachined scanning devices studied in this research include 1) a water-immersible scanning mirror microsystem, 2) a micromechanical scanning transducer, and 3) a multi-layer linear transducer array. Especially, the water-immersible scanning mirror microsystem is capable of scanning focused ultrasound beam (from a single-element transducer) in two dimensions for 3D high-resolution acoustic microscopy. The micromechanical scanning transducer is capable of sending and receiving ultrasound signal from a single-element transducer to a 2D array of locations for 3D acoustic tomography. The multi-layer linear transducer array allows a unique electronic scanning scheme to simulate the functioning of a much larger 2D transducer array for 3D acoustic tomography. The design, fabrication and testing of the above three devices have been successfully accomplished and their applications in 3D acoustic microscopy and tomography have been demonstrated

Micromachined Scanning Devices for 3D Acoustic Imaging

Acoustic imaging (including ultrasound and photoacoustic imaging) refers to a class of imaging methods that use high-frequency sound (ultrasound) waves to generate contrast images for the interrogated media. It provides 3D spatial distribution of structural, mechanical, and even compositional properties in different materials. To conduct 3D ultrasound imaging, 2D ultrasound transducer arrays followed by multi-channel high-frequency data acquisition (DAQ) systems are frequently used. However, as the quantity and density of the transducer elements and also the DAQ channels increase, the acoustic imaging system becomes complex, bulky, expensive, and also power consuming. This situation is especially true for 3D imaging systems, where a 2D transducer array with hundreds or even thousands of elements could be involved. To address this issue, the objective of this research is to achieve new micromachined scanning devices to enable fast and versatile 2D ultrasound signal acquisition for 3D image reconstruction without involving complex physical transducer arrays and DAQ electronics. The new micromachined scanning devices studied in this research include 1) a water-immersible scanning mirror microsystem, 2) a micromechanical scanning transducer, and 3) a multi-layer linear transducer array. Especially, the water-immersible scanning mirror microsystem is capable of scanning focused ultrasound beam (from a single-element transducer) in two dimensions for 3D high-resolution acoustic microscopy. The micromechanical scanning transducer is capable of sending and receiving ultrasound signal from a single-element transducer to a 2D array of locations for 3D acoustic tomography. The multi-layer linear transducer array allows a unique electronic scanning scheme to simulate the functioning of a much larger 2D transducer array for 3D acoustic tomography. The design, fabrication and testing of the above three devices have been successfully accomplished and their applications in 3D acoustic microscopy and tomography have been demonstrated

