No description available.

This dissertation presents the research performed to develop an optical model, improve some design parameters, and analyze the performance of the UA modular gamma camera. Initially we provide a brief background on nuclear medical imaging with scintillation cameras. The key hardware components of a camera are introduced, and some of the fundamental physics involved in the detection of gamma rays is explained. Then we describe a stand-alone modular camera imaging system that was developed to image human breasts in the clinic. The hardware and software components, calibration procedure, and general operation of the system are detailed. We explain the concepts of position estimation and scatter rejection and note how they have been applied to imaging with the UA modular gamma camera. Position estimation uses the output signals of the camera to determine where an incident gamma ray interacted within the camera, and scatter rejection uses the signals to decide whether or not an incident gamma ray underwent scattering prior to being detected by the camera. Then we present an analytical optical model of the UA modular gamma camera. Taking into account physical and optical properties of the camera components, the model performs radiometric calculations to estimate the mean response of the camera to a scintillation event anywhere within the scintillation crystal. The results of several studies using the optical model to test and improve some camera design parameters are reported. Finally, we demonstrate how straightforward signal detection theory can be used to evaluate the performance of a modular gamma camera for the task of detecting signals in noisy backgrounds. Guided by the preliminary design of a dedicated breast imaging system, estimates of how well the UA modular gamma camera can detect lesions within human breasts were generated.

We hypothesize that acid-outside transplasmalemmalar pH gradients, typical of those found in drug-insensitive breast tumors, will result in drug resistance by the exclusion of weakly basic chemotherapeutic agents via ion trapping. We tested our hypothesis in vitro by evaluating pH dependent cytotoxicity of various clinically relevant chemotherapeutic drugs in the MCF-7 human breast adenocarcinoma cell line. The results of this work were consistent with our hypothesis, namely that (i) weak acids are more toxic in acidic extracellular media, (ii) weak bases are more toxic in basic extracellular media, and (iii) large lipophilic molecules are unaffected by pH change. Both doxorubicin and mitoxantrone were found to have enhanced intracellular accumulation in alkaline media, with the effect being more significant on mitoxantrone. In contrast, only mitoxantrone had a partition coefficient that was appropriately high and compatible with our ion-trapping model. Moreover, the pH dependent cytotoxicity and drug accumulation of mitoxantrone, doxorubicin and chlorambucil remained constant even in drug resistant cell lines such as the MCF-7D40 and MCF-7mitox cell lines. In an MCF-7 tumor/SCID mouse model the in vivo biodistribution of doxorubicin in alkalotic mice was evaluated. No significant difference in doxorubicin accumulation was observed in the tumor compared to controls. We evaluated the anti-tumor activity of mitoxantrone in the C3H model system and found that metabolic alkalosis significantly increased both the tumor growth delay and the log cell kill. In our MCF-7/SCID model system, doxorubicin combined with chronic metabolic alkalosis resulted in a reduction in the rate of tumor regrowth. In vivo mitoxantrone C3H mouse toxicity studies were performed with and without metabolic alkalosis. The LD50 and average weight loss per group remained constant with or without alkalosis. In conclusion, mitoxantrone has a pH-dependent behavior consistent with ion-trapping. (Abstract shortened by UMI.)

Radiology departments are implementing conversion from the use of hard copy film in favor of digital imaging. New digital acquisitions are increasing the efficacy of radiological imaging. The outputs of new modalities such as magnetic resonance (MR) and computed tomography (CT) are digital. They both involve gathering information that allows reconstructing cross sectional projections of internal structures and displaying them as digital images. Other technologies, e.g., computed radiography (CR), can provide digital radiographic data that replaces analog projection radiography. To date, the processed digital data is still transferred to film to provide a typical radiographic film in appearance. The film is presented to the doctor for diagnostic review. The research in this dissertation is concerned with making a film-less department. It specifically addresses problems in presenting CR images to the physician. The goal of this research is to create a computer recognition algorithm that will automatically recognize the orientation and discriminate between the lateral and posteroanterior view of digital chest radiographs image. The algorithm maintains 91.9% accuracy rate. The recognition takes .15 second per image.

