Quantitative magnetization transfer MRI unbiased by on-resonance saturation and dipolar order contributions
Lucas Soustelle, Thomas Troalen, Andreea Hertanu, Jean-Philippe Ranjeva, Maxime Guye, Gopal Varma, David C. Alsop, Guillaume Duhamel, Olivier M. Girard
Abstract
Purpose
To demonstrate the bias in quantitative MT (qMT) measures introduced by the presence of dipolar order and on-resonance saturation (ONRS) effects using magnetization transfer (MT) spoiled gradient-recalled (SPGR) acquisitions, and propose changes to the acquisition and analysis strategies to remove these biases.
Methods
The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T1D) effects in Z-spectrum acquisitions, and a matched quantitative MT (qMT) mathematical model that includes ONRS effects of readout pulses. Variable flip angle and MT data were fitted jointly to simultaneously estimate qMT parameters (macromolecular proton fraction [MPF], T2,f, T2,b, R, and free pool T1). This framework is compared with standard qMT and investigated in terms of reproducibility, and then further developed to follow a joint single-point qMT methodology for combined estimation of MPF and T1.
Results
Bland–Altman analyses demonstrated a systematic underestimation of MPF (−2.5% and −1.3%, on average, in white and gray matter, respectively) and overestimation of T1 (47.1 ms and 38.6 ms, on average, in white and gray matter, respectively) if both ONRS and dipolar order effects are ignored. Reproducibility of the proposed framework is excellent (ΔMPF = −0.03% and ΔT1 = −19.0 ms). The single-point methodology yielded consistent MPF and T1 values with respective maximum relative average bias of −0.15% and −3.5 ms found in white matter.
Conclusion
The influence of acquisition strategy and matched mathematical model with regard to ONRS and dipolar order effects in qMT-SPGR frameworks has been investigated. The proposed framework holds promise for improved accuracy with reproducibility.