Photoacoustic imaging in biomedicine

Photoacoustic imaging (also called optoacoustic or thermoacoustic imaging) has the potential to image animal or human organs, such as the breast and the brain, with simultaneous high contrast and high spatial resolution. This article provides an overview of the rapidly expanding field of photoacoustic imaging for biomedical applications. Imaging techniques, including depth profiling in layered media, scanning tomography with focused ultrasonic transducers, image forming with an acoustic lens, and computed tomography with unfocused transducers, are introduced. Special emphasis is placed on computed tomography, including reconstruction algorithms, spatial resolution, and related recent experiments. Promising biomedical applications are discussed throughout the text, including (1) tomographic imaging of the skin and other superficial organs by laser-induced photoacoustic microscopy, which offers the critical advantages, over current high-resolution optical imaging modalities, of deeper imaging depth and higher absorptioncontrasts, (2) breast cancerdetection by near-infrared light or radio-frequency–wave-induced photoacoustic imaging, which has important potential for early detection, and (3) small animal imaging by laser-induced photoacoustic imaging, which measures unique optical absorptioncontrasts related to important biochemical information and provides better resolution in deep tissues than optical imaging

Full-wave Nonlinear Inverse Scattering for Acoustic and Electromagnetic Breast Imaging.

Acoustic and electromagnetic full-wave nonlinear inverse scattering techniques are explored in both theory and experiment with the ultimate aim of noninvasively mapping the material properties of the breast. There is evidence that benign and malignant breast tissue have different acoustic and electrical properties and imaging these properties directly could provide higher quality images with better diagnostic certainty. In this dissertation, acoustic and electromagnetic inverse scattering algorithms are first developed and validated in simulation. The forward solvers and optimization cost functions are modified from traditional forms in order to handle the large or lossy imaging scenes present in ultrasonic and microwave breast imaging. An antenna model is then presented, modified, and experimentally validated for microwave S-parameter measurements. Using the antenna model, a new electromagnetic volume integral equation is derived in order to link the material properties of the inverse scattering algorithms to microwave S-parameters measurements allowing direct comparison of model predictions and measurements in the imaging algorithms. This volume integral equation is validated with several experiments and used as the basis of a free-space inverse scattering experiment, where images of the dielectric properties of plastic objects are formed without the use of calibration targets. These efforts are used as the foundation of a solution and formulation for the numerical characterization of a microwave near-field cavity-based breast imaging system. The system is constructed and imaging results of simple targets are given. Finally, the same techniques are used to explore a new self-characterization method for commercial ultrasound probes. The method is used to calibrate an ultrasound inverse scattering experiment and imaging results of simple targets are presented. This work has demonstrated the feasibility of quantitative microwave inverse scattering by way of a self-consistent characterization formalism, and has made headway in the same area for ultrasound.Ph.D.Applied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91585/1/mshaynes_1.pd

On the 3D electromagnetic quantitative inverse scattering problem: algorithms and regularization

