Quantitative Magnetic Resonance has become more and more accepted for clinical trial in many fields. This technique not only can generate qMRI maps (such as T1/T2/PD) but also can be used for further postprocessing including segmentation of brain and characterization of different brain tissue. Another main application of qMRI is to measure the volume of the brain tissue such as the deep Grey Matter (dGM). The deep grey matter serves as the brain's "relay station" which receives and sends inputs between the cortical brain regions. An abnormal volume of the dGM is associated with certain diseases such as Fetal Alcohol Spectrum Disorders (FASD). The goal of this study is to investigate the effect of age on the volume change of the dGM using qMRI. Thirteen patients (mean age= 26.7 years old and age range from 0.5 to 72.5 years old) underwent imaging at a 1.5T MR scanner. Axial images of the entire brain were acquired with the mixed Turbo Spin-echo (mixed -TSE) pulse sequence. The acquired mixed-TSE images were transferred in DICOM format image for further analysis using the MathCAD 2001i software (Mathsoft, Cambridge, MA). Quantitative T1 and T2-weighted MR images were generated. The image data sets were further segmented using the dual-space clustering segmentation. Then volume of the dGM matter was calculated using a pixel counting algorithm and the spectrum of the T1/T2/PD distribution were also generated. Afterwards, the dGM volume of each patient was calculated and plotted on scatter plot. The mean volume of the dGM, standard deviation, and range were also calculated. The result shows that volume of the dGM is 47.5 ±5.3ml (N=13) which is consistent with former studies. The polynomial tendency line generated based on scatter plot shows that the volume of the dGM gradually increases with age at early age and reaches the maximum volume around the age of 20, and then it starts to decrease gradually in adulthood and drops much faster in elderly age. This result may help scientists to understand more about the aging of the brain and it can also be used to compare with the results from former studies using different techniques.

PURPOSE: Extremely low gestation age newborns (ELGANs) are at high risk for developmental brain abnormalities, which can lead to cognitive, physical, emotional and behavioral deficits. This study is to determine potential brain volumetric abnormalities of ELGAN children at 9 to 10 years of age. METHODS: High-resolution magnetic resonance imaging (MRI) scans were obtained from 82 ELGAN children using a dual-echo turbo spin-echo (DE-TSE) pulse sequence at 3.0T (or 1.5T at only one site). The DICOM MR images were processed with quantitative MRI algorithms programmed in Mathcad. The brain gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were quantified using semi-automated clustering segmentation algorithms. RESULTS: Total brain volumes (GM+WM) of ELGAN children showed a large distribution range from 400 to 1500 mL. About 63% of the children had smaller brain volumes while 5% of them had larger brain volumes compared to the published data from normal children at the same ages1. Smaller brain volumes were observed more often in males (74%) than in females (50%). WM reduction was the major change in ELGANs with over 90% of them (86% of males and 92% of females) having reduced WM volumes. GM volumes were either reduced (15%) or enlarged (32%); GM reduction was observed more often in males (31%) than in females (4.8%), while GM enlargement was more frequently observed in females (35%) than in males (28%). Intracranial CSF volumes range from 25 mL to 600 mL, with 16% of ELGAN children (9% of males and 21% of females) having smaller CSF volume, while 38% of them (53% of males and 27% of females) having larger CSF volume. Correlation analysis revealed a positive correlation between total intracranial matter (ICM) and CSF volumes (male: r = 0.4972, p = 0.0014 and female: r = 0.3233, p = 0.0125), but a negative correlation was found between brain volumes and CSF volumes (male: r = - 0.2998, p = 0.0424 and female: r = - 0.2279, p = 0.0596). Further analysis demonstrated a negative correlation between GM and CSF both in absolute (male: r = - 0.4489, p = 0.0039 and female: r = - 0.3769, p = 0.0041) and in relative (male: r = - 0.8675, p = 0.0000 and female: r = - 0.8350, p = 0.0000) volumes, while WM volumes did not correlate with CSF volumes. CONCLUSION: ELGAN children had mostly smaller brain volumes while some of them displayed larger brain volumes at ages of 9 to 10 years. The reduction of WM was a characteristic change in ELGAN children and contributed to smaller brain volumes. GM volumes either increased or decreased. Larger intracranial CSF volumes were associated with larger intracranial matter (ICM) volume.

