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A comprehensive protocol for multiparametric brain MRI
Dvir Radunsky1, Chen Solomon1, Tamar Blumenfeld-Katzir1, Neta Stern1, Shir Filo2, Aviv Mezer2, Anita Karsa3, Karin Shmueli3, Lucas Soustelle 4, Guillaume Duhamel4, Olivier M. Girard4, and Noam Ben-Eliezer1,5,6
1Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 2The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel, 3Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 4Aix Marseille University, CNRS, CRMBM, Marseille, France, 5Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 6Center for Advanced Imaging Innovation and Research (CAI2R), University Langone Medical Center, New York, Israel

Synopsis

The clinical utility of quantitative MRI (qMRI) techniques was demonstrated in numerous pathologies. This work investigated a range of qMRI pulse-sequences and processing methods with proven clinical applicability, aiming to establish a comprehensive and standard qMRI scan protocol for the brain. This multiparametric protocol provides a wide range of numeric maps (e.g., T1, T2, T2*, PD, M0, B1+, water and macromolecular fractions, susceptibility, mean diffusivity, and more) with whole-brain coverage, diverse set of clinical biomarkers, and the ability for stable longitudinal and multi-center investigations. Limitations and practical tips are provided for users interested in quantitative brain imaging.

Introduction

The field of quantitative MRI (qMRI) has been gaining increased attention in the last years. qMRI offers three main advantages: (1) quantitative parametric maps alongside traditional contrast-weighted images1; (2) higher sensitivity to subtle changes that are hard to detect visually2; (3) scalability of measured values (e.g. relaxation times) across different protocol implementations, scanners, and scan settings3.
The clinical utility of qMRI was exemplified in many pathologies3, for example, infiltration inside and beyond the peritumoral edema of glioblastomas using T1 mapping4, evaluation of steatosis and iron overload in liver disease using susceptibility maps5, and pathological changes in Multiple Sclerosis patients using T22 and magnetization transfer related maps6. Choosing the optimal set of qMRI imaging techniques is not trivial, particularly in light of the many pulse sequences and processing methods available, and within the time limitations of a single scan session.
In this work, we investigated a range of qMRI pulse-sequences and processing methods, in order to establish a comprehensive and quantitative brain MRI protocol that offers accurate and reproducible values. The ensuing brain protocol produces a range of parametric maps, which can facilitate the use of quantitative MRI biomarkers in longitudinal and multi-center studies.

Methods

The qMRI brain protocol was designed to include commonly used contrast mechanisms, and optimized with respect to the experimental settings and postprocessing technique. The list of pulse sequences and scan settings is delineated in Table 1. It includes two anatomical scans: 3D-MP2RAGE and FLAIR, and the following pulse-sequences for qMRI: multi-echo spin-echo (MESE); 3D multi-echo gradient-echo (GRE); spin-echo echo planner imaging (SE-EPI); SE Inversion-recovery EPI; and, 3D-spoiled GRE (SPGR) with, and without, magnetization transfer (MT) preparation pulses. The inhomogeneous MT (ihMT) sequence implementation is described in Mchinda et al7. Multiband acceleration technique implemented in the Center for Magnetic Resonance Research (CMRR) was used for diffusion scans, with multiband factor of 28. The total nominal scan time was 47:47 min:sec.
The list of pulse-sequences and processing methods used for each qMRI map is delineated in Table 2. These includes:
qT2, PD: T2 and PD maps were produced using the Echo Modulation Curve (EMC) platform9, after applying Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising10.
qT2*: T2* fitting was based on a simple mono-exponential decay.
qT1, B1+, M0, WF, MTVF: T1 mapping was based on mrQ software package11. Postprocessing steps includes brain masking (FSL12­), and linear and non-linear registrations (ANTs13 and SPM14, respectively). Based on the data acquired for mrQ, a transmitting field (B1+) map was generated, alongside tissue parameter maps- M0, water fraction (WF) and macromolecular tissue volume fraction (MTVF)11.
QSM: Quantitative Susceptibility Mapping calculations were based on the pipeline described by Karsa et al15. The reconstruction process included phase unwrapping and background field removal (MEDI toolbox16), FSL masking followed by noise-based mask erosion17 and tilt correction18,19.
MD, FA: Diffusion tensor imaging scans were analyzed using Explore DTI software20, producing the mean diffusivity (MD) and fractional anisotropy (FA) maps.
MTR, MTsat, ihMTR: MT ratio (MTR) maps were calculated as the ratio between SPGR data acquired with and without MT preparation pulse21. MTsat maps were reconstructed with inherent correction for RF inhomogeneity and T1 relaxation22. The inhomogeneous MTR (ihMTR) processing pipeline is described in23, and involves FSL masking, MP-PCA denoising (MRtrix324), and motion and Gibbs artifacts corrections (ihMT-MoCo and cosine apodization25).
To demonstrate the protocol utility, five healthy volunteers were scanned on a 3T whole-body scanner (Prisma, Siemens Healtheneers), using a 64-channals head coil, after obtaining an inform consent.

