Woodchucks are the preferred animal model to study chronic viral hepatitis and the development of hepatocellular carcinoma (HCC), which occurs as a result of infection with woodchuck hepatitis virus. Significant elevations in the phosphomonoester peak in 31P-MRS spectrum correlated to the presence of HCC. Ex vivo 31P-NMR determined that HCC tissue had significantly elevated concentrations of PC compared to uninfected control tissues, confirming that PME is specific to the tumour’s growth. Finally, a recombinant vaccinia virus was constructed to stimulate the immune systems of infected woodchucks against cells expressing core antigens. Despite reductions in surface antigen expression and viral load, elevations in serum GGT and the PME in 31P-MRS indicated that there was tumour growth in treated woodchucks. In conclusion, the PME peak represents a potential biomarker of cancerous growth when used in conjunction with serological tests to detect HCC in the liver due to chronic hepatitis virus infection. / May 2009

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) has been developed and applied to clinical analysis studies due to its non-invasive properties. Because of the increasing clinical interests of applying MRS, a lot of post-processing tools has been developed, among which LCModel is one of the most popular. LCModel estimates the absolute metabolite concentrations in vivo according to the basis file, so basis files play an important role for the accuracy of absolute metabolite concentrations. The default basis sets of LCModel were made by phantom experiments. However, some special metabolites are difficult to get them, so the basis sets lack for these metabolites. In order to avoid this trouble, LCModel provides a special method called ¡§spectra offering¡¨. In this study, we use GAMMA Visual Analysis (GAVA) software to create basis sets and compare the shape of LCModel default basis sets with the shape of GAVA basis sets. Some metabolites which are not included in the LCModel phantom experiments are also generated. Finally, we estimate the absolute concentrations in normal subjects and patients by using these two kinds of basis sets respectively. Using LCModel ¡§spectra offering¡¨ method to append extra metabolites for LCModel basis sets is applicable to those metabolites of singlet resonance but not of J-coupling resonance in the meanwhile. Our results demonstrate that using GAVA simulation as the basis set leads to different quantitative results from using basis sets of in vitro. We believe that using GAVA simulation as the basis set would provide better consistency among all metabolites and thus achieve more accurate quantification of MRS.

Proton magnetic resonance spectroscopy has been widely applied to the diagnosis of brain diseases. In the meanwhile, the classification of brain abscesses plays an important role on the accurate prognosis in clinics. Recently, the interest in using proton MRS to classify pyogenic brain abscesses has been arising because of its non-invasive property and good accuracy in detecting metabolites. The brain abscess can be classified by means of the metabolites observed in the MR spectra, which may thus benefit the accuracy of the brain abscess diagnosis clinically. However, the interpretation of MR spectra by experienced radiologists can be also very subjective and therefore results in the variation of diagnosis. In this study, we investigate the potential possibility of using Principal Component Analysis (PCA) to classify the short TE MR spectra in more objective way.

Conventional magnetic resonance imaging (MRI) is a noninvasive and nondestructive technique and ideally suited for applications in clinical studies. In addition to the information of human anatomy provided by MRI, magnetic resonance spectroscopy (MRS) also provided a noninvasive method to investigate the metabolites in the body and is therefore regarded as a valuable method to examine tumors and disorders especially for the brain applications. To diagnose pyogenic brain abscess from other diseases is very important for clinic treatment. Cytosolic amino acids, lactate, alanine and acetate have been recognized as potential abscess markers, especially amino acids. LCModel is a well-known and reliable post-processing tool for MRS which can provide objectively quantitative of metabolite concentration. In this thesis, we would use LCModel to analyze the spectra of amino acids and further to identify and quantitate these metabolites. And we hope that the method would benefit more precisely noninvasive diagnosis and treatment of pyogenic brain abscess. However, due to the possibly poor SNR of in vivo proton MR spectroscopy, it might be difficult to identify these metabolites. In this study, we would validate the accuracy of LCModel in the analysis of amino acids. We used GAVA-simulated resonance spectra with different level noise as our input signals and analyzed by LCModel to understand the influence of concentrations and SNR caused by different level noise. Our goal is to find an optimally reliable method to help the clinic diagnosis of abscess patients.

Magnetic Resonance Imaging (MRI) can obtain the tissues of in vivo non-invasively. Proton MR Spectroscopy uses the resonance principle to collect the signals of proton and transforms them to spectrums. It provides information of metabolites in patient¡¦s brain for doctors to observe the change of pathology. Observing the metabolites of brain abscess patients is most important process in clinical diagnosis and treatment. Then, doctors use different spectrums of echo time (TE) to enhance the accuracy in the diagnosis. In our study, we use independent component analysis (ICA) to analyze MR spectroscopy. After analyzing, the independent components represent the elements which compose the input data. Then, we use the projection which is mentioned by Ssu-Ying Lu¡¦s Thesis to help us observe the relationship between independent components and spectrums of patients. We also discuss the result of spectrums with using ICA and PCA and discover some questions (whether it need to do scale normalization before inputting data or not, the result of scale normalization doesn¡¦t expect, and the peak in some independent components confuse us by locating in indistinct place) to discuss and to find possible reason after experiments.

LCModel has been widely used for MR spectroscopy analysis. LCMgui, which is the built-in user interface of LCModel, based on Linux system, provides the functionality to convert MRS data of various formats to match the format of LCModel raw file, except for GE MRSI data which can be analyzed by LCModel only with GE Sage/IDL software. Hence, the first part of this work was to develop a multi-platform tool with LCModel to support all GE data, including GE MRSI data and phased array data. With this tool, users can analyze MRS data with LCModel on their familiar environment such as Windows, and Linux. The MR spectroscopy experiments with phased array coils provide optimized SNR which lead to more accurate absolute quantification by some sophisticate combination algorithms of phased array coils. Thus, the second part of this work was to propose an algorithm of combining data obtained from phased array coils by doing phase correction and calculation of weighting factor. In addition, the comparison of the accuracy between using quadrature coil and phased array coils with different combination algorithms was investigated in order to demonstrate the efficiency of using phased array coils and the combination program.

Magnetic resonance (MR) has been developed and applied to clinical analysis studies due to its non-invasive properties. Because of the increasing interest of applying magnetic resonance spectroscopy imaging (MRSI) to clinical application, some post-processing softwares, like LCModel, provide a graphical user interface for convenient and efficient analysis. However, the features of combining MR imaging (MRI) with MRS information and browsing all analyzed results are not provided by LCModel. Our study proposed a method to implement the architecture for processing General Electric (GE), Siemens MRSI data sets and provides features including interactive display, selection and analysis of full 2D slices. For multi-console analysis, our tool also provides the combination of MRS, MRI, and data sets generated by LCModel, such as the projection of three planes and metabolite/spectra map, and therefore the three formats of data sets could be obtained from scanners of various manufactures. Especially, it is more complicated when processing GE data sets, so some mechanisms for processing are proposed, like the transformation, the three plane loc images detection and MRSI detection, etc. Additionally, our tool also has the advantage of the compatibility of further extended functionalities, which would be more flexible and useful for clinical applications.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 128-134). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 128-134). Also available on the Internet.

Thesis (Ph. D.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xv, 190 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 153-180).