No description available.

Das Signal-zu-Rausch-Verhältnis (SNR) stellt bei modernen Bildgebungstechniken in der Magnetresonanz-Tomographie heutzutage oftmals die entscheidende Limitation dar. Eine Verbesserung durch Modifikation der Hardware ist kostspielig und führt meistens zu einer Verstärkung anderer Probleme, wie zum Beispiel erhöhte Energiedeposition ins Gewebe. Im Gegensatz dazu ist Dichtegewichtung eine Methode, die eine SNR-Erhöhung durch Modifikation der Aufnahmetechnik ermöglicht. In der MR-Bildgebung erfolgt oftmals eine retrospektive Filterung des aufgenommenen Signalverlaufs, beispielsweise zur Artefaktreduktion. Damit einhergehend findet eine Veränderung der Modulationstransferfunktion (MTF) bzw. ihrer Fouriertransformierten, der räumlichen Antwortfunktion (SRF), statt. Optimales SNR wird nach dem Matched Filter-Theorem erzielt, wenn die nachträgliche Filterung dem aufgenommenen Signalverlauf proportional ist. Dies steht dem Ziel der Artefaktreduktion entgegen. Bei Dichtegewichtung steht durch nicht-kartesische Abtastung des k-Raums mit der k-Raum-Dichte ein zusätzlicher Freiheitsgrad zur Verfügung. Dieser ermöglicht es, im Falle eines konstanten Signalverlaufs eine gewünschte MTF ohne Filterung zu erreichen. Bei veränderlichem Signalverlauf kann ein SNR Matched Filter angewendet werden, dessen negative Einflüsse auf die MTF durch Dichtegewichtung kompensiert werden. Somit ermöglicht Dichtegewichtung eine vorgegebene MTF und gleichzeitig ein optimales SNR. In der vorliegenden Arbeit wurde Dichtegewichtung erstmals bei den schnellen Multi-Echo-Sequenzen Turbo-Spin-Echo und Echoplanar-Bildgebung (EPI) angewendet. Im Gegensatz zu bisherigen Implementierungen muss hier der Signalabfall durch T2- bzw. T2*-Relaxation berücksichtigt werden. Dies führt dazu, dass eine prospektiv berechnete dichtegewichtete Verteilung nur bei einer Relaxationszeit optimal ist. Bei Geweben mit abweichenden Relaxationszeiten können sich wie auch bei den kartesischen Varianten dieser Sequenzen Änderungen an SRF und SNR ergeben. Bei dichtegewichteter Turbo-Spin-Echo-Bildgebung des Gehirns konnte mit den gewählten Sequenzparametern ein SNR-Vorteil von 43 % gegenüber der kartesischen Variante erzielt werden. Die Akquisition wurde dabei auf die T2-Relaxationszeit von weißer Substanz optimiert. Da die meisten Gewebe im Gehirn eine ähnliche Relaxationszeit aufweisen, blieb der visuelle Gesamteindruck identisch zur kartesischen Bildgebung. Der SNR-Gewinn konnte in der dichtegewichteten Implementierung zur Messzeithalbierung genutzt werden. Dichtegewichtete EPI weist eine hohe Anfälligkeit für geometrische Verzerrungen, welche durch Inhomogenitäten des Hauptmagnetfeldes verursacht werden, auf. Die Verzerrungen konnten erfolgreich mit einer Conjugate Phase-Methode korrigiert werden. Dazu muss die räumliche Verteilung der Feldinhomogenitäten bekannt sein. Dazu ist zusätzlich zur eigentlichen EPI-Aufnahme die zeitaufwendige Aufnahme einer sogenannten Fieldmap erforderlich. Im Rahmen dieser Arbeit konnte eine Methode entwickelt werden, welche die zur Erlangung einer Fieldmap notwendige Aufnahmedauer auf wenige Sekunden reduziert. Bei dieser Art der Fieldmap-Aufnahme müssen jedoch durch Atmung hervorgerufene Effekte auf die Bildphase berücksichtigt werden. Die Fieldmap-Genauigkeit kann durch Aufnahme unter Atempause, Mittelung oder retrospektiver Phasenkorrektur erhöht werden. Für