A 3D wavelet based encoding spectroscopic method (WE-SI) is investigated and implemented on a 3 Tesla Siemens Scanner. Compared to CSI, the proposed method is able to reduce acquisition time, and preserves the spatial metabolite distribution. As expected, a decrease in Signal to Noise Ratio (SNR) is noticed in WE-SI data compared to CSI. The dissertation explores important physical principles in MRI and spectroscopic imaging as a background, following by introduction of the wavelet encoding theory and comparison to Fourier encoding.

A 3D wavelet based encoding spectroscopic method (WE-SI) is investigated and implemented on a 3 Tesla Siemens Scanner. Compared to CSI, the proposed method is able to reduce acquisition time, and preserves the spatial metabolite distribution. As expected, a decrease in Signal to Noise Ratio (SNR) is noticed in WE-SI data compared to CSI. The dissertation explores important physical principles in MRI and spectroscopic imaging as a background, following by introduction of the wavelet encoding theory and comparison to Fourier encoding.

Magnetic Resonance Spectroscopy (MRS) can be applied to probe noninvasively the concentrations and distribution of metabolites of human tissue in vivo. As the improving of hardware and localization techniques, MRS becomes more and more important in clinical applications. Furthermore, some post-processing software, like LCModel, provide a graphical user interface for efficient and convenient analysis of MR spectroscopic imaging and thus increase the value of MRS applications. Although LCModel provides an efficient analysis and produces stable results, it can not provide metabolite map to observe the distribution of metabolite concentrations. For this reason our study processes the output data of LCModel and Digital Imaging and Communications in Medicine (DICOM) format MR images for 2D/3D metabolite map displaying. Users can use this software to observe the metabolic distribution in AP, SI and RL slice of brain tissue. In the meanwhile, as the absolute quantification of MRS has played more and more important role in clinical applications, this study also provides the LCModel end users an easy way for interpretation.

abstract: Magnetic resonance spectroscopic imaging (MRSI) is a valuable technique for assessing the in vivo spatial profiles of metabolites like N-acetylaspartate (NAA), creatine, choline, and lactate. Changes in metabolite concentrations can help identify tissue heterogeneity, providing prognostic and diagnostic information to the clinician. The increased uptake of glucose by solid tumors as compared to normal tissues and its conversion to lactate can be exploited for tumor diagnostics, anti-cancer therapy, and in the detection of metastasis. Lactate levels in cancer cells are suggestive of altered metabolism, tumor recurrence, and poor outcome. A dedicated technique like MRSI could contribute to an improved assessment of metabolic abnormalities in the clinical setting, and introduce the possibility of employing non-invasive lactate imaging as a powerful prognostic marker. However, the long acquisition time in MRSI is a deterrent to its inclusion in clinical protocols due to associated costs, patient discomfort (especially in pediatric patients under anesthesia), and higher susceptibility to motion artifacts. Acceleration strategies like compressed sensing (CS) permit faithful reconstructions even when the k-space is undersampled well below the Nyquist limit. CS is apt for MRSI as spectroscopic data are inherently sparse in multiple dimensions of space and frequency in an appropriate transform domain, for e.g. the wavelet domain. The objective of this research was three-fold: firstly on the preclinical front, to prospectively speed-up spectrally-edited MRSI using CS for rapid mapping of lactate and capture associated changes in response to therapy. Secondly, to retrospectively evaluate CS-MRSI in pediatric patients scanned for various brain-related concerns. Thirdly, to implement prospective CS-MRSI acquisitions on a clinical magnetic resonance imaging (MRI) scanner for fast spectroscopic imaging studies. Both phantom and in vivo results demonstrated a reduction in the scan time by up to 80%, with the accelerated CS-MRSI reconstructions maintaining high spectral fidelity and statistically insignificant errors as compared to the fully sampled reference dataset. Optimization of CS parameters involved identifying an optimal sampling mask for CS-MRSI at each acceleration factor. It is envisioned that time-efficient MRSI realized with optimized CS acceleration would facilitate the clinical acceptance of routine MRSI exams for a quantitative mapping of important biomarkers. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2016

Proton echo planar spectroscopic imaging(PEPSI) is a novel and rapid technique of magnetic resonance spectroscopic imaging(MRSI). To analyze the metabolite in PEPSI by using LCModel, an automatic reconstruction system is necessary. Recently, many researches use graphic processing unit(GPU) to accelerate imaging reconstruction, and Compute Unified Device Architecture(CUDA) is developed by C language, so the programmers can write the program in parallel computing easily. PEPSI data acquisition includes non water suppression and water suppression scans, each scan contains odd and even echoes, these two data are reconstructed separately. The image reconstruction contains k-space filter, time-domain filter, three-dimension fast Fourier transform(FFT), phase correction and combine odd and even data. We use MATLAB, C++ and parallel computing to implement PEPSI reconstruction, and parallel computing applied CUDA which proposed by NVIDIA. In our study, the averaged non water suppression spectroscopic imaging executed by three different programming language are almost the same. In our data scale, the execution time of parallel computing is faster than MATLAB and C++, especially in the FFT step. Therefore, we simulated and compared the performance of one- to three-dimension FFT. Our result shows that accelerating performance of GPU depends on the number of data points according to the performance of FFT and the execution time of single coil PEPSI reconstruction. While the amount of data points is larger than 65536, as demonstrated in our study, parallel computing contribute in terms of computational acceleration.

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.

Magnetic Resonance Spectroscopy (MRS) and Spectroscopic Imaging (MRSI) play an emerging role in clinical assessment, providing in vivo estimation of disease markers while being non-invasive and applicable to a large range of tissues. However, static magnetic field inhomogeneity, as well as eddy currents in the acquisition hardware, cause important distortions in the lineshape of acquired NMR spectra, possibly inducing significant bias in the estimation of metabolite concentrations. In the post-acquisition stage, this is classically handled through the use of pre-processing methods to correct the dataset lineshape, or through the introduction of more complex analytical model functions. This thesis concentrates on handling arbitrary lineshape distortions in the case of quantitation methods that use a metabolite basis-set as prior knowledge. Current approaches are assessed, and a novel approach is proposed, based on adapting the basis-set lineshape to the measured signal.Assuming a common lineshape to all spectral components, a new method is derived and implemented, featuring time domain local regression (LOWESS) filtering. Validation is performed on synthetic signals as well as on in vitro phantom data. Finally, a completely new approach to MRS quantitation is proposed, centred on the use of the compact spectral support of the estimated common lineshape. The new metabolite estimators are tested alone, as well as coupled with the more common residual-sum-of-squares MLE estimator, significantly reducing quantitation bias for high signal-to-noise ratio data.

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.