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Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA
Published on: July 9, 2021
Joint system relaxometry (JSR) and Crámer-Rao lower bound optimization of sequence parameters: A framework for
Rui Pedro A G Teixeira1,2, Shaihan J Malik1, Joseph V Hajnal1,2
1Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
Purpose:
This study aims to increase the precision of single-compartment DESPOT relaxometry by two means: (i) a joint system relaxometry (JSR) approach that estimates parameters in a single step using all available data; and (ii) optimizing acquisition parameters by deploying a robust design tool based on the Crámer-Rao lower bound (CRLB).
Methods:
Following the development of the analysis and design capabilities, phantom and four in vivo subject experiments were performed to compare directly the precision achieved with DESPOT and JSR estimation using published protocols and protocols designed using a proposed CRLB framework.
Results:
Experimental data demonstrate JSR's ability to decrease relaxometry estimation variance. Phantom results show 72 to 77% improvement using the same data as conventional DESPOT. This is further improved to 81 to 87% using optimal parameters. Both experiments show systematic bias depending on the acquisition parameters used, which are shown to be highly reproducible and to vary with different magnetization transfer conditions.
Conclusions:
Compared with DESPOT, JSR produces reproducible relaxation maps with improved precision. Further improvement was achieved using CRLB as a protocol design tool. With this combined approach, it is possible to achieve submillimeter maps of ρ,T1,T2, and B0 in an 11-min examination, making the approach appealing for potential clinical use. Magn Reson Med 79:234-245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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