die gewählten EPI-Sequenzparameter wurde mit Dichtegewichtung gegenüber der kartesischen Variante ein SNR-Gewinn von 14 % erzielt. Anhand einer funktionellen MRT (fMRI)-Fingertapping-Studie konnte demonstriert werden, dass die SNR-Steigerung auch zu einer signifikant erhöhten Aktivierungsdetektion in Teilen der Hirnareale führt, die bei der Fingerbewegung involviert sind. Die Verwendung von zusätzlicher EPI-Phasenkorrektur und iterativer Optimierung der dichtegewichteten k-Raum-Abtastung führt zu weiteren Verbesserungen der dichtegewichteten Bildgebung mit Multi-Echo-Sequenzen. / Magnetic resonance imaging (MRI) is often limited by the signal to noise ratio (SNR). In standard Cartesian acquisition methods, the SNR can be improved by applying a so-called matched filter to the acquired raw data, which correlates with the anticipated signal profile. Unfortunately, this filter changes the spatial response function (SRF), which characterizes the imaging properties of the imaging method, in an undesired way. For example, a matched filter often amplifies undesired image artifacts and is thus normally omitted. In contrast, filters which change the SRF are typically applied, e.g., for artifact reduction. These however do not provide an optimal SNR. Density weighting is a method which allows a desired SRF and an optimal SNR at the same time. This is achieved by introducing a new degree of freedom to the SRF; the density of the acquisition steps in k-space. In this work, density weighting was adapted to turbo spin echo (TSE) and echo planar imaging (EPI). In contrast to earlier implementations of density weighting, signal relaxation has to be taken into consideration with these multi-echo sequences. As a result, the desired SRF and SNR are only obtained for one prospectively determined relaxation time. For deviating relaxation times, changes in SRF and SNR may occur. In density weighted TSE brain imaging, an average SNR gain of 43 % over Cartesian imaging could be achieved for the chosen sequence parameters. The density weighted acquisition was optimized for the T2 relaxation time of white matter. Since the relaxation times of most other tissues in the brain did not significantly differ, the overall visual impression of density weighted and Cartesian images was identical. The achieved SNR gain could be used to halve the acquisition time of the density weighted implementation. Density weighted EPI is especially prone to geometric distortions caused by inhomogeneities of the main magnetic field. The distortions could be successfully corrected with a conjugate phase method. For these methods, a time-consuming acquisition of a so-called field map is typically required. A method could be developed which greatly reduces the field map acquisition time to a few seconds. It was found that phase changes caused by respiration influence the field map accuracy of this and similar methods. A significantly higher accuracy could be achieved by an acquisition under breath-hold or by retrospective phase correction or averaging. It was demonstrated in an fMRI group study that an average SNR gain of 14 % for density weighted EPI resulted in an increased detection power in the activated brain areas. First results involving additional EPI phase correction and iterative k-space sampling optimization demonstrate further improvements of density weighted imaging with multi-echo sequences.

