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Phase-contrast imaging is a method of imaging widely used in biomedical research and applications. It is a label-free method that exploits intrinsic differences in the refractive index of different tissues to differentiate between biological structures under analysis. The basic principle of phase-contrast imaging has inspired a lot of implementations that are suited for different applications. This thesis explores multiple novel implementations of phase-contrast imaging in the following order. 1, We combined scanning Oblique Back-illumination Microscope (sOBM) and confocal microscope to produce phase and fluorescence contrast images in an endomicroscopy configuration. This dual-modality design provides co-registered, complementary labeled and unlabeled contrast of the sample. We further miniaturized the probe by dispensing the two optical fibers in our old design. And we presented proof of principle demonstrations with ex-vivo mouse colon tissue. 2, Then we explored sOBM-based phase and amplitude contrast imaging under different wavelengths. Hyperspectral imaging is achieved by multiplexing a wide-range supercontinuum laser with a Michaelson interferometer (similar to Fourier transform spectroscopy). It features simultaneous acquisition of hyperspectral phase and amplitude images with arbitrarily thick scattering biological samples. Proof-of-principle demonstrations are presented with chorioallantoic membrane of a chick embryo, illustrating the possibility of high-resolution hemodynamics imaging in thick tissue. 3, We focused on increasing the throughput of flow cytometry with principle of phase-contrast imaging and compressive sensing. By utilizing the linearity of scattered patterns under partially coherent illumination, our cytometer can detect multiple objects in the same field of view. By utilizing an optimized matched filter on pupil plane, it also provides increased information capacity of each measurement without sacrificing speed. We demonstrated a throughput of over 10,000 particles/s with accuracy over 91% in our results. 4, A fourth part, which describes the principle and preliminary results of a computational fluorescence endomicroscope is also included. It uses a numerical method to achieve sectioning effect and renders a pseudo-3D image stack with a single shot. The results are compared with true-3D image stack acquired with a confocal microscope.

Frequently, testing of Single Photon Emission Computed Tomography (SPECT) motion correction algorithms is done either by using simplistic deformations that do not accurately simulate true patient motion or by applying the algorithms directly to data acquired from a real patient, where the true internal motion is unknown. In this work, we describe a way to combine these two approaches by using imaging data acquired from real volunteers to simulate the data that the motion correction algorithms would normally observe. The goal is to provide an assessment framework which can both: simulate realistic SPECT acquisitions that incorporate realistic body deformations and provide a ground truth volume to compare against. Every part of the motion correction algorithm needs to be exercised: from parameter estimation of the motion model, to the final reconstruction results. In order to build the ground truth anthropomorphic numerical phantoms, we acquire high resolution MRI scans and motion observation data of a volunteer in multiple different configurations. We then extract the organ boundaries using thresholding, active contours, and morphology. Phantoms of radioactivity uptake and density inside the body can be generated from these boundaries to be used to simulate SPECT acquisitions. We present results on extraction of the ribs, lungs, heart, spine, and the rest of the soft tissue in the thorax using our segmentation approach. In general, extracting the lungs, heart, and ribs in images that do not contain the spine works well, but the spine could be better extracted using other methods that we discuss. We also go in depth into the software development component of this work, describing the C++ coding framework we used and the High Level Interactive GUI Language (HLING). HLING solved a lot of problems but introduced a fair bit of its own. We include a set of requirements to provide a foundation for the next attempt at developing a declarative and minimally restrictive methodology for writing interactive image processing applications in C++ based on lessons learned during the development of HLING.

In the medical field, the detection and diagnosis of diseases continue to improve. Developments in diagnostic techniques have helped to improve treatment in the early stages and avoid many risks to patients. One relatively new diagnostic technique is optical coherence tomography (OCT), which is used in many medical applications to perform internal microstructure imaging of the human body at high resolution (typically 10 micro metre), at high speed and in real time. OCT is non-invasive and can be used as a contact or non-contact technique to obtain an image. In medicine, there are many applications that involve OCT, such as in ophthalmology, gastroenterology, cardiology and oncology. This work demonstrates the design, development and implementation of a high resolution swept laser OCT system for the imaging and diagnosis of tissues in laboratory and clinical experiments. It reports an investigation to measure the thickness of the peritoneal membrane and the use of optical imaging contrast agents such as gold nanorods. There is also an account of the design of an endoscope-catheter fast scanning OCT system for biomedical studies of the gastrointestinal tract and gynaecological areas. These results were achieved by using a swept tuneable laser source with a very high tuning speed of 16 kHz over a wide range of wavelengths: 1260 nm to 1390 nm. The laser sweeps across 110 nm at a 16 kHz repetition rate. The real axial line speed is limited by the source that is used in the OCT system. The axial resolution of the system is 7 µm and its transverse resolution is 15 µm. The bandwidth of the source is up to DeltaGamma = 110 nm, centred at Gamma0 = 1325 nm, and the coherent length is 7 µm. On the sample arm of the interferometer, the swept laser OCT technique is combined with an optical probe and endoscope in order to develop a novel diagnostic imaging device to visualize tissue in vivo for animal and human experimental trials.

