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Magnetic resonance based diffusion imaging has been gaining more utility and clinical relevance over the past decade. Using conventional echo planar techniques it is possible to acquire and characterize water diffusion within the central nervous system (CNS); namely in the form of Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI). While each modality provides valuable clinical information in terms of the presence of diffusion, DWI, and its directionality, DTI, the techniques used for analysis are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption reduces the amount of relevant information that can be interpreted in a clinical setting. By measuring the excess kurtosis, or peakedness, of the Gaussian distribution it is possible to get a better understanding of the underlying cellular structure. The objective of this work is to provide mathematical and experimental evidence that Diffusion Kurtosis Imaging (DKI) can provide additional information about the micromolecular environment of the pediatric spinal cord by more completely characterizing the probabilistic nature of random water displacement. A novel DKI imaging sequence based on a 2D spatially selective radio frequency pulse providing reduced FOV imaging with view angle tilting (VAT) was implemented, optimized on a 3Tesla MRI scanner, and tested on pediatric subjects (normal:15; patients with spinal cord injury:5). Software was developed and validated in-house for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in kurtosis parameters (mean kurtosis, axial kurtosis) between normal and patients. DKI provides incremental and new information over conventional diffusion acquisitions that can be integrated into clinical protocols when coupled with higher order estimation algorithms. / Electrical and Computer Engineering

The wide band gap semiconductor Cadmium Zinc Telluride (CZT) is of recent interest for medical imaging at room temperature. A number of properties, including superior energy resolution, 3D photon position sensitivity, compact size, direct photon conversion and energy-resolving capability, make CZT a promising candidate for positron emission tomography (PET) and photon-counting X-ray imaging systems. Despite these advantages, drawbacks, such as low mobility of holes, hole trapping, charge sharing effect and characteristic X-ray escape degrade the performance of large volume CZT detectors. In this research, characterization and evaluation of single-crystal CZT photon detector using simulation and experimental studies were done. First, a comprehensive analytical model was developed and implemented by using Monte Carlo simulation and finite element analyses. This model includes the generation and transportation of charge carries within CZT detectors, and it provides useful guidance in optimizing the electrode design and associated readout circuits. Second, the performance of a 20×20×5mm3 CZT crystal with 8×8 pixel anodes and a planar cathode was integrated with readout electronics that can be used to build a PET system was studied. The experiments demonstrate an energy resolution of ~2.26±0.84% full width half maximum (FWHM) at 662 keV and 19±3 ns coincidence time resolution with planar parallel field configuration. A novel algorithm based on charge sharing effect and transient signal analysis targeting the improvement of spatial resolution, was proposed. The sub-pitch spatial resolution is found to be ~30 µm and ~250 µm under signal-to-noise ratio of ~17, for inside and outside the valid range of charge sharing, respectively. Finally, the feasibility of CZT in photon-counting Computed Tomography (CT) was studied by using monoenergetic sources, with a special attention paid to energy degradation due to characteristic X-ray escape and the charge sharing effect. The effects of detector configuration and incident beam location were also investigated. The results show that the pixel size can be reduced to 500 µm without significant count loss (~5%) and charge loss (~15%) for the photo-counting X-ray applications. / Thesis / Doctor of Philosophy (PhD)

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<p> Autosomal Dominant Polycystic Kidney Disease (ADPKD) is among the most prevalent life-threatening genetic conditions. Despite this, no approved medical therapies exist to treat the disease. Until the recent past, no methods could reliably measure the course of the disease far in advance of end stage renal disease (ESRD). As normal tissue is progressively destroyed or blocked by enlarging cysts, remaining nephrons compensate in a process called hyperfiltration. This beneficial physiological response confounds tests of renal function. Thus, potential interventions could not be tested against a reliable measurement of disease progression. </p><p> However, progressive changes are visually apparent on medical imaging examinations throughout the course of ADPKD. The search for ADPKD proxy biomarkers is now focused on quantitative imaging, or the extraction of information from medical images for purposes of diagnosis or disease tracking. Recent studies from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)- sponsored Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) showed Total Kidney Volume (TKV) is a usable quantitative imaging biomarker which can track disease in the early, asymptomatic phase and register measurable changes in as little as 12 months. These findings launched several new trials into potential ADPKD therapies. </p><p> Advanced analysis of polycystic kidney images, however, has never been done. The method CRISP used to extract TKV was stereology, an efficient means to estimate volume. However, stereology was tradi- tionally a dead end for further advanced analysis. TKV is useful for clinical trials and large population-based studies, but cannot accurately predict disease progression or stratify risk due to known out- lier cases. Thus, the utility of TKV for individual patient prognosis is limited. This work builds upon stereology data, describing a reliable and accurate new semi-automatic method to fully segment images us- ing only labeled stereology grids. Then, two new second generation quantitative imaging biomarkers are introduced and analyzed: Cyst- Parenchyma Surface Area (CPSA) and cyst concentration. These new physiologically motivated biomarkers will complement or potentially replace TKV in efforts to bring quantitative imaging to individual patients. </p><p> The goal of this body of work is to enable a pathway for efficient advanced image analysis in ADPKD, never before attempted in this dis- order, and to define new quantitative imaging biomarkers which will complement or replace existing ones in hopes of making individualized disease tracking for ADPKD patients a reality.</p>

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