Learning Tissue Geometries for Photoacoustic Image Analysis

Photoacoustic imaging (PAI) holds great promise as a novel, non-ionizing imaging modality, allowing insight into both morphological and physiological tissue properties, which are of particular importance in the diagnostics and therapy of various diseases, such as cancer and cardiovascular diseases. However, the estimation of physiological tissue properties with PAI requires the solution of two inverse problems, one of which, in particular, presents challenges in the form of inherent high dimensionality, potential ill-posedness, and non-linearity. Deep learning (DL) approaches show great potential to address these challenges but typically rely on simulated training data providing ground truth labels, as there are no gold standard methods to infer physiological properties in vivo. The current domain gap between simulated and real photoacoustic (PA) images results in poor in vivo performance and a lack of reliability of models trained with simulated data. Consequently, the estimates of these models occasionally fail to match clinical expectations. The work conducted within the scope of this thesis aimed to improve the applicability of DL approaches to PAI-based tissue parameter estimation by systematically exploring novel data-driven methods to enhance the realism of PA simulations (learning-to-simulate). This thesis is part of a larger research effort, where different factors contributing to PA image formation are disentangled and individually approached with data-driven methods. The specific research focus was placed on generating tissue geometries covering a variety of different tissue types and morphologies, which represent a key component in most PA simulation approaches. Based on in vivo PA measurements (N = 288) obtained in a healthy volunteer study, three data-driven methods were investigated leveraging (1) semantic segmentation, (2) Generative Adversarial Networks (GANs), and (3) scene graphs that encode prior knowledge about the general tissue composition of an image, respectively. The feasibility of all three approaches was successfully demonstrated. First, as a basis for the more advanced approaches, it was shown that tissue geometries can be automatically extracted from PA images through the use of semantic segmentation with two types of discriminative networks and supervised training with manual reference annotations. While this method may replace manual annotation in the future, it does not allow the generation of any number of tissue geometries. In contrast, the GAN-based approach constitutes a generative model that allows the generation of new tissue geometries that closely follow the training data distribution. The plausibility of the generated geometries was successfully demonstrated in a comparative assessment of the performance of a downstream quantification task. A generative model based on scene graphs was developed to gain a deeper understanding of important underlying geometric quantities. Unlike the GAN-based approach, it incorporates prior knowledge about the hierarchical composition of the modeled scene. However, it allowed the generation of plausible tissue geometries and, in parallel, the explicit matching of the distributions of the generated and the target geometric quantities. The training was performed either in analogy to the GAN approach, with target reference annotations, or directly with target PA images, circumventing the need for annotations. While this approach has so far been exclusively conducted in silico, its inherent versatility presents a compelling prospect for the generation of tissue geometries with in vivo reference PA images. In summary, each of the three approaches for generating tissue geometry exhibits distinct strengths and limitations, making their suitability contingent upon the specific application at hand. By opening a new research direction in the form of learning-to-simulate approaches and significantly improving the realistic modeling of tissue geometries and, thus, ultimately, PA simulations, this work lays a crucial foundation for the future use of DL-based quantitative PAI in the clinical setting

Developing Wavefront Shaping Techniques for Focusing through Highly Dynamic Scattering Media

One of the prime limiting factors of optical imaging in biological applications is the diffusion of light by tissue, which prevents focusing at depths greater than the optical diffusion limit of ~1 mm in soft tissue. This greatly restricts the utility of optical diagnostic and therapeutic techniques, such as optogenetics, microsurgery, optical tweezing, and phototherapy of deep tissue, which require focused light in order to function. Wavefront shaping extends the depth at which optical focusing may be achieved by compensating for phase distortions induced by scattering, allowing for focusing through constructive interference. However, due to physiological motion, scattering of light in tissue is deterministic only within a brief speckle correlation time. In in vivo soft tissue, this speckle correlation is on the order of milliseconds. Because wavefront shaping relies on deterministic scattering in order to compensate for the resulting phase distortion, the wavefront must be optimized within this brief period. This presents a challenge as the speed of digital wavefront shaping has typically been limited by the relatively long time required to measure and display the optimal phase pattern due to the low speed of cameras, data transfer and processing, and spatial light modulators. In order to overcome these restrictions, wavefront shaping techniques which minimize the time required in measurement and display are therefore vital. In this dissertation, I will describe our efforts to improve the speed of wavefront shaping without sacrificing the performance of the systems. To this end, we have successfully developed several systems which are capable of full-phase wavefront shaping with latencies of 9 ms or less. In addition, we report an all-digital alignment compensation protocol, which may be used to obtain optimal alignment in digital optical phase conjugation systems, a key component when acquiring the best possible focusing performance

High-speed alignment optimization of digital optical phase conjugation systems based on autocovariance analysis in conjunction with orthonormal rectangular polynomials

Digital optical phase conjugation (DOPC) enables many optical applications by permitting focusing of light through scattering media. However, DOPC systems require precise alignment of all optical components, particularly of the spatial light modulator (SLM) and camera, in order to accurately record the wavefront and perform playback through the use of time-reversal symmetry. We present a digital compensation technique to optimize the alignment of the SLM in five degrees of freedom, permitting focusing through thick scattering media with a thickness of 5 mm and transport scattering coefficient of 2.5  mm  −  1 while simultaneously improving focal quality, as quantified by the peak-to-background ratio, by several orders of magnitude over an unoptimized alignment

Roadmap on wavefront shaping and deep imaging in complex media

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working in this field, which has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead. &nbsp;</p