To differentiate pyogenic brain abscess from other brain diseases such as necrotic glioblastomas is very important for clinic treatment. Cytosolic animo acids, lactate, alanine, succinate and acetate have been recognized as potential abscess markers. LCModel is a well-known tool to analyze the MRS data, as it provides opportunity of quantitative of metabolite concentration. Using MRS with LCModel to identify and quantitate these metabolites would benefit more precisely noninvasive diagnosis and treatment of pyogenic brain abscess. However, to differentiate the MR spectra of strongly overlapping metabolites are not easy. In this study, we validate the accuracy of LCModel on detecting these overlapping metabolites. We use some GAVA-simulated resonance spectra as our input signals and figure out the performance of LCModel analysis in different conditions. Our goal is to find an optimal analysis method to help the clinic diagnosis of abscess patients. Our result shows that the determination of basis sets is very important since the analyzed result might be different due to the improper selection of basis sets.

Phased array coils are composed of several surface coils receiving individual element signals simultaneously. Each individual surface coil provides the equivalent of the coil diameter range, and higher SNR. Therefore, combining these non-interactive phased array coils, can achieve a wide range of scan areas, uniform sensitivity and better SNR. Therefore our experiment was performed with two different coils of quadrature coil and phased array coil. Phased array MRS data were compared using various combination approaches. Data acquired by quadrature coil was regarded as a standard to verify the reliability and accuracy of metabolite concentration. The aim of our study is to do eddy current correction before and after the combination of each element coil data with LCModel analysis for quantitative comparison of metabolite concentrations. Our result shows that doing eddy current correction for each phased array coil before signal combination can achieve higher reliability and accuracy of SNR and quantitative concentrations of MR spectra in vivo.

Conventional magnetic resonance (MR) imaging has been widely applied to clinical analysis studies due to its non-invasive property. Proton MR spectroscopy complements conventional MR imaging by enabling better lesion characterization. Thus, proton MR spectroscopy is used to assist in the differential diagnosis of intracranial pathologies. LCModel is a reliable and user-friendly post-processing tool which is used to analyse absolute concentrations in our thesis. Our phantom are solution of alanine (Ala), cytosolic amino acids (AAs), lactate (Lac), and n-acetyl aspartate (NAA) in a spherical flasks of glass. We used three basis sets with difference echo time (TE) to experiment. We also performed a retrospective study of subjects with brain abscesses referred during a span of 10 years. All subjects underwent conventional MR imaging and in vivo proton MR spectroscopy, and subjects are classified four groups according to the spectrum characteristics described in the literatures. In this thesis, phantom experiments as well as GAVA simulation are included for the basis sets comparison. Then, abscesses subjects are analyzed by LCModel using these basis sets and compared with clinical diagnosis. Our result shows that using GAVA simulation as the basis sets may provide better consistency among all metabolites and thus achieve more reliable quantification of magnetic resonance spectroscopy.

LCModel, which is performed on Linux, has been widely used for quantitative analysis of MRS. Its interface, LCMgui, converts MRS data of various formats to RAW file for LCModel analysis automatically. In this work, we had a web-based MRS analysis tool for GE MRS, GE MRS with Phase-Array and GE 2D-MRSI and improve the capability of web-based MRS analysis tool for GE 3D-MRSI, Siemens MRS/MRSI, and Philips MRS/MRSI. Meanwhile, T2 correction has been involved in the absolute quantification with LCModel. With the same echo-time, the different T2 value of each metabolite results in different degree of signal decay. In order to correct and make absolute concentrations more accurate, we exploit a factor to correct effect of different T2. Two groups of MRS data (TE = 35 and 272 ms) have been studied for comparison.

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.

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.