Ultraviolet-B (UVB) irradiation is a major cause of human non-melanoma skin cancer. Transcription factor activator protein-1 (AP-1) plays an important role in tumor promotion. The mechanism of UVB induced tumor promotion may be through AP-1 activation. In this dissertation, the molecular mechanisms of UVB induced AP-1 activation were pursued in a human keratinocyte cell line, HaCaT. UVB induced AP-1 activation was observed by gel shift assays and transactivation assays. c-Fos and Jun D were the main components of the bound AP-1 complexes. Inhibition of RNA and protein synthesis significantly abrogated UVB induced AP-1 DNA binding. Further investigation revealed a correlation between increased AP-1 activity and c-fos gene expression after UVB. These results suggested that increased c-fos expression may play an important role in UVB induced AP-1 activation. To further examine the mechanism of UVB induced c-fos expression, the UVB signaling pathways were studied. Two members of mitogen-activated protein kinase (MAPK) family, p38 and extracellular signal-regulated protein kinase (ERK), were activated after UVB. Blocking p38 significantly abrogated UVB induced c-fos expression. Inhibiting ERK partially abrogated UVB induced c-fos expression. These data suggested that both p38 and ERK were required in UVB induced c-fos expression in human keratinocytes. Furthermore, p38 appeared to play a more important role than ERK in this process. Since c-fos expression may play an important role in UVB induced AP-1 activation, and AP-1 activation is known to play a role in tumor promotion, both p38 and ERK could be potential targets for chemoprevention of skin cancer. (-)-Epigallocatechin gallate (EGCG), the major polyphenol isolated from green tea, has been shown to inhibit UVB induced tumorigenesis. However, the mechanisms that underline the inhibitory effects of EGCG are not well understood. In this study, EGCG was found to inhibit UVB induced AP-1 transactivation and c-fos expression. Furthermore, EGCG inhibited UVB induced p38 activation. Thus, suppression of p38 signaling cascade was the mechanism of EGCG's inhibitory effect on UVB induced c-fos expression, which may further explain its inhibitory effect on UVB induced AP-1 activation.

Semiconductor-based gamma-ray-imaging detectors are under development for use in high-resolution nuclear medicine imaging applications. These detectors, based on cadmium zinc telluride, hold great promise for delivering improved spatial resolution and detection efficiency over current methods. This dissertation presents work done on three fronts, all directed toward enhancing the practicality of these imaging devices. Electronic readout systems were built to produce gamma-ray images from the raw signals generated by the imagers. Mathematical models were developed to describe the detection process in detail. Finally, a method was developed for recovering the energy spectrum of the original source by using maximum-likelihood estimation techniques. Two electronics systems were built to read out signals from the imaging detectors. The first system takes signals from a 48 x 48-pixel array at 500 k samples per second. Pulse-height histograms are formed for each pixel in the detector, all in real time. A second system was built to read out four 64 x 64 arrays at 4 million pixels per second. This system is based on digital signal processors and flexible software, making it easily adaptable to new imaging tasks. A mathematical model of the detection process was developed as a tool for evaluating possible detector designs. One part of the model describes how the mobile charge carriers, which are released when a gamma ray is absorbed in a photoelectric interaction, induce signals in a readout circuit. Induced signals follow a "near-field effect," wherein only carriers moving close to a pixel electrode produce significant signal. Detector pixels having lateral dimensions that are small compared to the detector thickness will develop a signal primarily due to a single carrier type. This effect is confirmed experimentally in time-resolved measurements and with pulse-height spectra. The second part of the model is a simulation of scattering processes that take place when a gamma ray is absorbed within the detector volume. A separate simulation predicts the spreading of charge carriers due to diffusion and electrostatic forces. The models are used in a technique to improve the energy resolution of the detectors by estimation of the source spectrum using the Expectation-Maximization algorithm.

Traditional information-processing accounts of the reasoning process in radiology assume that humans process the details of the input image in order to compute judgments. In these accounts, the development of expertise involves the acquisition of increasingly precise and complex internal problem representations that are based on a normal anatomy prototype. Fuzzy-trace theory predicts that accurate judgments rely on the reasoners ability to ignore irrelevant detail, to retrieve relevant gist memories and to accurately instantiate the image information with respect to the internal representation. Fuzzy-trace theory predicts that the development of expertise involves the ability to access and process less precise and complex internal representations (i.e., gist). The purpose of this study was to examine the internal representations used to make judgments in radiology and to quantify the changes in complexity of the internal representations, as well as the differences in time, accuracy and confidence that might be associated with experience. Thirty-five subjects from general and specialized expertise samples participated. Each subject was presented with 32 chest films including normal films, films with precise disease patterns (mass category) and diffuse disease patterns (interstitial and airspace category). For each film, the participant made a series of judgments (normal/abnormal; category; specific diagnosis) and then sketched the features that were essential to the judgments. The information content and complexity of the representations were calculated using an approach that considered the underlying meaning of the sketches rather than the surface form. The sketches were converted to propositions and the information in the propositions was evaluated in terms of possible world semantics. Time, accuracy, confidence and content measures supported the prediction of fuzzy-trace theory that the internal representations are abnormality-based. Consistent with predictions regarding the acquisition and use of gist representations expertise was associated with greater improvements in accuracy for interstitial (as opposed to mass) films; accuracy was higher with interstitial films when judgments were less reliant on surface detail (normal/abnormal); and accuracy was higher for mass films when judgments were more reliant on surface detail (specific diagnosis). Complexity measures showed that the overall representations did not get more complex with the development of expertise.

