Quantitative T1 mapping using multi-slice multi-shot inversion recovery EPI

Sanchez Panchuelo, Rosa M; Mougin, Olivier; Turner, Robert; Francis, Susan T

Abstract

An efficient multi-slice inversion–recovery EPI (MS-IR-EPI) sequence for fast, high spatial resolution, quantitative T1 mapping is presented, using a segmented simultaneous multi-slice acquisition, combined with slice order shifting across multiple acquisitions. The segmented acquisition minimises the effective TE and readout duration compared to a single-shot EPI scheme, reducing geometric distortions to provide high quality T1 maps with a narrow point-spread function. The precision and repeatability of MS-IR-EPI T1 measurements are assessed using both T1-calibrated and T2-calibrated ISMRM/NIST phantom spheres at 3 and 7T and compared with single slice IR and MP2RAGE methods. Magnetization transfer (MT) effects of the spectrally-selective fat-suppression (FS) pulses required for in vivo imaging are shown to shorten the measured in-vivo T1-values. We model the effect of these fat suppression pulses on T1 measurements and show that the model can remove their MT contribution from the measured T1, thus providing accurate T1 quantification. High spatial resolution T1 maps of the human brain generated with MS-IR-EPI at 7T are compared with those generated with the widely implemented MP2RAGE sequence. Our MS-IR-EPI sequence provides high SNR per unit time and sharper T1 maps than MP2RAGE, demonstrating the potential for ultra-high resolution T1 mapping and the improved discrimination of functionally relevant cortical areas in the human brain.

Citation

Sanchez Panchuelo, R. M., Mougin, O., Turner, R., & Francis, S. T. (2021). Quantitative T1 mapping using multi-slice multi-shot inversion recovery EPI. NeuroImage, 234, Article 117976. https://doi.org/10.1016/j.neuroimage.2021.117976

• Journal Article Type: Article
• Acceptance Date: Mar 13, 2021
• Online Publication Date: Mar 26, 2021
• Publication Date: Jul 1, 2021
• Deposit Date: Mar 21, 2021
• Publicly Available Date: Mar 27, 2022
• Journal: NeuroImage
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 234
• Article Number: 117976
• DOI: https://doi.org/10.1016/j.neuroimage.2021.117976
• Keywords: Cognitive Neuroscience; Neurology
• Public URL: https://nottingham-repository.worktribe.com/output/5410606
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1053811921002536