No description available.

Cerebral small vessel disease (SVD) is highly prevalent in older adults and is a predictor of stroke, dementia, and death. SVD is also associated with cognitive dysfunction, gait problems, and urinary incontinence. SVD is diagnosed based on white matter hyperintensities on T2 weighted scans. This thesis investigates the cardiac frequency component of resting state functional magnetic resonance imaging data in young healthy adults, older healthy adults, and older adults with pronounced SVD. A cardiac pulsatility metric is defined, and a tissue type contrast is observed between white matter, grey matter, and cerebrospinal fluid. Aging and disease effects are observed on cardiac pulsatility in white matter. The increased pulsatility may reflect the pathology of venous collagenosis and draining vein stenosis. Developing a better understanding of the etiology of SVD is an important step towards treating the disease.

Cerebral small vessel disease (SVD) is highly prevalent in older adults and is a predictor of stroke, dementia, and death. SVD is also associated with cognitive dysfunction, gait problems, and urinary incontinence. SVD is diagnosed based on white matter hyperintensities on T2 weighted scans. This thesis investigates the cardiac frequency component of resting state functional magnetic resonance imaging data in young healthy adults, older healthy adults, and older adults with pronounced SVD. A cardiac pulsatility metric is defined, and a tissue type contrast is observed between white matter, grey matter, and cerebrospinal fluid. Aging and disease effects are observed on cardiac pulsatility in white matter. The increased pulsatility may reflect the pathology of venous collagenosis and draining vein stenosis. Developing a better understanding of the etiology of SVD is an important step towards treating the disease.

Thesis (M.S.)--University of Alabama at Birmingham, 2009. / Description based on contents viewed June 2, 2009; title from PDF t.p. Includes bibliographical references (p. 48-49).

Functional Magnetic Resonance Imaging (fMRI) based studies are rapidly expanding in the field of preclinical research. The majority of these studies use Echo Planar Imaging (EPI) to measure Blood Oxygenation Level Dependent (BOLD) signal contrasts in the brain. In such studies the magnitude and statistical significances of these contrasts are then related to brain function and cognition. It is assumed that any observed signal contrast is ultimately due to differences in biological state and that scanner performance is stable and repeatable between subjects and studies. However, due to confounding issues introduced by in vivo subjects, little work has been undertaken to test this basic assumption. As the BOLD signal contrasts generated in such experiments are often very low, even small changes in scanner performance may dominate the BOLD contrast, distorting any biological conclusions drawn. A series of fMRI phantoms were produced to measure scanner performance independent of biological subjects. These phantoms produce specified signal contrast levels on demand during an fMRI scan by means of current-induced magnetic field gradients. These were used to generate data sets that emulated the BOLD signal contrast of in vivo imaging. Two studies examining scanner performance were then conducted on high-field preclinical MRI scanners. Firstly, in a longitudinal study on a single scanner, measurements were taken over a number of days across a week long period and then every two months over a year long period. Secondly, the behaviour of four preclinical scanners (three at 7T, one at 9.4T) was comparatively assessed. Measurements of several imaging parameters including contrast generated and functional contrast to noise ratio (fCNR) were obtained in both studies. If the scanners involved are truly comparable then they should generate similar measurement values. Across both studies parameter measurements showed significant differences for identical contrast settings on the phantom. Although signal contrast itself proved very comparable across the studies fCNR proved to be highly variable. As well as these measurements of longer tem behaviour proving variable, short and mid-term signal stability displayed a wide range of variability. Variations in the level and quality of both signal and noise were observed. Modelling of signal changes based on fundamental physical principles was also performed for comparison. The impact of these behaviours and variations on in vivo studies could result in skewed biological conclusions at any single site, with some sites exhibiting greater problems than others. The multisite results suggest potential difficulties when comparing biological conclusions between sites, even when using identical imaging parameters. In summary, these results suggest that a cautious approach should be taken with the conclusions of both fMRI and associated resting state connectivity studies that use EPI as their acquisition sequence. Improvements to both the experimental design of studies and regular quality monitoring of scanners should be undertaken to minimise these effects. Clinical MRI scanners should also be assessed for similar aberrations in behaviour.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19931号 / 医博第4151号 / 新制||医||1017(附属図書館) / 33017 / 京都大学大学院医学研究科医学専攻 / (主査)教授 高橋 淳, 教授 髙橋 良輔, 教授 大森 孝一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

