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Balanced steady-state free precession (bSSFP) is a fast, SNR-efficient magnetic resonance (MR) imaging sequence suffering from dark banding artifacts due to its off-resonance dependence. These banding artifacts are difficult to mitigate at high field strengths and in the presence of metallic implants. Recent developments in parametric modelling of bSSFP have led to advances in banding removal and parameter estimation using multiple phase-cycled bSSFP. With increasing number of coils in receivers, more storage and processing is required. Coil combination is used to reduce dimensionality of these datasets which otherwise might be prohibitively large or computationally intractable for clinical applications. However, our recent work demonstrates that some combination methods are problematic in conjunction with elliptical phase-cycled bSSFP.This thesis will present a method for phase estimation of coil-combined multiple phase-cycled bSSFP to reduce storage and computational requirements for elliptical models. This method is general and works across many coil combination techniques popular in MR reconstruction including the geometric coil combine and adaptive coil combine algorithms. A viable phase estimate for the sum-of-squares is also demonstrated for computationally efficient dimension reduction. Simulations, phantom experiments, and in vivo MR imaging is performed to validate the proposed phase estimates.Compressed sensing (CS) is an increasingly important acquisition and reconstruction framework. CS MR allows for reconstruction of datasets sampled well-under the Nyquist rate and its application is natural in MR where images are often sparse under common linear transforms. An extension of this framework is the ordering problem for CS, first introduced in 2008. Although the assumption is made in CS that images are sparse in some specified transform domain, it might not be maximally sparse. For example, a signal ordered such that it is monotonic is maximally sparse in the finite differences domain. Knowledge of the correct ordering of an image's pixels can lead to much more sparse and powerful regularizers for the CS inverse problem. However, this problem has met with little interest due to the strong dependence on initial image estimates.This thesis will also present an algorithm for estimating the optimal order of a signal such that it is maximally sparse under an arbitrary linear transformation without relying on any prior image estimate. The algorithm is combinatoric in nature and feasible for small signals of interest such as T1 mapping time curves. Proof of concept simulations are performed that validate performance of the algorithm. Computationally feasible modifications for in vivo cardiac T1 mapping are also demonstrated.

This thesis examines the use of diagonal tension (shear) testing to determine factors affecting shear strength of Interlocking Compressed Earth Block (ICEB) panels. This work expands on the current information available about strength properties of ICEB assemblies, which are dry-stacked, as opposed to having mortared beds. Variables such as block strength, grout strength and grouting pattern can influence the results of these types of tests and are examined in this investigation. To study variables affecting diagonal shear strength, 9 panels were tested, consisting of blocks produced by a manual block press. Strength testing was adopted from common ASTM standards to determine constituent material properties. A modified version of ASTM E519 test procedure is used to perform diagonal tension testing. Imaging analysis, using a high resolution camera, was run simultaneously during testing to capture displacement histories of select panels. It was determined that both block and grout strength significantly affect the shear strength of ICEB panels. Additionally, vertical grouting and block type also have a strong influence. Imaging analysis results confirm that the dominant failure mode in ICEB panels is bed joint sliding both pre and post peak load, with noticeable displacements at head joint locations on a few panels. Lastly, diagonal cracking along the block face was noticeable on several panels following peak load. Further testing remains to determine other factors affecting shear strength, namely, the application of normal pre-compression loads to the panel.

The problem with which this investigation is concerned is that of determining if recent studies showing no significant comprehension loss at compressed rates were valid for a learning situation.

Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed information than ever. Aside from the evolution of hardware and software, multimodal imaging techniques offer a promising solution to produce higher quality images by fusing medical images from different modalities. This strategy utilizes more structural and/or functional image information, thereby allowing clinical results to be more comprehensive and better interpreted. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. The method proposed in this study is part of the planned joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI platform. However, since their image data are highly undersampled, analytical methods, such as filtered backprojection, are unable to generate images of sufficient quality. To overcome this drawback, we resort to compressed sensing techniques, which employ sparse priors that result from an application of L₁-norm minimization. To utilize multimodal information, a projection distance is introduced and is tuned to tailor the texture and pattern of final images. Specifically CT and MRI images are alternately reconstructed using the updated multimodal results that are calculated at the latest step of the iterative optimization algorithm. This method exploits the structural similarities shared by the CT and MRI images to achieve better reconstruction quality. The improved performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI body images and a pair of undersampled CT-MRI head images. These images are tested using joint reconstruction, analytical reconstruction, and independent reconstruction without using multimodal imaging information. Results show that the proposed method improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity measurements compared to independent reconstruction methods. It offers a similar quality as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and a 30% sampling rate for MRI. It is concluded that structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of image reconstruction. / Ph. D. / Medical imaging techniques play a central role in modern clinical diagnoses and treatments. Consequently, there is a constant demand to increase the overall quality of medical images. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. Multimodal imaging techniques can provide more detailed diagnostic information by fusing medical images from different imaging modalities, thereby allowing clinical results to be more comprehensive to improve clinical interpretation. A new form of multimodal imaging technique, which combines the imaging procedures of computed tomography (CT) and magnetic resonance imaging (MRI), is known as the “omnitomography.” Both computed tomography and magnetic resonance imaging are the most commonly used medical imaging techniques today and their intrinsic properties are complementary. For example, computed tomography performs well for bones whereas the magnetic resonance imaging excels at contrasting soft tissues. Therefore, a multimodal imaging system built upon the fusion of these two modalities can potentially bring much more information to improve clinical diagnoses. However, the planned omni-tomography systems face enormous challenges, such as the limited ability to perform image reconstruction due to mechanical and hardware restrictions that result in significant undersampling of the raw data. Image reconstruction is a procedure required by both computed tomography and magnetic resonance imaging to convert raw data into final images. A general condition required to produce a decent quality of an image is that the number of samples of raw data must be sufficient and abundant. Therefore, undersampling on the omni-tomography system can cause significant degradation of the image quality or artifacts after image reconstruction. To overcome this drawback, we resort to compressed sensing techniques, which exploit the sparsity of the medical images, to perform iterative based image reconstruction for both computed tomography and magnetic resonance imaging. The sparsity of the images is found by applying sparse transform such as discrete gradient transform or wavelet transform in the image domain. With the sparsity and undersampled raw data, an iterative algorithm can largely compensate for the data inadequacy problem and it can reconstruct the final images from the undersampled raw data with minimal loss of quality. In addition, a novel “projection distance” is created to perform a joint reconstruction which further promotes the quality of the reconstructed images. Specifically, the projection distance exploits the structural similarities shared between the image of computed tomography and magnetic resonance imaging such that the insufficiency of raw data caused by undersampling is further accounted for. The improved performance of the proposed approach is demonstrated using a pair of undersampled body images and a pair of undersampled head images, each of which consists of an image of computed tomography and its magnetic resonance imaging counterpart. These images are tested using the proposed joint reconstruction method in this work, the conventional reconstructions such as filtered backprojection and Fourier transform, and reconstruction strategy without using multimodal imaging information (independent reconstruction). The results from this work show that the proposed method addressed these challenges by significantly improving the image quality from highly undersampled raw data. In particular, it improves about 5dB in signal-to-noise ratio and nearly 10% in structural similarity measurements compared to other methods. It achieves similar image quality by using less than 5% of the X-ray dose for computed tomography and 30% sampling rate for magnetic resonance imaging. It is concluded that, by using compressed sensing techniques and exploiting structural similarities, the planned joint computed tomography and magnetic resonance imaging system can perform imaging outstanding tasks with highly undersampled raw data.

