Hospitals have limited budgets, making the cost of an examination important. A whole-body MRI scan is much less expensive than a PET-CT scan, making the MRI desirable in cases when the result from the MR machine will be sufficient. Also, unlike CT, MRI does not rely on ionizing radiation, which is known to increase the risk of developing cancer. To make the most out of the MRI results, an efficient visualization of the data is important. The goal of this project was to develop an application that would facilitate radiologists’ evaluation of whole-body MRI data of lymphoma patients. This was achieved by introducing a fused image between two types of MRI images, offering simplified loading of all the study MRI data and creating a rotatable maximum intensity projection from which points can be selected and zoomed to in other types of images. Unfortunately the loading of the data and some parts of the interaction is somewhat slow, which is something that needs to be addressed before this application could become a possibly useful tool for the radiologists.

In this master thesis methods for increasing temporal resolution when reconstructing radially sampled MRI data have been developed and evaluated. This has been done in two steps; first the order in which data is sampled in k-space has been optimized, and second; temporal filters have been developed in order to utilize the high sampling density in central regions of k-space as a result of the polar sampling geometry to increase temporal resolution while maintaining image quality.By properly designing the temporal filters the temporal resolution is increased by a factor 3–20 depending on other variables such as imageresolution and the size of the time varying areas in the image. The results are obtained from simulated raw data and subsequent reconstruction. The next step should be to acquire and reconstruct raw data to confirm the results. / This Master thesis work was performed at Dept. Radiation Physis, Linköping University, but examined at Dept. Radiation Physics, Umeå University

In this thesis, we have used the kernel based orthogonal projection to latent structures (K-OPLS) method to discriminate between Alzheimer's Disease patients (AD) and healthy control subjects (CTL), and to predict conversion from mild cognitive impairment (MCI) to AD. In this regard three cohorts were used to create two different datasets; a small dataset including 63 subjects based on the Alzheimer’s Research Trust (ART) cohort and a large dataset including 1074 subjects combining the AddNeuroMed (ANM) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. In the ART dataset, 34 regional cortical thickness measures and 21 volumetric measures from MRI in addition to 3 metabolite ratios from MRS, altogether 58 variables obtained for 28 AD and 35 CTL subjects. Three different K-OPLS models were created based on MRI and MRS measures and their combination. Combining the MRI and the MRS measures significantly improved the discriminant power resulting in a sensitivity of 96.4% and a specificity of 97.1%. In the combined dataset (ADNI and AddNeuroMed), the Freesurfer pipeline was utilized to extract 34 regional cortical thickness measures and 23 volumetric measures from MRI scans of 295 AD, 335 CTL and 444 MCI subjects. The classification of AD and CTL subjects using the K-OPLS model resulted in a high sensitivity of 85.8% and a specificity of 91.3%. Subsequently, the K-OPLS model was used to prospectively predict conversion from MCI to AD, according to the one year follow up diagnosis. As a result, 78.3% of the MCI converters were classified as AD-like and 57.5% of the MCI non-converters were classified as control-like. Furthermore, an age correction method was proposed to remove the effect of age as a confounding factor. The age correction method successfully removed the age-related changes of the data. Also, the age correction method slightly improved the performance regarding to classification and prediction. This resulted in that 82.1% of the MCI converters were correctly classified. All analyses were performed using 7-fold cross validation. The K-OPLS method shows strong potential for classification of AD and CTL, and for prediction of MCI conversion.

The pumping performance of the heart is affected by the mechanical properties of the muscle fibre part of the cardiac wall, the myocardium. The myocardium has a complex structure, where muscle fibres have different orientations at different locations, and during the cardiac cycle, the myocardium undergoes large elastic deformations. Hence, myocardial strain pattern is complex. In this thesis work, a computation method for myocardial strain and a detailed map of myocardial transmural strain during the cardiac cycle are found by the use of surgically implanted metallic markers and beads. The strain is characterized in a local cardiac coordinate system. Thereafter, non-invasive phase contrast magnetic resonance imaging (PC-MRI) is used to compare strain at different myocardial regions. The difference in resolution between marker data and PC-MRI data is elucidated and some of the problems associated with the low resolution of PC-MRI are given.