<p> The motivation for this work is a vision of widespread adoption of a priori quantitative epidemiological information for clinical decision-making, and can be seen as a quantitative large-scale extension of evidence-based medicine (EBM). Medical images can be seen as a spatially encoded map of physiological measurements that can be used to predict prognosis and to drive treatment plans. This paradigm can be very powerful and is driven by the recent big data revolution in computer science as well as the increasing availability of medical imaging modalities due to decreases in manufacturing costs. In order to achieve this overarching goal, three practical requirements must be reached and correspond to the parts of this thesis: Part A: Developing IT infrastructure and technology that enables the dataset to be properly collected and organized for analysis. Part B &amp; C: Generation of functional (Part B) and structural (Part C) medical imaging contrast that are optimized for analysis. Part D: Pattern recognition techniques (including both image processing and machine learning techniques) to mine information from the large imaging datasets generated. As part of the thesis, I discuss my contribution to IT infrastructure (Part A) by developing a Short Message Service (SMS)-based system to control the clinically used Picture Archival and Communication System (PACS) (Ch.2) as well as an imaging study tool that categorizes patient imaging data for use in retrospective studies(Ch.3). I then go on to detail my work with functional neuroimaging of obesity using functional magnetic resonance imaging (fMRI)(Ch.4) and (Ch.5). Chapters 6-9 details my efforts at studying abnormal aging versus normal aging using diffusion MRI as well as applications of diffusion MRI to surgical planning. Chapters 10 discusses my work integrating diffusion MR with FLAIR MRI to investigate the properties of white matter lesions and how it can be used in the clinical setting. Chapter 11 then moves on to talk about my work modifying standard brain parcellation techniques to allow them to work with aged brains with large infarcts. Chapters 6-11 altogether represent my efforts in structural neuroimaging using MRI (Part C). The thesis then closes with capstone work in development staging using hand x-rays using fuzzy logic (Ch. 12 &amp; 13). To close the work with Alzheimer's Disease (AD) and aging, we used machine learning techniques to predict disease progression based on a baseline MRI scan as well as higher order analysis of our diffusion MRI dataset by integrating MRI information with other clinical information such as neuropsychological tests, cardiovascular status. This is all in an effort to computationally explore the relationship between MRI measurements and clinical presentation of disease as measured by neuropsychological scores. Similarly with the Obesity work, we related fMRI activation differences between high and low calorie foods with non-imaging information such as insulin resistance (Ch. 16).</p>

The purpose of this study was to determine the cumulative doses of x-ray radiation to the thyroid gland and female breast from spinal radiographs for Adolescent idiopathic scoliosis (AIS) and to estimate the number of cancers at these sites attributable to x-rays. / Subjects for this study were patients referred from 1960 to 1979 for AIS to Hopital Ste-Justine, Montreal. / About 85 percent of 2,181 subjects were first referred for scoliosis between the ages of eleven and seventeen and the average time under observation was about three years. The mean number of radiographs was about twelve. The mean cumulative dose to the thyroid gland and to the female breast was about three cGy. Seven excess breast cancer and thyroid cancer cases were projected to occur over the lifetime of the women; among these, two excess deaths from cancers were projected. Approximately one in every 250 women in this cohort would be expected to develop breast or thyroid cancer over their lifetime, and one in every 900 women would be expected to develop a fatal cancer. (Abstract shortened by UMI.)

<p> Diffuse optical tomography (DOT) is an optical imaging modality that uses near infrared light to recover functional information of tissue. In this thesis we focus on breast imaging where estimation of the optical properties of the breast can assist in detecting cancerous tumors and in judging overall breast health. </p><p> To this end we explore the application of a parametric level set method (PaLS) for image reconstruction for hyperspectral DOT. Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used as the foundation for reconstruction. The PaLS method significantly reduces the number of unknowns relative to more traditional level-set reconstruction methods and has been shown to be particularly well suited for ill-posed inverse problems such as the one of interest here. We extend the PaLS method to imaging problems by considering a redundant dictionary matrix for basis functions allowing for recovery of a wide array of shapes. </p><p> Additionally we explore the ability of diffuse optical tomography (DOT) to recover 3D tubular shapes representing vascular structures in breast tissue. Using the PaLS method, we incorporate the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimation compared to an unregularized approach and a pixel based reconstruction.</p>