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Title
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Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework : a simulation study
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Author
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Abstract
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| Magnetic resonance imaging (MRI) based T-1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T-1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson's disease. In conventional T-1 MR relaxometry, a quantitative T-1 map is obtained from a series of T-1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T-1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T-1-weighted images is acquired and from which a high resolution (HR) T-1 map is directly estimated. In this paper, the SRR T-1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T-1-weighted images is modeled and the motion parameters are estimated simultaneously with the T-1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T-1 maps compared to a previously reported SRR based T-1 mapping approach. |
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Language
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English
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Source (journal)
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Fundamenta informaticae. - Amsterdam
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Publication
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Amsterdam
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Ios press
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2020
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ISSN
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0169-2968
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DOI
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10.3233/FI-2020-1896
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Volume/pages
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172
:2
(2020)
, p. 105-128
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ISI
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000514112300002
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Full text (Publisher's DOI)
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