From an engineering background, it is often believed that the human anatomy has already been fully described. Radiology has greatly contributed to understand the inside of the human body without surgical intervention. Despite great advances in clinical CT scanning, image quality is still related to a limited amount X-ray exposure for the patient safety. This limitation prevents fine anatomical structures to be visible and, more importantly, to be detected. Where such modality is of great advantage for screening patients, extracting parameters like surface area and volume implies the bone structure to be large enough in relation to the scan resolution. The mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented on a CT scan. Any statistical analysis will be biased towards air cells of smaller size. To allow a complete representation of the mastoid air cell system, a micro-CT scanner is more adequate. Micro-CT scanning uses approximately the same amount of X-rays but for a much longer exposure time compared to what is normally allowed for patients. Human temporal bone specimens are therefore necessary when using such scanning method. Where the conventional clinical CT scanner lacks level of minutes details, micro-CT scanning provides an overwhelming amount of fine details. Prior to any image analysis of medical data, visualization of the data is often needed to learn how to extract the structures of interest for further processing. Visualization of micro-CT scans is of no exception. Due to the high resolution nature of the data, visualization of such data not only requires modern and powerful computers, but also necessitates a tremendous amount of time to adjust the hiding of irrelevant structures, to find the correct orientation, while emphasising the structure of interest. Once the quality of the data has been assessed, and a strategy for the image processing has been decided, the image processing can start, to in turn extract metrics such as the surface area or volume and draw statistics from it. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this bone awakens the curiosity of the experimenter, especially with the correct visualization settings. This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously applied for conventional clinical CT scannings. The study compared current resul s with previous studies, with successive downsampling the data down to a resolution found in conventional clinical CT scanning. The results from the statistical analysis showed that all the small mastoid air cells, that cannot be detected in conventional clinical CT scans, do heavily contribute to the estimation of the surface area, and in consequence to the estimation of the surface area to volume ratio by a factor of about 2.6. Such a result further strengthens the idea of the mastoid to play an active role in pressure regulation and gas exchange. Discovery of micro-channels through specific use of a non-traditional transfer function was then reported, where a qualitative and a quantitative preanalysis was performed are described. To gain more knowledge about these micro-channels, a local structure tensor analysis was applied where structures are described in terms of planar, tubular, or isotropic structures. The results from this structural tensor analysis, also reported in this thesis, suggest these micro-channels to potentially be part of a more complex framework, which hypothetically would provide a separate blood supply for the mucosa lining the mastoid air cell system.

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Two brain segmentation approaches based on Hidden Markov Models are proposed. The first approach aims to segment normal brain 3D multi-channel MR images into three tissues WM, GM, and CSF. Linear Discriminant Analysis, LDA, is applied to separate voxels belonging to different tissues as well as to reduce their features vector size. The second approach aims to detect MS lesions in Brain 3D multi-channel MR images and to label WM, GM, and CSF tissues. Preprocessing is applied in both approaches to reduce the noise level and to address sudden intensity and global intensity correction. The proposed techniques are tested using 3D images from Montereal BrainWeb data set. In the first approach, the results were numerically assessed and compared to results reported using techniques based on single channel data and applied to the same data sets. The results obtained using the multi channel HMM-based algorithm were better than the results reported for single channel data in terms of an objective measure of overlap, Dice coefficient, compared to other methods. In the second approach, the segmentation accuracy is measured using Dice coefficient and total lesions load percentage

The propagation mechanism of signals for multiple input multiple output (MIMO) channels can be explained via a random matrix. Random matrix theory is a very powerful tool to understand behaviour of such channels and analyse their performance measure of MIMO systems. In this work we study: The asymptotic eigenvalue distribution and the mutual information of a multiuser (MU) multiple-input multiple output (MIMO) channel with a certain fraction of users experiencing line-of-sight. It shows that the AED of the channel matrix decomposes into two separate bulks for practically relevant parameter choices and differs very much from the common assumption of independent identically distributed (iid) entries which induces the quarter circle law. This happens even without antenna correlation at either side of the channel. In order to tackle this problem the paper makes use of recent developments in free probability theory which allow to deal with complex-valued eigenvalue distributions of non-Hermitian matrices. Moreover to understand behaviour of MIMO channels we derived asymptotic complex-valued eigenvalue distributions of practically relevant channels models by means of their respective square equivalent and singular equivalent of channel matrices. Finally we derived an explicit mutual information formula which allows us calculate the mutual information (in general) analytically in high signal-to noiseratio (SNR) regime for numerous practical important scenarios. Furthermore the numerical result shows that, high-SNR approximation draws reliable portrait even for quite moderate SNR level.

Reverberation is defined as equally-spaced, bright linear echoes resulting from reflection from specular-type interfaces. They are provoked by the acoustic Impedance change between the tissue and transducer front surface. B. Angelsen developed a mathematical approach to correct this ultrasound artifact by coupling the ultrasound transducer with an ideal electrical load in order to obtain zero reflection coefficients on the transducer from face [1]. However, when analyzing Impedance spectroscopy this approach cannot be achieved by using passive electronics in most simulated cases. Active impedance may be seen as a trend to improve image quality. This research therefore implements and simulates methods for active impedance synthesis [5] and applies Particle Swarm Optimization to the proposed electronics. Finally, the simulations are compared through a LAB experiment.

The commonly used simulation method Field II, which is based on the spatial impulse response approach, has excellent accuracy in linear domain. However the computational time can be up to many days for one simulation. One of the solutions to this problem is a convolution-based methodology called COLE. It is much faster than Field II and has very good approximation. It generates the data by reducing multi-dimensional convolution model to multiple single-dimensional convolutions. This thesis is about implementing COLE on the FieldSim 3 platform and using it for blood flow imaging. This platform is written in MATLAB with object-oriented programming and it is now under development at department of circulation and medical imaging. Both Field II and real scanner have been used to compare with COLE. The simulated phantom for both simulators was a straight tube with scatterers moving inside, whereas a string phantom was used to get the data from the scanner. The computational time of COLE with 2D Doppler mode scan in FieldSim 3 achieved 85 times faster than Field II. The plotted PW Doppler spectra and the 2D power spectra showed that the velocity resolutions of both simulators were at the same level. COLE had higher noise floor than Field II and scanner in Doppler mode scan. COLE had relatively high sampling frequency requirement compared with Field II. If the sampling frequency was not high enough, COLE would produce side lobes in the PW Doppler spectra.