The increasing demand on wood and wood products, and the simultaneously decreasing quality of wood as a raw material leads to the increasing significance of wood-based composites such as particleboard or flakeboard. The resulting mechanical and physical properties are to the large extend dictated by the densification of the wood component. To be able to predict the density of the material, the behavior of structural elements must be known. A theory developed for rigid plastic foams was modified and applied to the deformation of wood in transverse compression. A testing procedure for high strain compression over a range of temperatures was developed. In addition, a stochastic model for prediction of high strain behavior was developed. Wood of yellow poplar (<i>Liriodendron tulipuera</i>) was used as the experimental material. / Master of Science

Time-compressed speech discrimination of children grouped as high and low risk on a reading-readiness test was examined. Children were grouped according to performance on a measure of reading-readiness skills. All passed a hearing screening at fifteen decibels for octave frequencies 250-4000 Hz. The Word Intelligibility by Picture Identification (WIPI) comprised the time-compressed speech task, in a sound field at seventy decibels Sound Pressure Level and zero degrees azimuth. The protocol for administration of the time-compressed speech task was sixty per cent time compression, then zero per cent time compression. Significant effects appeared for time compression ratio and test group. Average difference was twelve per cent and approximately eight per cent at zero.

Contemporary definitions of human listening suggest that it is a multi-dimensional phenomenon. Short-term and interpretative listening may be viewed as important aspects of the listening process. However, research in time-compressed speech has focused on listening comprehension while not adequately treating other important types of listening. A broader view of the listening process would include all of the skills considered relevant to everyday human communication. This study examined the effect of time-compressed speech on comprehensive, interpretative and short-term listening. The Kentucky Comprehensive Listening Test was used to measure the three types of listening. Cut and splice tape editing was employed in the development of four master test tapes: a control tape presented at normal rate and tapes with test stimuli time-compressed by 30%, 45%, and 60%. Each of four randomly selected groups, 120 total subjects, was exposed to one of the four test tapes. The data from the test administrations was analyzed by analysis-of-variance and simple means tests. Results indicate that a statistically significant amount of the variance in comprehensive, interpretative and short-term listening scores may be explained by the manipulated variable, time-compression. However, the amount of variance-accounted-for is relatively low for both short-term and interpretative listening. Closer examination of the data indicates that short-term and interpretative listening test scores do not significantly decay until a high level of time-compression (60%) is reached. Conversely, in the case of comprehensive listening, a relatively linear relation exists between degree of time-compression and test scores. Significant drops in mean scores were found at more moderate levels of time-compression. The findings are discussed in light of differences between short-term and long-term memory. Comprehensive listening, which relies upon long-term memory, may suffer from a lack of adequate processing and encoding time which may be induced by time-compression. Short-term and Interpretative listening are processes which rely primarily on short-term memory and may not be adversely affected until a level of time-compression is reached which impairs intelligibility. Implications are noted for future research and for educational applications.

The compressed sensing (CS) model of signal processing, while offering many unique advantages in terms of low-cost sensor design, poses interesting challenges for both signal acquisition and recovery, especially for signals of large size. In this work, we investigate how CS might be applied practically and efficiently in the context of natural video. We make use of a CS video acquisition approach in line with the popular single-pixel camera framework of blind, nonaptive, random sampling while proposing new approaches for the subsequent recovery of the video signal which leverage interrame redundancy to minimize recovery error. We introduce a method of approximation, which we term multihypothesis (MH) frame prediction, to create accurate frame predictions by comparing hypotheses drawn from the spatial domain of chosen reference frames to the non-overlapping, block-by-block CS measurements of subsequent frames. We accomplish this frame prediction via a novel distance-weighted Tikhonov regularization technique. We verify through our experiments that MH frame prediction via distance-weighted regularization provides state-of-the-art performance for the recovery of natural video sequences from blind CS measurements. The distance-weighted regularization we propose need not be limited to just frame prediction for CS video recovery, but may also be used in a variety of contexts where approximations must be generated from a set of hypotheses or training data. To show this, we apply our technique to supervised hyperspectral image (HSI) classification via a novel classifier we term the nearest regularized subspace (NRS) classifier. We show that the distance-weighted regularization used in the NRS method provides greater classification accuracy than state-of-the-art classifiers for supervised HSI classification tasks. We also propose two modifications to the core NRS classifier to improve its robustness to variation of input parameters and and to further increase its classification accuracy.

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