In this thesis, 3D quantitative microwave imaging algorithms are developed with emphasis on efficiency of the algorithms and quality of the reconstruction. First, a fast simulation tool has been implemented which makes use of a volume integral equation (VIE) to solve the forward scattering problem. The solution of the resulting linear system is done iteratively. To do this efficiently, two strategies are combined. First, the matrix-vector multiplications needed in every step of the iterative solution are accelerated using a combination of the Fast Fourier Transform (FFT) method and the Multilevel Fast Multipole Algorithm (MLFMA). It is shown that this hybridMLFMA-FFT method is most suited for large, sparse scattering problems. Secondly, the number of iterations is reduced by using an extrapolation technique to determine suitable initial guesses, which are already close to the solution. This technique combines a marching-on-in-source-position scheme with a linear extrapolation over the permittivity under the form of a Born approximation. It is shown that this forward simulator indeed exhibits a better efficiency. The fast forward simulator is incorporated in an optimization technique which minimizes the discrepancy between measured data and simulated data by adjusting the permittivity profile. A Gauss-Newton optimization method with line search is employed in this dissertation to minimize a least squares data fit cost function with additional regularization. Two different regularization methods were developed in this research. The first regularization method penalizes strong fluctuations in the permittivity by imposing a smoothing constraint, which is a widely used approach in inverse scattering. However, in this thesis, this constraint is incorporated in a multiplicative way instead of in the usual additive way, i.e. its weight in the cost function is reduced with an improving data fit. The second regularization method is Value Picking regularization, which is a new method proposed in this dissertation. This regularization is designed to reconstruct piecewise homogeneous permittivity profiles. Such profiles are hard to reconstruct since sharp interfaces between different permittivity regions have to be preserved, while other strong fluctuations need to be suppressed. Instead of operating on the spatial distribution of the permittivity, as certain existing methods for edge preservation do, it imposes the restriction that only a few different permittivity values should appear in the reconstruction. The permittivity values just mentioned do not have to be known in advance, however, and their number is also updated in a stepwise relaxed VP (SRVP) regularization scheme. Both regularization techniques have been incorporated in the Gauss-Newton optimization framework and yield significantly improved reconstruction quality. The efficiency of the minimization algorithm can also be improved. In every step of the iterative optimization, a linear Gauss-Newton update system has to be solved. This typically is a large system and therefore is solved iteratively. However, these systems are ill-conditioned as a result of the ill-posedness of the inverse scattering problem. Fortunately, the aforementioned regularization techniques allow for the use of a subspace preconditioned LSQR method to solve these systems efficiently, as is shown in this thesis. Finally, the incorporation of constraints on the permittivity through a modified line search path, helps to keep the forward problem well-posed and thus the number of forward iterations low. Another contribution of this thesis is the proposal of a new Consistency Inversion (CI) algorithm. It is based on the same principles as another well known reconstruction algorithm, the Contrast Source Inversion (CSI) method, which considers the contrast currents – equivalent currents that generate a field identical to the scattered field – as fundamental unknowns together with the permittivity. In the CI method, however, the permittivity variables are eliminated from the optimization and are only reconstructed in a final step. This avoids alternating updates of permittivity and contrast currents, which may result in a faster convergence. The CI method has also been supplemented with VP regularization, yielding the VPCI method. The quantitative electromagnetic imaging methods developed in this work have been validated on both synthetic and measured data, for both homogeneous and inhomogeneous objects and yield a high reconstruction quality in all these cases. The successful, completely blind reconstruction of an unknown target from measured data, provided by the Institut Fresnel in Marseille, France, demonstrates at once the validity of the forward scattering code, the performance of the reconstruction algorithm and the quality of the measurements. The reconstruction of a numerical MRI based breast phantom is encouraging for the further development of biomedical microwave imaging and of microwave breast cancer screening in particular

Photoacoustic computed tomography in biological tissues: algorithms and breast imaging

Photoacoustic computed tomography (PAT) has great potential for application in the biomedical field. It best combines the high contrast of electromagnetic absorption and the high resolution of ultrasonic waves in biological tissues. In Chapter II, we present time-domain reconstruction algorithms for PAT. First, a formal reconstruction formula for arbitrary measurement geometry is presented. Then, we derive a universal and exact back-projection formula for three commonly used measurement geometries, including spherical, planar and cylindrical surfaces. We also find this back-projection formula can be extended to arbitrary measurement surfaces under certain conditions. A method to implement the back-projection algorithm is also given. Finally, numerical simulations are performed to demonstrate the performance of the back-projection formula. In Chapter III, we present a theoretical analysis of the spatial resolution of PAT for the first time. The three common geometries as well as other general cases are investigated. The point-spread functions (PSF's) related to the bandwidth and the sensing aperture of the detector are derived. Both the full-width-at-half-maximum of the PSF and the Rayleigh criterion are used to define the spatial resolution. In Chapter IV, we first present a theoretical analysis of spatial sampling in the PA measurement for three common geometries. Then, based on the sampling theorem, we propose an optimal sampling strategy for the PA measurement. Optimal spatial sampling periods for different geometries are derived. The aliasing effects on the PAT images are also discussed. Finally, we conduct numerical simulations to test the proposed optimal sampling strategy and also to demonstrate how the aliasing related to spatially discrete sampling affects the PAT image. In Chapter V, we first describe a prototype of the RF-induced PAT imaging system that we have built. Then, we present experiments of phantom samples as well as a preliminary study of breast imaging for cancer detection

