Inversion Recovery (IR) is a powerful tool for contrast manipulation in Mag- netic Resonance Imaging (MRI). IR can provide strong contrast between tissues with different values of T1 relaxation times. The tissue magnetization stored at an IR image pixel can take positive as well as negative values. The corresponding polarity information is contained in the phase of the complex image. Due to numerous factors associated with the Magnetic Resonance (MR) scanner and the associated acquisition system, the acquired complex image is modulated by a spatially varying background phase which makes the retrieval of polarity information non-trivial. Many commercial MR scanners perform magnitude-only reconstruction which, due to loss of polarity information, reduces the dynamic contrast range. Phase sensitive IR (PSIR) can provide enhanced image contrast by estimating and removing the background phase and retrieving the correct polarity information. In this thesis, the background phase of complex MR image is modeled using a statistical model based on Markov Ran- dom Fields (MRF). Two model optimization methods have been developed. The first method is a computationally effcient algorithm for finding semi-optimal solutions satisfying the proposed model. Using an adaptive model neighborhood, it can recon- struct low SNR images with slow phase variations. The second method presents a region growing approach which can handle images with rapid phase variations. Ex- perimental results using computer simulations and in vivo experiments show that the proposed method is robust and can perform successful reconstruction even in adverse cases of low signal to noise ratios (SNRs) and high phase variations.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18451号 / 医博第3906号 / 新制||医||1004(附属図書館) / 31329 / 京都大学大学院医学研究科医学専攻 / (主査)教授 福山 秀直, 教授 村井 俊哉, 教授 平岡 眞寛 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM

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Discrete and Continuous spatial encoding methods are described with details of understanding principles and practical implications. Step by step experimental op- timization procedure of these methods to achieve slice selection are also discussed. In the subsequent chapters we use these methods for different applications. Spin-lattice relaxation parameters of NMR active nuclei provide valuable infor- mation on molecular dynamics. Single scan selective excitation methods of mea- surement of T1 result in significant reduction of time compared to the standard inversion recovery method and are attractive tools of applications in `Real time' NMR investigations of biological and chemical processes. It is shown here that the addition of the gradient echo following the selective excitation not only significantly improves the S/N ratio, but also makes GESSIR a versatile pulse sequence. Using this sequence, T1 values ranging from 2 s to 56 s have been measured with accuracy comparable to the standard IR experiment. This indicates that it is possible to utilize GESSIR for a wide range of molecules containing protons and hetero nuclei with medium to long T1 relaxation times as a routine NMR technique. The utility of the technique for studying other relaxation parameters has also been demonstrated. It may be mentioned that for measurement of relaxation parameters routinely, a few well-chosen points are enough. A fine selection of large number of experimental points could be useful when high accuracy is required or Chapter 3. GESSIR 91 for applications where certain property of the system investigated is changing in a continuous manner spatially and requires large number of slices to be selected as discussed in the next chapter. The long duration of time-honored two dimensional experiments is reduced to fraction of seconds by employing the ultrafast encoding experiments. Main com- plications in making the UF experiments available for routine use were the limited spectral widths and resolution in both UF and conventional dimensions. Various developments have been made in the path of improvements in increasing the spectral width in UF dimension. Of these, two experimental methods that are already proposed, namely the folding of peaks into the observable spectral window and the interleaved acquisition which doubles the spectral widths in both dimensions. The integration of covariance processing with ultrafast technique yields better correlated spectrum with considerable improvement in resolution. The effectiveness of the new processing is demonstrated for UF COSY experiments which can be easily extended to other ultrafast homonuclear experiments like TOCSY, NOESY as well as multi dimensions. The proposed processing method is an initial step to work on improving resolutions of UF data and making the ease of applicability of ultrafast spectroscopy as a routine multidimensional NMR. At the same time of this work W. Qui et.al [268] proposed a processing method based on covariance and pattern recognition for improving resolutions of spatially encoded data. They used pattern recognition algorithm also for avoiding the artifacts due to very low resolution data available with the UF experiment. They implemented the method UF TOCSY spectra and shown resolution improvement with the covariance pro- cessing for relatively more number of detection gradients which is many times hardware limited. Our method of covariance data processing is essentially same as that of Qui, less number of acquisition gradients were used in our processing, linear prediction and apodization concepts were utilized and the artifacts arise due mismatch of datas with positive and negative acquisition gradients are minimized by shifting one the data. In conclusion new methods of processing/the combination of various processing methods of the ultrafast data have the scope of improving the quality of ultrafast NMR spectra.

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It is well known that ions affect polymers in specific ways not solely based on electric charge, usually referred to as the Hoffmeister effect or ion specific effects. Poly(N-isopropylacrylamid) (PNIPAM) is a thermosensitive polymer with a LCST at 32℃. PNIPAM is a well studied polymer due to its similarities with denaturation of proteins in aqueous solutions. Utilizing diffusion NMR this report studied the effect different Hoffmeister anion concentrations have on the configuration of pre-transitional PNIPAM. A fractionation process was developed for PNIPAM, yielding a product of about 87 000 g/mol, used for diffusion measurements. Diffusion coefficients for PNIPAM in saline solutions ranging from 0 to 800 mM were measured for NaCl, NaClO4, NaSCN and NaI. Diffusion coefficients for PNIPAM were also measured at some concentrations of NaF, Na2SO4 and Na2CO3. Hydrodynamic radius was calculated from the diffusion coefficients. The report found a pre-transitional chain collapse of PNIPAM which increased with ionic concentrations of NaCl, NaClO4, NaSCN, NaF and Na2CO3, but not for NaI and Na2SO4. At 800 mM the hydrodynamic radius decreased with 9% for NaCl, 13% for NaClO4 and 5% for NaSCN. The hydrodynamic radius decreased with 19% at 300 mM Na2CO3 and with 10% at 400 mM NaF. There was a significant decrease in hydrodynamic radius for high concentrations of NaCl and NaClO4 but exact decrease needs to be replicated to validate the findings due to an unexpected large decrease in hydrodynamic radius already at 50 mM. Values for NaF and Na2CO3 should be replicated with internal standard to accomodate for possible precipitation of the longer polymer chains within the fraction.