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New approach to phase correction in multi-echo T2 relaxometry
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. (Biomedical systems)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. (Biomedical systems)
2014 (English)In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 249, 100-107 p.Article in journal (Refereed) Published
Abstract [en]

Estimation of the transverse relaxation time, T-2, from multi-echo spin-echo images is usually performed using the magnitude of the noisy data, and a least squares (LS) approach. The noise in these magnitude images is Rice distributed, which can lead to a considerable bias in the LS-based T-2 estimates. One way to avoid this bias problem is to estimate a real-valued and Gaussian distributed dataset from the complex data, rather than using the magnitude. In this paper, we propose two algorithms for phase correction which can be used to generate real-valued data suitable for LS-based parameter estimation approaches. The first is a Weighted Linear Phase Estimation algorithm, abbreviated as WELPE. This method provides an improvement over a previously published algorithm, while simplifying the estimation procedure and extending it to support multi-coil input. The algorithm fits a linearly parameterized function to the multi-echo phase-data in each voxel and, based on this estimated phase, projects the data onto the real axis. The second method is a maximum likelihood estimator of the true decaying signal magnitude, which can be efficiently implemented when the phase variation is linear in time. The performance of the algorithms is demonstrated via Monte Carlo simulations, by comparing the accuracy of the estimates. Furthermore, it is shown that using one of the proposed algorithms enables more accurate T-2 estimates; in particular, phase corrected data significantly reduces the estimation bias in multi-component T-2 relaxometry example, compared to when using magnitude data. WELPE is also applied to a 32-echo in vivo brain dataset, to show its practical feasibility.

Place, publisher, year, edition, pages
2014. Vol. 249, 100-107 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-233700DOI: 10.1016/j.jmr.2014.09.025ISI: 000346552700014OAI: oai:DiVA.org:uu-233700DiVA: diva2:753710
Projects
SysTEAM
Funder
EU, European Research Council, 247035
Available from: 2014-10-16 Created: 2014-10-08 Last updated: 2017-12-05Bibliographically approved

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Björk, MarcusStoica, Peter

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