Photoacoustic imaging in biomedicine

Photoacoustic imaging (also called optoacoustic or thermoacoustic imaging) has the potential to image animal or human organs, such as the breast and the brain, with simultaneous high contrast and high spatial resolution. This article provides an overview of the rapidly expanding field of photoacoustic imaging for biomedical applications. Imaging techniques, including depth profiling in layered media, scanning tomography with focused ultrasonic transducers, image forming with an acoustic lens, and computed tomography with unfocused transducers, are introduced. Special emphasis is placed on computed tomography, including reconstruction algorithms, spatial resolution, and related recent experiments. Promising biomedical applications are discussed throughout the text, including (1) tomographic imaging of the skin and other superficial organs by laser-induced photoacoustic microscopy, which offers the critical advantages, over current high-resolution optical imaging modalities, of deeper imaging depth and higher absorptioncontrasts, (2) breast cancerdetection by near-infrared light or radio-frequency–wave-induced photoacoustic imaging, which has important potential for early detection, and (3) small animal imaging by laser-induced photoacoustic imaging, which measures unique optical absorptioncontrasts related to important biochemical information and provides better resolution in deep tissues than optical imaging

UWB linear array for 3D microwave imaging

Microwave imaging is being investigated by many research groups for its po- tential in medical diagnostic and treatment elds. Microwave signals are to sensitive to small changes into the dielectric properties of the objects under test which can be related to their physiological state. The use of UWB signals ( 3.1 - 10.6 GHz ) allow a good compromise between penetration and resolution. Moreover in the last years UWB frequency range has strongly exploited in med- ical imaging research eld, due to its characteristics of low-cost implementation and low-power used. The objective of this work is the design and the fabrication of multiplexed slot antenna array, that will be used in a 3D arrayed microwave tomographic system for medical imaging. This system is characterized by a rotation of mul- tiplexed linear antenna array around the object under test. With the aim of reducing the acquisition time it is replaced the vertical movement with a real antenna array. In the chapter 1 the introduction of medical imaging will be pre- sented and the system requirements are described. In the chapters 2 the basic concepts of UWB microstrip slot antennas and feeding methods will be analyzed respectively. In chapter 3 the individual array elements will be chosen and two array con gurations will be designed. In chapter 4 the feeding networks will be planned and simulated, the prototypes will be fabricated and the simulation data will be compared with measurement results. Finally the calibration will be done and the array con guration with the best performance will be chosen. In chapter 5 the conclusions will be deduced and the future works will be proposed

Hybrid-dual-fourier tomographic algorithm for a fast three-dimensionial optical image reconstruction in turbid media

A reconstruction technique for reducing computation burden in the 3D image processes, wherein the reconstruction procedure comprises an inverse and a forward model. The inverse model uses a hybrid dual Fourier algorithm that combines a 2D Fourier inversion with a 1D matrix inversion to thereby provide high-speed inverse computations. The inverse algorithm uses a hybrid transfer to provide fast Fourier inversion for data of multiple sources and multiple detectors. The forward model is based on an analytical cumulant solution of a radiative transfer equation. The accurate analytical form of the solution to the radiative transfer equation provides an efficient formalism for fast computation of the forward model

