Optical Signal Processing For Data Compression In Ultrafast Measurement

Today the world is filled with continuous deluge of digital information which are ever increasing by every fraction of second. Real-time analog information such as images, RF signals needs to be sampled and quantized to represent in digital domain with help of measurement systems for information analysis, further post processing and storage. Photonics offers various advantages in terms of high bandwidth, security, immunity to electromagnetic interference, reduction in frequency dependant loss as compared to conventional electronic measurement systems. However the large bandwidth data needs to be acquired as per Nyquist principle requiring high bandwidth electronic sampler and digitizer. To address this problem, Photonic Time Stretch has been introduced to reduce the need for high speed electronic measurement equipment by significantly slowing down the speed of sampling signal. However, this generates massive data volume. Photonics-assisted methods such as Anamorphic Stretch Transform, Compressed Sensing and Fourier spectrum acquisition sensing have been addressed to achieve data compression while sampling the information. In this thesis, novel photonic implementations of each of these methods have been investigated through numerical and experimental demonstrations. The main contribution of this thesis include (1) Application of photonic implementation of compressed sensing for Optical Coherence Tomography, Fiber Bragg Grating enabled signal sensing and blind spectrum sensing applications (2) Photonic compressed sensing enabled ultra-fast imaging system (3) Fourier spectrum acquisition for RF spectrum sensing with all-optical approach (4) Adaptive non-uniform photonic time stretch methods using anamorphic stretch transform to reduce the the number of samples to be measured

Optical Signal Processing For Data Compression In Ultrafast Measurement

Today the world is filled with continuous deluge of digital information which are ever increasing by every fraction of second. Real-time analog information such as images, RF signals needs to be sampled and quantized to represent in digital domain with help of measurement systems for information analysis, further post processing and storage. Photonics offers various advantages in terms of high bandwidth, security, immunity to electromagnetic interference, reduction in frequency dependant loss as compared to conventional electronic measurement systems. However the large bandwidth data needs to be acquired as per Nyquist principle requiring high bandwidth electronic sampler and digitizer. To address this problem, Photonic Time Stretch has been introduced to reduce the need for high speed electronic measurement equipment by significantly slowing down the speed of sampling signal. However, this generates massive data volume. Photonics-assisted methods such as Anamorphic Stretch Transform, Compressed Sensing and Fourier spectrum acquisition sensing have been addressed to achieve data compression while sampling the information. In this thesis, novel photonic implementations of each of these methods have been investigated through numerical and experimental demonstrations. The main contribution of this thesis include (1) Application of photonic implementation of compressed sensing for Optical Coherence Tomography, Fiber Bragg Grating enabled signal sensing and blind spectrum sensing applications (2) Photonic compressed sensing enabled ultra-fast imaging system (3) Fourier spectrum acquisition for RF spectrum sensing with all-optical approach (4) Adaptive non-uniform photonic time stretch methods using anamorphic stretch transform to reduce the the number of samples to be measured

Quantification and Reconstruction in Photoacoustic Tomography

Optical absorption is closely associated with many physiological important parameters, such as the concentration and oxygen saturation of hemoglobin. Conventionally, accurate quantification in PAT requires knowledge of the optical fluence attenuation, acoustic pressure attenuation, and detection bandwidth. We circumvent this requirement by quantifying the optical absorption coefficients from the acoustic spectra of PA signals acquired at multiple optical wavelengths. We demonstrate the method using the optical-resolution photoacoustic microscopy: OR-PAM) and the acoustical-resolution photoacoustic microscopy: AR-PAM) in the optical ballistic regime and in the optical diffusive regime, respectively. The data acquisition speed in photoacoustic computed tomography: PACT) is limited by the laser repetition rate and the number of parallel ultrasound detecting channels. Reconstructing an image with fewer measurements can effectively accelerate the data acquisition and reduce the system cost. We adapted Compressed Sensing: CS) for the reconstruction in PACT. CS-based PACT was implemented as a non-linear conjugate gradient descent algorithm and tested with both phantom and in vivo experiments. Speckles have been considered ubiquitous in all scattering-based coherent imaging technologies. As a coherent imaging modality based on optical absorption, photoacoustic: PA) tomography: PAT) is generally devoid of speckles. PAT suppresses speckles by building up prominent boundary signals, via a mechanism similar to that of specular reflection. When imaging smooth boundary absorbing targets, the speckle visibility in PAT, which is defined as the ratio of the square root of the average power of speckles to that of boundaries, is inversely proportional to the square root of the absorber density. If the surfaces of the absorbing targets have uncorrelated height fluctuations, however, the boundary features may become fully developed speckles. The findings were validated by simulations and experiments. The first- and second-order statistics of PAT speckles were also studied experimentally. While the amplitude of the speckles follows a Gaussian distribution, the autocorrelation of the speckle patterns tracks that of the system point spread function

