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

Data-driven reconstruction methods for photoacoustic tomography:Learning structures by structured learning

Photoacoustic tomography (PAT) is an imaging technique with potential applications in various fields of biomedicine. By visualising vascular structures, PAT could help in the detection and diagnosis of diseases related to their dysregulation. In PAT, tissue is illuminated by light. After entering the tissue, the light undergoes scattering and absorption. The absorbed energy is transformed into an initial pressure by the photoacoustic effect, which travels to ultrasound detectors outside the tissue.This thesis is concerned with the inverse problem of the described physical process: what was the initial pressure in the tissue that gave rise to the detected pressure outside? The answer to this question is difficult to obtain when light penetration in tissue is not sufficient, the measurements are corrupted, or only a small number of detectors can be used in a limited geometry. For decades, the field of variational methods has come up with new approaches to solve these kind of problems. these kind of problems: the combination of new theory and clever algorithms has led to improved numerical results in many image reconstruction problems. In the past five years, previously state-of-the-art results were greatly surpassed by combining variational methods with artificial neural networks, a form of artificial intelligence.In this thesis we investigate several ways of combining data-driven artificial neural networks with model-driven variational methods. We combine the topics of photoacoustic tomography, inverse problems and artificial neural networks.Chapter 3 treats the variational problem in PAT and provides a framework in which hand-crafted regularisers can easily be compared. Both directional and higher-order total variation methods show improved results over direct methods for PAT with structures resembling vasculature.Chapter 4 provides a method to jointly solve the PAT reconstruction and segmentation problem for absorbing structures resembling vasculature. Artificial neural networks are embodied in the algorithmic structure of primal-dual methods, which are a popular way to solve variational problems. It is shown that a diverse training set is of utmost importance to solve multiple problems with one learned algorithm.Chapter 5 provides a convergence analysis for data-consistent networks, which combine classical regularisation methods with artificial neural networks. Numerical results are shown for an inverse problem that couples the Radon transform with a saturation problem for biomedical images.Chapter 6 explores the idea of fully-learned reconstruction by connecting two nonlinear autoencoders. By enforcing a dimensionality reduction in the artificial neural network, a joint manifold for measurements and images is learned. The method, coined learned SVD, provides advantages over other fully-learned methods in terms of interpretability and generalisation. Numerical results show high-quality reconstructions, even in the case where no information on the forward process is used.In this thesis, several ways of combining model-based methods with data-driven artificial neural networks were investigated. The resulting hybrid methods showed improved tomography reconstructions. By allowing data to improve a structured method, deeper vascular structures could be imaged with photoacoustic tomography.<br/

Holographic Fourier domain diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is a non-invasive optical modality which can be used to measure cerebral blood flow (CBF) in real-time. It has important potential applications in clinical monitoring, as well as in neuroscience and the development of a non-invasive brain-computer interface. However, a trade-off exists between the signal-to-noise ratio (SNR) and imaging depth, and thus CBF sensitivity, of this technique. Additionally, as DCS is a diffuse optical technique, it is limited by a lack of inherent depth discrimination within the illuminated region of each source-detector pair, and the CBF signal is therefore also prone to contamination by the extracerebral tissues which the light traverses. Placing a particular emphasis on scalability, affordability, and robustness to ambient light, in this work I demonstrate a novel approach which fuses the fields of digital holography and DCS: holographic Fourier domain DCS (FD-DCS). The mathematical formalism of FD-DCS is derived and validated, followed by the construction and validation (for both in vitro and in vivo experiments) of a holographic FD-DCS instrument. By undertaking a systematic SNR performance assessment and developing a novel multispeckle denoising algorithm, I demonstrate the highest SNR gain reported in the DCS literature to date, achieved using scalable and low-cost camera-based detection. With a view to generating a forward model for holographic FD-DCS, in this thesis I propose a novel framework to simulate statistically accurate time-integrated dynamic speckle patterns in biomedical optics. The solution that I propose to this previously unsolved problem is based on the Karhunen-Loève expansion of the electric field, and I validate this technique against novel expressions for speckle contrast for different forms of homogeneous field. I also show that this method can readily be extended to cases with spatially varying sample properties, and that it can also be used to model optical and acoustic parameters