The development of a child can be monitored by studying the changes in physical appearance or the development of capabilities e.g. walking and talking. But is it possible to find a quantitative measure for brain development? The aim of this thesis work is to investigate that possibility using quantitative magnetic resonance imaging (qMRI) images by answering the following questions: Can brain development be determined using qMRI? If so, what properties of the brain can be used? Can the age of a child be automatically detected with an algorithm? If so, how can this algorithm function? With what accuracy? Previous studies have shown that it is possible to detect properties in the brain changing with age, based on MRI images. These properties have e.g. been changes in T1 and T2 relaxation time, i.e. properties in water signal behavior that can be measured using multiple MR acquisitions. In the literature this was linked to a rapid myelination process that occurs after birth. Furthermore the organization and growth of the brain is a property that can be measured and monitored. This thesis have investigated several different properties in the brain based on qMRI images in order to identify those who have a strong correlation with age in the range 0-20 years. The properties that were found to have a high correlation were: Position of the first histogram peak in T1 weighted qMRI images, Fraction of white matter in the brain, Mean pixel value of PD weighted qMRI images, Volume of white matter in the brain, Curves on the form f(x) = ae^(-bx) +c are fitted to the data sets and confidence intervals are calculated to frame the statistical insecurity of the curve. The mean error in percent for the different properties can be seen in the list below: Property, Mean error [%] 0-20 years, Mean error [%] 0-3 years Peak position: 53.84, 98.17 Fraction of WM: 118.97, 71.67 Mean pixel value: 200.89, 126.28 Volume of WM: 241.72, 72.58 The conclusions drawn based on the presented results are that there are properties in the brain that correlates well to aging, but the error is too large for making a valid prediction of age over the entire range of 0-20 years. When decreasing the age range to 0-3 years the mean error becomes smaller, but it is still too large. More data is needed to evaluate and improve this result.

Image registration is the process of aligning two images such that their mutual features overlap. This is of great importance in several medical applications. In 2008 a novel method for simultaneous T1, T2 and proton density quantification was suggested. The method is in the field of quantitative Magnetic Resonance Imaging or qMRI. In qMRI parameters are quantified by a pixel-to-pixel fit of the image intensity as a function of different MR scanner settings. The quantification depends on several volumes of different intensities to be aligned. If a patient moves during the data aquisition the datasets will not be aligned and the results are degraded due to this. Since the quantification takes several minutes there is a considerable risk of patient movements. In this master thesis three image registration methods are presented and a comparison in robustness and speed was made. The phase based algorithm was suited for this problem and limited to finding rigid motion. The other two registration algorithms, originating from the Statistical Parametrical Mapping, SPM, package, were used as references. The result shows that the pixel-to-pixel fit is greatly improved in the datasets with found motion. In the comparison between the different methods the phase based algorithm turned out to be both the fastest and the most robust method.

This thesis investigates if support vector machine classification is a suitable approach when performing automatic segmentation of knee cartilage using quantitative magnetic resonance imaging data. The data sets used are part of a clinical project that investigates if patients that have suffered recent knee damage will develop cartilage damage. Therefore the thesis also investigates if the segmentation results can be used to predict the clinical outcome of the patients. Two methods that perform the segmentation using support vector machine classification are implemented and evaluated. The evaluation indicates that it is a good approach for the task, but the implemented methods needs to be further improved and tested on more data sets before clinical use. It was not possible to relate the cartilage properties to clinical outcome using the segmentation results. However, the investigation demonstrated good promise of how the segmentation results, if they are improved, can be used in combination with quantitative magnetic resonance imaging data to analyze how the cartilage properties change over time or vary between knees.

Segmentation of the brain into sub-volumes has many clinical applications. Manyneurological diseases are connected with brain atrophy (tissue loss). By dividingthe brain into smaller compartments, volume comparison between the compartmentscan be made, as well as monitoring local volume changes over time. Theformer is especially interesting for the left and right cerebral hemispheres, dueto their symmetric appearance. By using automatic segmentation, the time consumingstep of manually labelling the brain is removed, allowing for larger scaleresearch.In this thesis, three automatic methods for segmenting the brain from magneticresonance (MR) images are implemented and evaluated. Since neither ofthe evaluated methods resulted in sufficiently good segmentations to be clinicallyrelevant, a novel segmentation method, called SB-GC (shape bottleneck detectionincorporated in graph cuts), is also presented. SB-GC utilizes quantitative MRIdata as input data, together with shape bottleneck detection and graph cuts tosegment the brain into the left and right cerebral hemispheres, the cerebellumand the brain stem. SB-GC shows promises of highly accurate and repeatable resultsfor both healthy, adult brains and more challenging cases such as childrenand brains containing pathologies.

Multiple sclerosis is a neurological disease causing a degeneration of myelin around the axons in the central nervous system. This process leaves traces in the form of lesions, which can be distinguished in an MRI examination. It is important to detect these at an early stage to state diagnosis and initiate medication.  In this Master's Thesis, an automatic segmentation algorithm was developed, with the purpose of segmenting possible multiple sclerosis lesions. Secondly, a progression model was developed with the purpose of estimating the state of each individual lesion. The implementation was based on synthetic contrast weighted images, segmentation maps and quantitative relaxation maps produced by SyMRI (SyntheticMR, Linköping, Sweden). The automatic segmentation algorithm has a relatively high sensitivity but low precision, causing a large number of false positives. The algorithm performed better in the cerebrum compared to the cerebellum. The large number of false positives appeared mainly due to partial volume effects, creating hyperintense artifacts in synthetic T2W FLAIR images. A larger amount of data would have been desirable to create a more robust algorithm. The progression model showed promising results, with a clear correlation to the synthetic contrast-weighted images and segmentation maps available in SyMRI. The progression model could be useful in disease monitoring, medical decisions and diagnosis of Multiple Sclerosis.