Efficiency analysis for quantitative MRI of T1 and T2 relaxometry methods. (26th July 2021)

• Record Type: Journal Article
• Authors: Leitão, David; Teixeira, Rui Pedro A. G.; Price, Anthony; Uus, Alena; Hajnal, Joseph V.; Malik, Shaihan J.
• Is Part Of: Physics in medicine & biology. Volume 66:Number 15(2021)
• Publication Date: 2021-07-26
• Subjects: efficiency -- quantitative MRI (qMRI) -- T1 mapping -- T2 mapping -- steady-state -- fingerprinting (MRF); Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac101f
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: This study presents a comparison of quantitative MRI methods based on an efficiency metric that quantifies their intrinsic ability to extract information about tissue parameters. Under a regime of unbiased parameter estimates, an intrinsic efficiency metric η was derived for fully-sampled experiments which can be used to both optimize and compare sequences. Here we optimize and compare several steady-state and transient gradient-echo based qMRI methods, such as magnetic resonance fingerprinting (MRF), for joint T 1 and T 2 mapping. The impact of undersampling was also evaluated, assuming incoherent aliasing that is treated as noise by parameter estimation. In vivo validation of the efficiency metric was also performed. Transient methods such as MRF can be up to 3.5 times more efficient than steady-state methods, when spatial undersampling is ignored. If incoherent aliasing is treated as noise during least-squares parameter estimation, the efficiency is reduced in proportion to the SNR of the data, with reduction factors of 5 often seen for practical SNR levels. In vivo validation showed a very good agreement between the theoretical and experimentally predicted efficiency. This work presents and validates an efficiency metric to optimize and compare the performance of qMRI methods. Transient methods were found to be intrinsically more efficient than steady-state methods, however the effect of spatial undersampling can significantly erode this advantage.