Prostate cancer is the most common malignancy afflicting Canadian men in 2011. Physicians use digital rectal exams (DRE), blood tests for prostate specific antigen (PSA) and transrectal ultrasound (TRUS)-guided biopsies for the initial diagnosis of prostate cancer. None of these tests detail the spatial extent of prostate cancer - information critical for using new therapies that can target cancerous prostate. With an MRI technique called proton magnetic resonance spectroscopic imaging (1H-MRSI), biochemical analysis of the entire prostate can be done without the need for biopsy, providing detailed information beyond the non-specific changes in hardness felt by an experienced urologist in a DRE, the presence of PSA in blood, or the “blind-guidance” of TRUS-guided biopsy. A hindrance to acquiring high quality 1H-MRSI data comes from signal originating from fatty tissue surrounding prostate that tends to mask or distort signal from within the prostate, thus reducing the overall clinical usefulness of 1H-MRSI data. This thesis has three major areas of focus: 1) The development of an optimized 1H-MRSI technique, called conformal voxel magnetic resonance spectroscopy (CV-MRS), to deal the with removal of unwanted lipid contaminating artifacts at short and long echo times. 2) An in vivo human study to test the CV-MRS technique, including healthy volunteers and cancer patients scheduled for radical prostatectomy or radiation therapy. 3) A study to determine the efficacy of using the 1H-MRSI data for optimized radiation treatment planning using modern delivery techniques like intensity modulated radiation treatment. Data collected from the study using the optimized CV-MRS method show significantly reduced lipid contamination resulting in high quality spectra throughout the prostate. Combining the CV-MRS technique with spectral-spatial excitation further reduced lipid contamination and opened up the possibility of detecting metabolites with short T2 relaxation times. Results from the in vivo study were verified with post-histopathological data. Lastly, 1H-MRSI data was incorporated into the radiation treatment planning software and used to asses tumour control by escalating the radiation to prostate lesions that were identified by 1H-MRSI. In summary, this thesis demonstrates the clinical feasibility of using advanced spectroscopic imaging techniques for improved diagnosis and treatment of prostate cancer.

Prostate cancer is the most common malignancy afflicting Canadian men in 2011. Physicians use digital rectal exams (DRE), blood tests for prostate specific antigen (PSA) and transrectal ultrasound (TRUS)-guided biopsies for the initial diagnosis of prostate cancer. None of these tests detail the spatial extent of prostate cancer - information critical for using new therapies that can target cancerous prostate. With an MRI technique called proton magnetic resonance spectroscopic imaging (1H-MRSI), biochemical analysis of the entire prostate can be done without the need for biopsy, providing detailed information beyond the non-specific changes in hardness felt by an experienced urologist in a DRE, the presence of PSA in blood, or the “blind-guidance” of TRUS-guided biopsy. A hindrance to acquiring high quality 1H-MRSI data comes from signal originating from fatty tissue surrounding prostate that tends to mask or distort signal from within the prostate, thus reducing the overall clinical usefulness of 1H-MRSI data. This thesis has three major areas of focus: 1) The development of an optimized 1H-MRSI technique, called conformal voxel magnetic resonance spectroscopy (CV-MRS), to deal the with removal of unwanted lipid contaminating artifacts at short and long echo times. 2) An in vivo human study to test the CV-MRS technique, including healthy volunteers and cancer patients scheduled for radical prostatectomy or radiation therapy. 3) A study to determine the efficacy of using the 1H-MRSI data for optimized radiation treatment planning using modern delivery techniques like intensity modulated radiation treatment. Data collected from the study using the optimized CV-MRS method show significantly reduced lipid contamination resulting in high quality spectra throughout the prostate. Combining the CV-MRS technique with spectral-spatial excitation further reduced lipid contamination and opened up the possibility of detecting metabolites with short T2 relaxation times. Results from the in vivo study were verified with post-histopathological data. Lastly, 1H-MRSI data was incorporated into the radiation treatment planning software and used to asses tumour control by escalating the radiation to prostate lesions that were identified by 1H-MRSI. In summary, this thesis demonstrates the clinical feasibility of using advanced spectroscopic imaging techniques for improved diagnosis and treatment of prostate cancer.