Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Subject: search.filters.subject.Health Sciences, Radiology

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Now showing 1 - 5 of 5
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    IN-SITU MEASUREMENT OF EPITHELIAL TISSUE OPTICAL PROPERTIES: DEVELOPMENT AND IMPLEMENTATION OF DIFFUSE REFLECTANCE SPECTROSCOPY TECHNIQUES
    (2009) Wang, Quanzeng; Wang, Nam Sun; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Cancer is a severe threat to human health. Early detection is considered the best way to increase the chance for survival. While the traditional cancer detection method, biopsy, is invasive, noninvasive optical diagnostic techniques are revolutionizing the way that cancer is diagnosed. Reflectance spectroscopy is one of these optical spectroscopy techniques showing promise as a diagnostic tool for pre-cancer detection. When a neoplasia occurs in tissue, morphologic and biochemical changes happen in the tissue, which in turn results in the change of optical properties and reflectance spectroscopy. Therefore, a pre-cancer can be detected by extracting optical properties from reflectance spectroscopy. This dissertation described the construction of a fiberoptic based reflectance system and the development of a series of modeling studies. This research is aimed at establishing an improved understanding of the optical properties of mucosal tissues by analyzing reflectance signals at different wavelengths. The ultimate goal is to reveal the potential of reflectance-based optical diagnosis of pre-cancer. The research is detailed in Chapter 3 through Chapter 5. Although related with each other, each chapter was designed to become a journal paper ultimately. In Chapter 3, a multi-wavelength, fiberoptic system was constructed, evaluated and implemented to determine internal tissue optical properties at ultraviolet A and visible wavelengths. A condensed Monte Carlo model was deployed to simulate light-tissue interaction and generate spatially distributed reflectance data. These data were used to train an inverse neural network model to extract tissue optical properties from reflectance. Optical properties of porcine mucosal and liver tissues were finally measured. In Chapter 4, the condensed Monte Carlo method was extended so that it can rapidly simulate reflectance from a single illumination-detection fiber thus enabling the calculation of large data sets. The model was implemented to study spectral reflectance changes due to breast cancer. The effect of adding an illumination-detection fiber to a linear array fiber for optical property determination was also evaluated. In Chapter 5, an investigation of extracting the optical properties from two-layer tissues was performed. The relationship between spatially-resolved reflectance distributions and optical properties in two-layer tissue was investigated. Based on all the aforementioned studies, spatially resolved reflectance system coupled with condensed Monte Carlo and neural network models was found to be objective and appear to be sensitive and accurate in quantitatively assessing optical property change of mucosal tissues.
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    High-Speed Reconstruction of Low-Dose CT Using Iterative Techniques for Image-Guided Interventions
    (2008-07-18) Bhat, Venkatesh Bantwal; Shekhar, Raj; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Minimally invasive image-guided interventions(IGIs) lead to improved treatment outcomes while significantly reducing patient trauma. Because of features such as fast scanning, high resolution, three-dimensional view and ease of operation, Computed-Tomography(CT) is increasingly the choice for IGIs. The risk of radiation exposure, however, limits its current and future use. We perform ultra low-dose scanning to overcome this limitation. To address the image quality problem at low doses, we reconstruct images using the iterative Paraboloidal Surrogate(PS) algorithm. Using actual scanner data, we demonstrate improvement in the quality of reconstructed images using the iterative algorithm at low doses as compared to the standard Filtered Back Projection(FBP) technique. We also accelerate the PS algorithm on a cluster of 32 processors and a GPU. We demonstrate approximately 20 times speedup for the cluster and two orders of improvement in speed for the GPU, while maintaining comparable image quality to the traditional uni-processor implementation.
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    Similarity Classification and Retrieval in Cancer Images and Informatics
    (2008-04-26) Tahmoush, David Alan; Samet, Hanan; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Techniques in image similarity, classification, and retrieval of breast cancer images and informatics are presented in this thesis. Breast cancer images in the mammogram modality have a lot of non-cancerous structures that are similar to cancer, which makes them especially difficult to work with. Only the cancerous part of the image is relevant, so the techniques must learn to recognize cancer in noisy mammograms and extract features from that cancer to classify or retrieve similar images. There are also many types or classes of cancer with different characteristics over which the system must work. Mammograms come in sets of four, two images of each breast, which enables comparison of the left and right breast images to help determine relevant features and remove irrelevant features. Image feature comparisons are used to create a similarity function that works well in the high-dimensional space of image features. The similarity function is learned on an underlying clustering and then integrated to produce an agglomeration that is relevant to the images. This technique diagnoses breast cancer more accurately than commercial systems and other published results. In order to collect new data and capture the medical diagnosis used to create and improve these methods, as well as develop relevant feedback, an innovative image retrieval, diagnosis capture, and multiple image viewing tool is presented to fulfill the needs of radiologists. Additionally, retrieval and classification of prostate cancer data is improved using new high-dimensional techniques like dimensionally-limited distance functions and dimensional choice.
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    HARDWARE-ACCELERATED AUTOMATIC 3D NONRIGID IMAGE REGISTRATION
    (2007-05-02) Hemaraj, Yashwanth; Shekhar, Raj; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Software implementations of 3D nonrigid image registration, an essential tool in medical applications like radiotherapies and image-guided surgeries, run excessively slow on traditional computers. These algorithms can be accelerated using hardware methods by exploiting parallelism at different levels in the algorithm. We present here, an implementation of a free-form deformation-based algorithm on a field programmable gate array (FPGA) with a customized, parallel and pipelined architecture. We overcome the performance bottlenecks and gain speedups of up to 40x over traditional computers while achieving accuracies comparable to software implementations. In this work, we also present a method to optimize the deformation field using a gradient descent-based optimization scheme and solve the problem of mesh folding, commonly encountered during registration using free-form deformations, using a set of linear constraints. Finally, we present the use of novel dataflow modeling tools to automatically map registration algorithms to hardware like FPGAs while allowing for dynamic reconfiguration.
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    EFFECTS OF ACUTE POSTURAL CHANGE ON MID-THIGH CROSS-SECTIONAL AREA AS MEASURED BY COMPUTED TOMOGRAPHY
    (2005-12-07) cerniglia, linda michelle; Rogers, Marc; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Fluid shifts resulting from postural change present a potential source of error when assessing the CSA of limb tissue using CT. In the present study mid-thigh axial scans of 13 older women were obtained at 5, 10, and 15 minutes of supine rest. Scans were analyzed for changes in CSA of subcutaneous fat(SF), low density muscle(LDM) and normal density muscle(NDM) tissue. A significant decrease was found in NDM CSA at 15 minutes (2.3 ± 0.8, 1.6%, P<0.05) with no change in LDM or SF CSA between any time interval The current study suggests that the potential measurement error associated with fluid shifting out of the tissues can be minimized when baseline and follow-up CT-derived images of mid-thigh CSA are obtained within the first 10 minutes the subject assumes the supine position and that the CSA of NDM and LDM may be affected differently by loss of hydrostatic pressure.