Simulation and Design of an UWB Imaging System for Breast Cancer Detection

Breast cancer is the most frequently diagnosed cancer among women. In recent years, the mortality rate due to this disease is greatly decreased thanks to both enormous progress in cancer research, and screening campaigns which have allowed the increase in the number of early diagnoses of the disease. In fact, if the tumor is identied in its early stage, e.g. when it has a diameter of less than one centimeter, the possibility of a cure can reach 93%. However, statistics show that more young aged women are suered breast cancer. The goal of screening exams for early breast cancer detection is to nd cancers before they start to cause symptoms. Regular mass screening of all women at risk is a good option to achieve that. Instead of meeting very high diagnostic standards, it is expected to yield an early warning, not a denitive diagnosis. In the last decades, X-ray mammography is the most ecient screening technique. However, it uses ionizing radiation and, therefore, should not be used for frequent check-ups. Besides, it requires signicant breast compression, which is often painful. In this scenario many alternative technologies were developed to overcome the limitations of mammography. Among these possibilities, Magnetic Resonance Imaging (MRI) is too expensive and time-consuming, Ultrasound is considered to be too operatordependent and low specicity, which are not suitable for mass screening. Microwave imaging techniques, especially Ultra WideBand (UWB) radar imaging, is the most interesting one. The reason of this interest relies on the fact that microwaves are non-ionizing thus permitting frequent examinations. Moreover, it is potentially lowcost and more ecient for young women. Since it has been demonstrated in the literatures that the dielectric constants between cancerous and healthy tissues are quite dierent, the technique consists in illuminating these biological tissues with microwave radiations by one or more antennas and analyzing the re ected signals. An UWB imaging system consists of transmitters, receivers and antennas for the RF part, the transmission channel and of a digital backend imaging unit for processing the received signals. When an UWB pulse strikes the breast, the pulse is re ected due to the dielectric discontinuity in tissues, the bigger the dierence, the bigger the backscatter. The re ected signals are acquired and processed to create the energy maps. This thesis aims to develop an UWB system at high resolution for the detection of carcinoma breast already in its initial phase. To favor the adoption of this method in screening campaigns, it is necessary to replace the expensive and bulky RF instrumentation used so far with ad-hoc designed circuits and systems. In order to realize that, at the very beginning, the overall system environment must be built and veried, which mainly consists of the transmission channel{the breast model and the imaging unit. The used transmission channel data come from MRI of the prone patient. In order to correctly use this numerical model, a simulator was built, which was implemented in Matlab, according to the Finite-Dierence-Time- Domain (FDTD) method. FDTD algorithm solves the electric and magnetic eld both in time and in space, thus, simulates the propagation of electromagnetic waves in the breast model. To better understand the eect of the system non-idealities, two 2D breast models are investigated, one is homogeneous, the other is heterogeneous. Moreover, the modeling takes into account all critical aspects, including stability and medium dispersion. Given the types of tissues under examination, the frequency dependence of tissue dielectric properties is incorporated into wideband FDTD simulations using Debye dispersion parameters. A performed further study is in the implementation of the boundary conditions. The Convolution Perfectly Matched Layer (CPML) is used to implement the absorbing boundaries. The objective of the imaging unit is to obtain an energy map representing the amount of energy re ected from each point of the breast, by recombining the sampled backscattered signals. For this purpose, the study has been carried out on various beamforming in the literature. The basic idea is called as "delay and sum", which is to align the received signals in such a way as to focus a given point in space and then add up all the contributions, so as to obtain a constructive interference at that point if this is a diseased tissue. In this work, Microwave Imaging via Space Time (MIST) Beamforming algorithm is applied, which is based on the above principle and add more elaborations of the signals in order to make the algorithm less sensitive to propagation phenomena in the medium and to the non-idealities of the system. It is divided into two distinct steps: the rst step, called SKin Artifact Removal (SKAR), takes care of removing the contributions from the signal caused by the direct path between the transmitter and receiver, the re ection of skin, as they are orders of magnitude higher compared to the re ections caused by cancers; the second step, which is BEAmForming (BEAF), performs the algorithm of reconstruction by forming a weighted combination of time delayed version of the calibrated re ected signals. As discussed above, more attention must be paid on the implementation of the ad-hoc integration circuits. In this scenario, due to the strict requirements on the RF receiver component, two dierent approaches of the implementation of the RF front-end, Direct Conversion (DC) receiver and Coherent Equivalent Time Sampling (CETS) receiver are compared. They are modeled behaviorally and the eects of various impairments, such as thermal, jitter, and phase noise, as well as phase inaccuracies, non-linearity, ADC quantization noise and distortion, on energy maps and on quantitative metrics such as SCR and SMR are evaluated. Dierential Gaussian pulse is chosen as the exciting source. Results show that DC receiver performs higher sensitivity to phase inaccuracies, which makes it less robust than the CETS receiver. Another advantage of the CETS receiver is that it can work in time domain with UWB pulses, other than in frequency domain with stepped frequency continuous waves like the DC one, which reduces the acquisition time without impacting the performance. Based on the results of the behavioral simulations, low noise amplier (LNA) and Track and Hold Amplier (THA) can be regarded as the most critical parts for the proposed CETS receiver, as well as the UWB antenna. This work therefore focuses on their hardware implementations. The LNA, which shows critical performance limitation at bandwidth and noise gure of receiver, has been developed based on common-gate conguration. And the THA based on Switched Source Follower (SSF) scheme has been presented and improved to obtain high input bandwidth, high sampling rate, high linearity and low power consumption. LNA and THA are implemented in CMOS 130nm technology and the circuit performance evaluation has been taken place separately and together. The small size UWB wide-slot antenna is designed and simulated in HFSS. Finally, in order to evaluate the eect of the implemented transistor level components on system performance, a multi-resolution top-down system methodology is applied. Therfore, the entire ow is analyzed for dierent levels of the RF frontend. Initially the system components are described behaviorally as ideal elements. The main activity consists in the analysis and development of the entire frontend system, observing and complementing each other blocks in a single ow simulation, clear and well-dened in its various interfaces. To achieve that the receiver is modeled and analyzed using VHDL-AMS language block by block, moreover, the impact of quantization, noise, jitter, and non-linearity is also evaluated. At last, the behavioral description of antenna, LNA and THA is replaced with a circuit-level one without changing the rest of the system, which permits a system-level assessment of low-level issues