In magnetic resonance imaging (MRI), sampling methods that lead to incomplete data coverage of k-space are used to accelerate imaging and reduce overall scan time. Non-Cartesian sampling trajectories such as radial, spiral, and random trajectories are employed to facilitate advanced imaging techniques, such as compressed sensing, or to provide more efficient coverage of k-space for a shorter scan period. When k-space is undersampled or unevenly sampled, traditional methods of transforming Fourier data to obtain the desired image, such as the FFT, may no longer be applicable. The Fourier reconstruction of optical interferometer data (FROID) algorithm is a novel reconstruction method developed by A. R. Hajian that has been successful in the field of optical interferometry in reconstructing images from sparsely and unevenly sampled data. It is applicable to cases where the collected data is a Fourier representation of the desired image or spectrum. The framework presented allows for a priori information, such as the positions of the sampled points, to be incorporated into the reconstruction of images. Initially, FROID assumes a guess of the real-valued spectrum or image in the form of an interpolated function and calculates the corresponding integral Fourier transform. Amplitudes are then sampled in the Fourier space at locations corresponding to the acquired measurements to form a model dataset. The guess spectrum or image is then adjusted such that the model dataset in the Fourier space is least squares fitted to measured values. In this thesis, FROID has been adapted and implemented for use in MRI where k-space is the Fourier transform of the desired image. By forming a continuous mapping of the image and modelling data in the Fourier space, a comparison and optimization with respect to data acquired in k-space that is either undersampled or irregularly sampled can be performed as long as the sampling positions are known. To apply FROID to the reconstruction of magnetic resonance images, an appropriate objective function that expresses the desired least squares fit criteria was defined and the model for interpolating Fourier data was extended to include complex values of an image. When an image with two Gaussian functions was tested, FROID was able to reconstruct images from data randomly sampled in k-space and was not restricted to data sampled evenly on a Cartesian grid. An MR image of a bone with complex values was also reconstructed using FROID and the magnitude image was compared to that reconstructed by the FFT. It was found that FROID outperformed the FFT in certain cases even when data were rectilinearly sampled.

As a non-invasive method, functional MRI (fMRI) has been widely used for human brain mapping. Although many applications have been done, there are still some critical issues associated with fMRI. Perfusion-weighted fMRI (PWI) with exogenous contrast agent suffered from the problems of recirculation, which could contaminate the cerebral blood flow (CBF) estimation and make its ability of prediction "tissue-at-risk" in debate. We propose a rapid and effective method that combines matched-filter-fitting (MFF) and ICA where ICA was used for regions with a prolonged TTP and MFF was utilized for the remaining areas. The calculation of cerebral hemodynamics afterwards demonstrates that the proposed method may lead to a more accurate estimation of CBF. The extent to which CBF is reduced in relationship to normal values has been utilized as an indicator to discern ischemic injury. However, despite the well known difference in CBF between gray and white matter, relatively little attention has been given as to how CBF may be differently altered in gray and white matter during ischemia due to the inability to accurately separate gray and white matter. To this end, we propose a robust clustering method for automatic classification of perfusion compartments. The method is first to apply a robust principal component analysis to reduce dimension and then to use a mixture model of multivariate T distribution for clustering. Our results in ischemic stroke patients at the hyperacute phase show the clear advantage over the conventional technique. BOLD fMRI, as a feasible and preferred method for developmental neuroimaging, is seldom conducted in pediatric subjects and therefore the information about brain functional development in the early age is somewhat lacking. To this end, this dissertation also focuses on how functional brain connectivity may be present in pediatric subjects in a sleeping condition. We propose a statistical method to delineate frequency-dependent brain connectivity among brain activation regions, and an automatic procedure combined with spatial ICA approach to determine the brain functional connectivity. Our results suggest that functional connectivity exists as young as two weeks old for both sensorimotor and visual cortices and that functional connectivity is highly age-dependent. / Dissertation