Joint Image Reconstruction and Segmentation Using the Potts Model

We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford-Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation. We focus on Radon data, where we in particular consider limited data situations. For instance, our method is able to recover all segments of the Shepp-Logan phantom from $7$ angular views only. We illustrate the practical applicability on a real PET dataset. As further applications, we consider spherical Radon data as well as blurred data

Photonic Time-Stretch Enabled High Throughput Microwave and MM-Wave Interferometry Applied to Fibre Grating Sensors and Non-Contact Measurement

The research presented in this thesis is focused towards developing real-time, high-speed applications, employing ultrafast optical microwave generation and characterisation techniques. This thesis presents a series of experiments wherein mode-locked laser pulses are utilised. Photonics-based microwave and MM-Wave generation and detection are explored and employed for applications pertaining to fibre grating sensors and non-contact measurement. The application concepts leverage techniques from optical coherence tomography and non-destructive evaluation of turbid media. In particular, I use the principle of dispersion-induced photonic Time-Stretch to slow down high-speed waveforms to speeds usable by state-of-the-art photo-detectors and digital signal processors. The concept of photonic time-stretch is applied to map instantaneous microwave frequency to the time instant of the signal, which in turn is related to spatial location as established by the space-wavelength-time conversions. The experimental methods applied throughout this thesis is based upon Michelson interferometer architecture. My original contribution to knowledge is the realisation of Photonics-based, single tone, and chirped microwave and MM-Wave pulse generation applied to deciphering physical strain profile along the length of a chirped fibre Bragg grating employed in a Michelson interferometer configuration. This interrogation scheme allows intra-grating high-resolution, high-speed, and temperature independent strain measurement. This concept is further extended to utilise photonic generation of microwave pulses to characterise surface profile information of thin film and thin plate infrared transparent slides of variable thickness setup in a Michelson interferometer architecture. The method basis for photonically generated high-frequency microwave signals utilises the principle of photonic Time-Stretch. The research was conducted in the Photonics Lab at the University of Kent. In addition, the photonically generated microwave/ MM-Wave pulses is utilised as a potential broadband frequency-swept source for non-contact measurement of turbid media. Investigation of the proof-of-concept based on an MM-Wave coherence tomography set-up is implemented at Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO)

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

ADVANCED INTRAVASCULAR MAGNETIC RESONANCE IMAGING WITH INTERACTION

Intravascular (IV) Magnetic Resonance Imaging (MRI) is a specialized class of interventional MRI (iMRI) techniques that acquire MRI images through blood vessels to guide, identify and/or treat pathologies inside the human body which are otherwise difficult to locate and treat precisely. Here, interactions based on real-time computations and feedback are explored to improve the accuracy and efficiency of IVMRI procedures. First, an IV MRI-guided high-intensity focused ultrasound (HIFU) ablation method is developed for targeting perivascular pathology with minimal injury to the vessel wall. To take advantage of real-time feedback, a software interface is developed for monitoring thermal dose with real-time MRI thermometry, and an MRI-guided ablation protocol developed and tested on muscle and liver tissue ex vivo. It is shown that, with cumulative thermal dose monitored with MRI thermometry, lesion location and dimensions can be estimated consistently, and desirable thermal lesions can be achieved in animals in vivo. Second, to achieve fully interactive IV MRI, high-resolution real-time 10 frames-per-second (fps) MRI endoscopy is developed as an advance over prior methods of MRI endoscopy. Intravascular transmit-receive MRI endoscopes are fabricated for highly under-sampled radial-projection MRI in a clinical 3Tesla MRI scanner. Iterative nonlinear reconstruction is accelerated using graphics processor units (GPU) to achieve true real-time endoscopy visualization at the scanner. The results of high-speed MRI endoscopy at 6-10 fps are consistent with fully-sampled MRI endoscopy and histology, with feasibility demonstrated in vivo in a large animal model. Last, a general framework for automatic imaging contrast tuning over MRI protocol parameters is explored. The framework reveals typical signal patterns over different protocol parameters from calibration imaging data and applies this knowledge to design efficient acquisition strategies and predicts contrasts under unacquired protocols. An external computer in real-time communication with the MRI console is utilized for online processing and controlling MRI acquisitions. This workflow enables machine learning for optimizing acquisition strategies in general, and provides a foundation for efficiently tuning MRI protocol parameters to perform interventional MRI in the highly varying and interactive environments commonly in play. This work is loosely inspired by prior research on extremely accelerated MRI relaxometry using the minimal-acquisition linear algebraic modeling (SLAM) method