Head imaging and spectroscopy at 1.5 Tesla

Bottomley, Paul A.; Hart, H.R.; Edelstein, William A.; Schenck, JF; Smith, L.S.; Mueller, O.; Redington, R. W.

doi:10.1016/0730-725x(82)90182-5

Cited by 3 publications

(1 citation statement)

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“…Lauterbur and Mansfield shared the 2003 Nobel Prize in Physiology or Medicine for their work in MRI. The world's first 1.5 T scanner was built in GE Research Center, New York (Bottomley, Hart, Edelstein, et al, 1982). Nowadays MRI is most commonly performed in the clinic at 1.5 T and 3 T, higher fields such as 7 T are gaining more popularity because of their increased sensitivity and resolution (Nowogrodzki, 2018).…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Advanced analysis of diffusion MRI data

2019

Linköping Studies in Science and Technology. Dissertations

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Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive imaging modality which can measure diffusion of water molecules, by making the MRI acquisition sensitive to diffusion. Diffusion MRI provides unique possibilities to study structural connectivity of the human brain, e.g. how the white matter connects different parts of the brain. Diffusion MRI enables a range of tools that permit qualitative and quantitative assessments of many neurological disorders, such as stroke and Parkinson. This thesis introduces novel methods for diffusion MRI data analysis. Prior to estimating a diffusion model in each location (voxel) of the brain, the diffusion data needs to be preprocessed to correct for geometric distortions and head motion. A deep learning approach to synthesize diffusion scalar maps from a T 1-weighted MR image is proposed, and it is shown that the distortion-free synthesized images can be used for distortion correction. An evaluation, involving both simulated data and real data, of six methods for susceptibility distortion correction is also presented in this thesis. A common problem in diffusion MRI is to estimate the uncertainty of a diffusion model. An empirical evaluation of tractography, a technique that permits reconstruction of white matter pathways in the human brain, is presented in this thesis. The evaluation is based on analyzing 32 diffusion datasets from a single healthy subject, to study how reliable tractography is. In most cases only a single dataset is available for each subject. This thesis presents methods based on frequentistic (bootstrap) as well as Bayesian inference, which can provide uncertainty estimates when only a single dataset is available. These uncertainty measures can then, for example, be used in a group analysis to downweight subjects with a higher uncertainty. Undertaking this PhD has been a truly life-changing experience for me and it would not have been possible to do without the support and guidance that I received from many people. I would like to first say a very big thank you to my supervisor Dr Anders Eklund for all the support and encouragement he gave me. Without his guidance and constant feedback this PhD would not have been achievable. My sincere thanks also go to Professor Hans Knutsson who has provided me an opportunity to start the PhD. Many thanks also to Dr Evren Özarslan who has provided insightful discussions and suggestions about the research. My deep appreciation goes out to the present members of the group: Cem Yolcu, David Abramian, Deneb Boito and Magnus Herberthson. I also thank people who were part of the group, including Jens Sjölund, Mats Andersson, Olivier Cros and Snehlata Shakya. I am honored to work with all of you. I also thank the wonderful staff in the Department of Biomedical Engineering for always being so helpful and friendly. I gratefully acknowledge the funding received towards my PhD from the Swedish research council, the ITEA/VINNOVA funded project BENEFIT, and the Seeing Organ Function (SOF) project supported by the Kn...

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Section: Magnetic Resonance Imagingmentioning

confidence: 99%