In recent years, researchers have increasingly used magnetic resonance imaging (MRI) to study temporal skeletal muscle changes using gradient echo (GRE) echo planar imaging (EPI). These studies, typically involving exercise or ischemic challenges, have differentiated healthy subjects from athletic or unhealthy populations, such as those with peripheral vascular disease. However, the analysis methodologies have been lacking. In this thesis, two sessions of post-exercise GRE EPI data were collected from six subjects' lower legs using a 3 Tesla MRI scanner and a custom built ergometer. Past studies used common medical imaging software for motion correction. This work shows that such tools degrade leg image data by introducing motion, increasing root mean squared error in rest data by 22%. A new approach decreased it by 12%. EPI distortion correction in muscle images was also achieved, with the correlation ratio of functional and structural images increasing by up to 8%. In addition, a brief but intense artifact in GRE EPI muscle images results from muscle tissue moving in and out of the imaged volume. This through-plane artifact was successfully modelled as a mono-exponential decay for regression analysis, increasing the utility of the residual signal. The regression parameters were also leveraged to produce muscle displacement maps, identifying 44% of voxels as displaced. The maps were validated in a motion phantom and in-vivo using ultrasound. Finally, independent component analysis (ICA) was applied to post-exercise GRE EPI images to detect features in a data-driven, multivariate way and improve on conventional ROI selection methods. ICA produced parametric maps that were spatially correlated to working muscles from every trial (most with |R| > 0.4). The components were also separated from the susceptibility, motion, and blood vessel signals, and temporally reliable within individuals. These methodological advances represent increased rigour in the analysis of muscle GRE EPI images. / Thesis / Doctor of Philosophy (PhD) / Adequate blood circulation to muscles is important for good health. Researchers have used magnetic resonance imaging (MRI) techniques to assess blood and oxygen supply to muscles. The work in this thesis improves upon the analysis methods in prior work, especially in the areas of motion correction of the images and selection of individual muscle regions for analysis. Previous techniques could sometimes make motion in muscle images worse. This work provides valuable motion and distortion correction for muscle imaging, ensuring that measurements truly reflect muscle physiology. It also describes a method to remove an unwanted signal from post-exercise muscle data, and create a map of the internal muscle motion that occurred. Finally, an advanced mathematical technique was used to extract signals of interest and important spatial features from muscle image data automatically. The technique produced reliable results within and among subjects.

Biologists and physicians study complex biologic phenomena in which they use advanced imaging methods. They acquire images containing a lot of information which must be extracted in a correct way. This requires computer skills and knowledge in image processing methods which they seldom have. To overcome the problem, this master thesis aimed to develop a routine for artefact correction and information extraction from images acquired in a research project at the Karolinska Institutet in Stockholm. By developing the routine, the thesis showed how software developed for images of human can be applied to images of rats. The routine handles formatting issues and artefact corrections, calculates diffusion metrics, and performs statistical tests on spatially aligned magnetic resonance images of rats acquired with diffusion weighted echo planar imaging. The routine was verified by analysing the images that it had processed and was considered to create reliable images. Future studies within the field should focus on developing atlases of rats and continue the work with identifying how software developed for images of human can be applied to images of rats. / Biologer och läkare studerar komplexa biologiska processer för vilket de använder avancerade bildgivande metoder. De samlar bilder som innehåller mycket information vilken måste extraheras på ett korrekt sätt. Detta kräver god datorvana och kunskaper inom bildprocessning, vilket de sällan har. För att komma runt problemet, syftade den här masteruppsattsen till att utveckla en rutin för artefaktkorrigering och informationsextrahering från bilder tagna i ett forskningsprojekt vid Karolinska Institutet i Stockholm. Genom att utveckla rutinen, visar uppsattsen hur mjukvaror utvecklade för bilder av människa kan appliceras på bilder av råtta. Rutinen hanterar formatteringsproblem och artefaktkorrigering, beräknar diffusionsmått, och utför statistiska tester på spatiellt matchade magnetresonansavbildningar tagna med diffusionsviktade ekoplana metoder. Rutinen verifierades genom att analysera bilder som den processat och det konstaterades att den skapar korrekta bilder. Framtida studier inom området bör fokusera på att utveckla atlaser av råttor och fortsätta identifieringen av hur mjukvaror utvecklade för bilder av människa kan appliceras på bilder av råtta.