Joint Maximum Likelihood Estimation of Motion and T₁ Parameters from Magnetic Resonance Images in a Super-resolution Framework: a Simulation Study

Issue title: Special Issue on Tomography and Neuroscience

Guest editors: Sara Brunetti, Paolo Dulio, Andrea Frosini and Grzegorz Rozenberg

Article type: Research Article

Authors: Beirinckx, Quinten^a | Ramos-Llordén, Gabriel^b | Jeurissen, Ben^c | Poot, Dirk H.J.^d | Parizel, Paul M.^{e; *} | Verhoye, Marleen^f | Sijbers, Jan^c | den Dekker, Arnold J.^{c; †}

Affiliations: [a] Department of Physics, University of Antwerp, Antwerp, Belgium. [email protected] | [b] Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA | [c] Department of Physics, University of Antwerp, Antwerp, Belgium | [d] Departments of Medical informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands | [e] David Hartley Chair of Radiology, Royal Perth Hospital, Perth, WA, Australia | [f] Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium

Correspondence: [] Address for correspondence: Quinten Beirinckx, imec - Vision Lab, Department of Physics, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium.

Note: [*] Also affiliated with: University of Western Australia Medical School

Note: [†] Also affiliated with: Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands

Abstract: Magnetic resonance imaging (MRI) based T1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson’s disease. In conventional T1 MR relaxometry, a quantitative T1 map is obtained from a series of T1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T1-weighted images is acquired and from which a high resolution (HR) T1 map is directly estimated. In this paper, the SRR T1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T1-weighted images is modeled and the motion parameters are estimated simultaneously with the T1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T1 maps compared to a previously reported SRR based T1 mapping approach.

Keywords: quantitative magnetic resonance imaging, super-resolution, T₁ mapping, maximum likelihood estimation, motion correction

DOI: 10.3233/FI-2020-1896

Journal: Fundamenta Informaticae, vol. 172, no. 2, pp. 105-128, 2020