An Integration Between SVM Classifiers and Multi-Resolution Techniques for Early Breast Cancer Detection

Because of the high contrast between the dielectric properties of normal and malignant breast tissues at microwave frequencies, microwave imaging techniques seem to be very attractive diagnosis methods for cancer detection [1][2]. In such a framework, inverse scattering methods are very promising tools, but their practical application is strongly limited by the need of 3D reconstructions, high spatial resolutions, and fast processing. Recently, to reduce the high computational costs and to fit the real‐time requirements, inversion methods based on learning by example techniques have been proposed [3]. LBE approaches based on support vector machines (SVMs) [3] and neural networks (NNs) [4] have been satisfactorily applied in various and complex electromagnetic problems. When dealing with breast cancer detection, the inversion process is recast as a classification or regression problem where the unknowns are retrieved from the data (i.e., the electric field samples collected in an external observation domain) by approximating the unknown relation data‐unknowns through an off‐line data fitting procedure (training phase). Once the training procedure (performed once and off‐line) is completed, the characteristics of the malignant breast tissue are real‐time estimated in the testing phase. In such a work, the detection problem is addressed by integrating a SVM‐based classifier with an iterative multi‐zooming procedure. More in detail, a succession of approximations of a probability map of the presence of pathology is determined. At each step, the spatial resolution of the risk‐map is improved in a limited set of regions of interest (ROIs) defined at the previous zooming step and characterized by a greater value of the occurrence probability of a malignant tissue. The multi‐step procedure is stopped when a stationary condition on the probability and on the number of ROIs is reached. The achievable trade‐off between computational complexity and spatial resolution is preliminary assessed by discussing a selected set of numerical simulations concerned with both noiseless as well as corrupted data