<p>Breast cancer is currently the most common non-skin cancer and the second leading cause of cancer-related death in women here in the United States. X-ray mammography is currently the standard clinical imaging modality for breast cancer screening and diagnosis due to its high sensitivity and resolution at a low patient dose. With the advancement of breast imaging from analog to digital, quantitative measurements rather than qualitative assessments can be made from these images. One such measurement, mammographic breast density (i.e. the percentage of the entire breast volume that is taken up by dense glandular tissue), has been shown to be a biomarker well correlated with cancer risk. However, a digital mammogram still suffers from its projective nature. The resulting overlap of normal breast tissue can obscure lesions, limit quantitative measurement accuracy, and present false alarms leading to unnecessary recall studies. To address this key limitation, several 3D imaging techniques have been developed such as breast magnetic resonance imaging (MRI), dedicated breast computed tomography (CT), and digital breast tomosynthesis (tomo). Perhaps the most recently developed modality is tomo, which is a limited-angle cone-beam CT of the breast compressed in the same geometry as mammography. Because tomo retains all the aforementioned advantages of mammography but adds depth information and can be built based on an existing digital mammography device, measuring breast density and extracting other quantitative features from tomo images was a major focus of this study.</p><p>Before attempting to measure breast density and other features from reconstructed tomo image volumes, the quantitative potential of this imaging modality was assessed. First, we explored a slice-by-slice technique that measures tissue density using only the information from a single slice from the reconstructed tomosynthesis volume with geometrically simple tissue-equivalent phantoms. Once this task has been satisfactorily performed, we studied a probabilistic approach toward quantitation of the entire 3D volume. Some work has been done previously in the realm of 2D hidden Markov random fields (HMRFs) to categorize mammograms according to their Wolf pattern, detect mammographic lesions, and segment satellite and mixed media (text/photograph) images. For this project, a 3D hidden Markov model (HMM) method was developed and applied to tomo images under the simplified assumption that the possible tissue type of each tomo voxel is either adipose (fatty) or glandular (dense). Because adipose and glandular tissue is easily distinguished in MR images, patient breast MRIs were used to train, validate, and finally to assess the accuracy of our HMM segmentation algorithm when applied to tomo images by comparing the volumetric breast density to the MRI breast density for the same patient. Because they are so often studied conjunctively, several image texture features were calculated and compared between MRI and tomo as well.</p><p>Another aim of our study was to investigate whether changes in macroscopic 3D imaging features (texture and density) can accurately predict the chemoprevention response that was measured with Random Periareolar Fine Needle Aspiration (RPFNA) cytology for a uniquely young high-risk cohort of women. This aim to investigate the potential of combining multi-modality macroscopic 3D imaging information with a cytological measure of risk and then investigating how response to tamoxifen and other chemoprevention treatment affects each of these risk biomarkers in young, high-risk women is completely novel in the fields of medical physics and biomedical engineering.</p> / Dissertation

