TY - JOUR

T1 - Optimizing the precision-per-unit-time of quantitative MR metrics

T2 - Examples for T1, T2, and DTI

AU - Fleysher, Lazar

AU - Fleysher, Roman

AU - Liu, Songtao

AU - Zaaraoui, Wafaa

AU - Gonen, Oded

N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2007/2

Y1 - 2007/2

N2 - Quantitative MR metrics (e.g., T1, T2, diffusion coefficients, and magnetization transfer ratios (MTRs etc)) are often derived from two images collected with one acquisition parameter changed between them (the "two-point" method). Since a low signal-to-noise-ratio (SNR) adversely affects the precision of these metrics, averaging is frequently used, although improvement accrues slowly - in proportion to the square root of imaging time. Fortunately, the relationship between the images' SNRs and the metric's precision can be exploited to our advantage. Using error propagation rules, we show that for a given sequence, specifying the total imaging time uniquely determines the optimal acquisition protocol. Specifically, instead of changing only one acquisition parameter and repeating the imaging pair until all available time is spent, we propose to adjust all of the parameters and the number of averages at each point according to their contribution to the sought metric's precision. The tactic is shown to increase the precision of the well-known two-point T1, T2, and diffusion coefficients estimation by 13-90% for the same sample, sequence, hardware, and duration. It is also shown that under this general framework, precision accrues faster than the square root of time. Tables of optimal parameters are provided for various experimental scenarios.

AB - Quantitative MR metrics (e.g., T1, T2, diffusion coefficients, and magnetization transfer ratios (MTRs etc)) are often derived from two images collected with one acquisition parameter changed between them (the "two-point" method). Since a low signal-to-noise-ratio (SNR) adversely affects the precision of these metrics, averaging is frequently used, although improvement accrues slowly - in proportion to the square root of imaging time. Fortunately, the relationship between the images' SNRs and the metric's precision can be exploited to our advantage. Using error propagation rules, we show that for a given sequence, specifying the total imaging time uniquely determines the optimal acquisition protocol. Specifically, instead of changing only one acquisition parameter and repeating the imaging pair until all available time is spent, we propose to adjust all of the parameters and the number of averages at each point according to their contribution to the sought metric's precision. The tactic is shown to increase the precision of the well-known two-point T1, T2, and diffusion coefficients estimation by 13-90% for the same sample, sequence, hardware, and duration. It is also shown that under this general framework, precision accrues faster than the square root of time. Tables of optimal parameters are provided for various experimental scenarios.

KW - Averaging

KW - Quantitative MRI

KW - Signal-to-noise-ratio (SNR)

KW - T

KW - T precision

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U2 - 10.1002/mrm.21144

DO - 10.1002/mrm.21144

M3 - Article

C2 - 17260375

AN - SCOPUS:33846781715

VL - 57

SP - 380

EP - 387

JO - Magnetic Resonance in Medicine

JF - Magnetic Resonance in Medicine

SN - 0740-3194

IS - 2

ER -