Results

Representative contrast-weighted images and quantitative maps from a single volunteer (Female, 28 y/o) are shown in Figures 1 and 2.
Figure 1 shows: (a-b) anatomical contrast-weighted FLAIR and MP2RAGE images; (c) mean diffusivity (MD); (d) fractional anisotropy (FA); (e) Magnetization transfer ratio (MTR); (f) MTsat; (g) inhomogeneous MTR (ihMTR); and (h) Quantitative susceptibility map (QSM).
Figure 2 shows quantitative maps of: relaxation times (a) T1; (b) T2; (c) T2*; The tissue fractional volumes of: (d) water (WF) and (e) macromolecules (MTVF); (f) transmitting field, B1+, inhomogeneity; (g) M0 (i.e., the hadamard product of the receive‐coil sensitivity and the proton density).

Discussion and Conclusions

The clinical applicability of qMRI is continuously increasing, offering a diverse range of numeric biomarkers. Herein we present a comprehensive qMRI protocol for brain imaging. The set of pulse-sequences, scan parameters, and postprocessing techniques were all adjusted to produce optimal contrast and stable and reproducible values.
The protocol produces a wide range of quantitative maps. Due to the long scan time, applying the entire protocol might be challenging. To accommodate shorter scan times, the protocol was design in a modular manner, allowing to acquire and reconstruct specific data and parametric maps. Further investigation of this protocol includes testing intra- and inter-subject variabilities.
The higher sensitivity and the reproducibility of qMRI data can facilitate longitudinal and multi-center investigations, and be utilized for many applications, including the investigation of pathologies in normal appearing WM and GM tissues, and the dependency of qMRI parameters on criterions such as age, sex, dominant hand, and more.

Acknowledgements

ISF Grant 2009/17

References

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Figures

Table 1 – list of pulse sequences and main scanning parameters

Table 2 – list of qMRI processing techniques and types of reconstructed maps

Figure 1 : Example results from a single volunteer (F, 28 y/o). Anatomical contrast-weighted (a) FLAIR and (b) MP2RAGE images; (c) mean diffusivity (MD); (d) fractional anisotropy (FA); (e) inhomogeneous magnetization transfer ratio (ihMTR); (f) MTsat; (g) MTR; and (h) Quantitative susceptibility map (QSM).

Figure 2 : Example quantitative maps from a single volunteer (F, 28 y/o). Relaxation times: (a) T1, (b) T2 and (c) T2*; Fractional volumes of: (d) water (WF) and (e) macromolecules (MTVF); (f) B1+ field inhomogeneity; (g) M0, the hadamard product of the receive‐coil sensitivity and the proton density (PD).

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)
1270
DOI: https://doi.org/10.58530/2022/1270