Three-Dimensional Photoacoustic Computed Tomography: Imaging Models and Reconstruction Algorithms

Photoacoustic computed tomography: PACT), also known as optoacoustic tomography, is a rapidly emerging imaging modality that holds great promise for a wide range of biomedical imaging applications. Much effort has been devoted to the investigation of imaging physics and the optimization of experimental designs. Meanwhile, a variety of image reconstruction algorithms have been developed for the purpose of computed tomography. Most of these algorithms assume full knowledge of the acoustic pressure function on a measurement surface that either encloses the object or extends to infinity, which poses many difficulties for practical applications. To overcome these limitations, iterative image reconstruction algorithms have been actively investigated. However, little work has been conducted on imaging models that incorporate the characteristics of data acquisition systems. Moreover, when applying to experimental data, most studies simplify the inherent three-dimensional wave propagation as two-dimensional imaging models by introducing heuristic assumptions on the transducer responses and/or the object structures. One important reason is because three-dimensional image reconstruction is computationally burdensome. The inaccurate imaging models severely limit the performance of iterative image reconstruction algorithms in practice. In the dissertation, we propose a framework to construct imaging models that incorporate the characteristics of ultrasonic transducers. Based on the imaging models, we systematically investigate various iterative image reconstruction algorithms, including advanced algorithms that employ total variation-norm regularization. In order to accelerate three-dimensional image reconstruction, we develop parallel implementations on graphic processing units. In addition, we derive a fast Fourier-transform based analytical image reconstruction formula. By use of iterative image reconstruction algorithms based on the proposed imaging models, PACT imaging scanners can have a compact size while maintaining high spatial resolution. The research demonstrates, for the first time, the feasibility and advantages of iterative image reconstruction algorithms in three-dimensional PACT

Towards ultrasound full-waveform inversion in medical imaging

Ultrasound imaging is a front-line clinical modality with a wide range of applications. However, there are limitations to conventional methods for some medical imaging problems, including the imaging of the intact brain. The goal of this thesis is to explore and build on recent technological advances in ultrasonics and related areas such as geophysics, including the ultrasound data parallel acquisition hardware, advanced computational techniques for field modelling and for inverse problem solving. With the significant increase in the computational power now available, a particular focus will be put on exploring the potential of full-waveform inversion (FWI), a high-resolution image reconstruction technique which has shown significant success in seismic exploration, for medical imaging applications. In this thesis a range of technologies and systems have been developed in order to improve ultrasound imaging by taking advantage of these recent advances. In the first part of this thesis the application of dual frequency ultrasound for contrast enhanced imaging of neurovasculature in the mouse brain is investigated. Here we demonstrated a significant improvement in the contrast-to-tissue ratio that could be achieved by using a multi-probe, dual frequency imaging system when compared to a conventional approach using a single high frequency probe. However, without a sufficiently accurate calibration method to determine the positioning of these probes the image resolution was found to be significantly reduced. To mitigate the impact of these positioning errors, a second study was carried out to develop a sophisticated dual probe ultrasound tomography acquisition system with a robust methodology for the calibration of transducer positions. This led to a greater focus on the development of ultrasound tomography applications in medical imaging using FWI. A 2.5D brain phantom was designed that consisted of a soft tissue brain model surrounded by a hard skull mimicking material to simulate a transcranial imaging problem. This was used to demonstrate for the first time, as far as we are aware, the experimental feasibility of imaging the brain through skull using FWI. Furthermore, to address the lack of broadband sensors available for medical FWI reconstruction applications, a deep learning neural network was proposed for the bandwidth extension of observed narrowband data. A demonstration of this proposed technique was then carried out by improving the FWI image reconstruction of experimentally acquired breast phantom imaging data. Finally, the FWI imaging method was expanded for3D neuroimaging applications and an in silico feasibility of reconstructing the mouse brain with commercial transducers is demonstrated.Open Acces