Each year, more than one million prosthetic vascular grafts are implanted. Well-over 50 % of these artificial vessels are of the small caliber variety with an inner diameter less than or equal to 10 mm. The challenge rests in implanting these synthetic substitutes into a hemodynamic environment with a high downstream resistance and low rates of flow. Over the course of four interrelated studies, we investigated the healing properties of small caliber prosthetic vascular grafts. All of these studies were conducted using baboons. First, we documented the difference in healing response between three different types of vascular grafts: (1) autologous artery (2) allogeneic vessel (3) prosthetic ePTFE. This comparison furnished an important model of graft healing. Proliferating endothelial cells were localized to the top 10 % of the neointima, while the proliferating smooth muscle cells were identified within the lower 10 % of the neointima. Secondly, we examined the effects of changing a prosthetic grafts material properties and how that change impacts healing of the grafts surface. These ultrastructural changes were introduced by radially stretching a porous 60 mm ePTFE vascular graft. Radially stretching the graft material decreased the void fraction, reduced the potential for transmural ingrowth, and changed the healing characteristics of the implanted vessels. Thirdly, we investigated the effect of a changing hemodynamic environment upon the healing of a vascular graft with uniform material properties. The changing hemodynamics were generated with a stenotic model. Under sub-acute conditions, an inverse relationship failed to exist between intimal thickening and wall shear stress. Lastly, the details of this hemodynamic environment were documented with computational fluid dynamics (CFD). The computational grids were constructed using three sets of geometric information: (1) incorporating the ideal material dimensions of the implanted vessel (2) utilizing contour information from pressure-perfused histologic cross-sections (3) applying geometric information form detailed MRI imaging. MRI imaging information provided the best description of the vessels hemodynamic environment. With this computational information, correlations were made between the intimal thickening and hemodynamic parameters.

Cardiovascular Disease (CVD) continues to be the leading cause of death in western countries according to the statistics update by the American Heart Association. Atherosclerosis is estimated to be responsible for a large portion of CVD and affects 60 million people in the United States. Accurate diagnosis is crucial for proper treatment planning. Currently, the clinical standard screening technique for diagnosing atherosclerosis is x-ray angiography, which reveals the residual lumen size. X-ray angiographic images possess good resolution and contrast, however, lumen size is not always a proper criterion given the positive remodeling nature of atherosclerotic plaques. In the past decade, it has been shown that most plaques responsible for a fatal or nonfatal myocardial infarction are less than 70% stenosed. Clinical data support the idea that plaques producing non-flow-limiting stenoses account for more cases of plaque rupture and thrombosis than plaques producing a more severe stenosis. Due to this fact, plaque itself must be imaged in order to assess its vulnerability. A wealth of literature suggests that multicontrast MRI has the potential of characterizing plaque constituents, and thus is a promising technique for plaque imaging. Because of the technical difficulties associated with in-vivo plaque imaging and the fact that our research was aimed at developing new methodologies, our approaches was to image excised coronary arteries under simulated in-vivo conditions in a tissue culture chamber. It is shown by this research that automatic plaque characterization techniques developed under ex-vivo conditions still apply for in-vivo studies. Based on this finding, an automatic plaque characterization technique using multicontrast MRI was developed. Furthermore, "shared k-space" reconstruction techniques were interrogated to assess their feasibility in accelerating multicontrast MRI acquisition. Results show that these techniques are promising in accelerating multicontrast MRI acquisitions.

The most critical problem of NMR spectroscopy and magnetic resonance imaging (MRI) is the relatively low sensitivity compared to other forms of spectroscopy, limiting the applicability of these techniques. Most of the existing research focused on trying to alleviate these problems through hyperpolarization techniques, strong magnetic fields, low temperature experiments (<25K), and pulse sequence development. Going beyond the previously stated methods, researchers found that the implementation of smaller resonant coils is a more convenient and effective way to alleviate the problem of low sensitivity in NMR spectroscopy and magnetic resonance imaging. In this work, a systematic optimization of the parameters for micro-coil probes and magic-angle coil spinning (MACS) probes was carried out with various common NMR nuclear species such as 1H, 31P, 23Na, 79Br on two NMR spectrometers (200 MHz and 500 MHz). The optimized wire diameter, coil diameter, number of turns, the inner and outer diameters of the capillary, the matching capacitors etc have been obtained and demonstrated with a real biological system. In addition, we found that the conventional placement of the sample within the glass tube wrapped by a coil yields a lower signal and sensitivity when compared to coating the sample onto the coil prior to wrapping around the capillary. A new method of performing MACS experiment with micro-coil technology, therefore, was subsequently proposed, namely, micro surface coil magic angle spinning (MISCMAS). The optimized experimental conditions were then determined with both liquid and solid state samples.