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the sampling rate. Then, the signal can be perfectly reconstructed from its samples. Increasing evidence shows that the requirements imposed by Shannon's sampling theorem are too conservative for many naturally-occurring signals, which can be accurately characterized by sparse representations that require lower sampling rates closer to the signal's intrinsic information rates. Finite rate of innovation (FRI) is a new theory that allows to extract underlying sparse signal representations while operating at a reduced sampling rate. The goal of this PhD work is to advance reconstruction techniques for sparse signal representations from both theoretical and practical points of view. Specifically, the FRI framework is extended to deal with applications that involve temporal and spatial localization of events, including inverse source problems from radiating fields. We propose a novel reconstruction method using a model-fitting approach that is based on minimizing the fitting error subject to an underlying annihilation system given by the Prony's method. First, we showed that this is related to the problem known as structured low-rank matrix approximation as in structured total least squares problem. Then, we proposed to solve our problem under three different constraints using the iterative quadratic maximum likelihood algorithm. Our analysis and simulation results indicate that the proposed algorithms improve the robustness of the results with respect to common FRI reconstruction schemes. We have further developed the model-fitting approach to analyze spontaneous brain activity as measured by functional magnetic resonance imaging (fMRI). For this, we considered the noisy fMRI time course for every voxel as a convolution between an underlying activity inducing signal (i.e., a stream of Diracs) and the hemodynamic response function (HRF). We then validated this method using experimental fMRI data acquired during an event-related study. The results showed for the first time evidence for the practical usage of FRI for fMRI data analysis. We also addressed the problem of retrieving a sparse source distribution from the boundary measurements of a radiating field. First, based on Green's theorem, we proposed a sensing principle that allows to relate the boundary measurements to the source distribution. We focused on characterizing these sensing functions with particular attention for those that can be derived from holomorphic functions as they allow to control spatial decay of the sensing functions. With this selection, we developed an FRI-inspired non-iterative reconstruction algorithm. Finally, we developed an extension to the sensing principle (termed eigensensing) where we choose the spatial eigenfunctions of the Laplace operator as the sensing functions. With this extension, we showed that eigensensing principle allows to extract partial Fourier measurements of the source functions from boundary measurements. We considered photoacoustic tomography as a potential application of these theoretical developments

Pulsed Optoacoustics in Solids

Optoacoustic techniques are widely used to probe and characterize target materials including solids, liquids and gases. Included in such applications are diagnoses of thin films and semiconductor materials. The need to obtain greater spatial resolution requires the generation of shorter optoacoustic pulses. For such pulses, non-thermal effects may be quite important. On the other hand, even when an optoacoustic pulse is generated by an initially non-thermal technique, the thermal aspects become important in its evolution and propagation. The research undertaken in this Ph.D. dissertation included the generation and detection of optoacoustic signals through the thermal elastic mechanism. Several applications in material property diagnostics were investigated using several pulsed lasers. Both contact and non-contact detection techniques were used. A compact, lightweight, inexpensive system using a semiconductor laser, with potentially wide applicability, was developed. We developed the methods of analysis required to compare and explain the experimental results obtained. Included in such development was the incorporation of the responsivity of a piezoelectric transducer, whose necessarily non-ideal characteristics need to be accounted for in any analysis. We extended the Rosencwaig-Gersho model, which is used to treat the thermal diffusion problem with a sinusoidal heat source, to a at source, to a general pulsed laser source. This problem was also solved by a numerical method we developed in this work. Two powerful tools were introduced to process experimental data. The Fourier transform was used to resolve the time interval between two acoustic echoes. The wavelet transform was used to identify optoacoustic pulses in different wave modes or those generated by different mechanisms. The wavelet shrinkage technique was used to remove white noise from the signal. We also developed a spectral ratio method, which eliminates the need for the knowledge of several material parameters, to obtain the optical absorption coefficient. Finally, we extended the optoacoustic measurement to biological samples and applied techniques that we developed in this work to process and analyze signals obtained from such samples

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.The Natural Science Foundation of China (NSFC) (grant numbers: 51675403, 51275381 and 51505475), National Research Foundation, South Africa (grant numbers: IFR160118156967 and RDYR160404161474), and UOW Vice-Chancellor’s Postdoctoral Research Fellowship.International Journal of Advanced Manufacturing Technology2019-04-01hj2018Electrical, Electronic and Computer Engineerin

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications