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Purpose: To develop a robust reconstruction pipeline for EPI data that enables 2D Nyquist phase error correction using sensitivity encoding without incurring major noise artifacts in low SNR data., Methods: SENSE with 2D phase error correction (PEC-SENSE) was combined with channel-wise noise removal using Marcenko-Pastur principal component analysis (MPPCA) to simultaneously eliminate Nyquist ghost artifacts in EPI data and mitigate the noise amplification associated with phase correction using parallel imaging. The proposed pipeline (coined SPECTRE) was validated in phantom DW-EPI data using the accuracy and precision of diffusion metrics; ground truth values were obtained from data acquired with a spin echo readout. Results from the SPECTRE pipeline were compared against PEC-SENSE reconstructions with three alternate denoising strategies: (i) no denoising; (ii) denoising of magnitude data after image formation; (iii) denoising of complex data after image formation. SPECTRE was then tested using high b $$ b $$ -value (i.e., low SNR) diffusion data (up to b = 3000 $$ b=3000 $$  s/mm 2 $$ {}^2 $$ ) in four healthy subjects., Results: Noise amplification associated with phase error correction incurred a 23% bias in phantom mean diffusivity (MD) measurements. Phantom MD estimates using the SPECTRE pipeline were within 8% of the ground truth value. In healthy volunteers, the SPECTRE pipeline visibly corrected Nyquist ghost artifacts and reduced associated noise amplification in high b $$ b $$ -value data., Conclusion: The proposed reconstruction pipeline is effective in correcting low SNR data, and improves the accuracy and precision of derived diffusion metrics., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA., Competing Interests: Declaration of competing interests TS is an employee of Philips UK. EF, JV, DSN and NYU School of Medicine, are co-inventors in the MP-PCA technology related to this research; a patent application has been filed and is pending. EF, JV, DSN, and TMS are shareholders and hold advisory roles at Microstructure Imaging, Inc., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To enable clinical applications of quantitative magnetization transfer (qMT) imaging by developing a fast method to map one of its fundamental model parameters, the bound pool fraction (BPF), in the human brain., Theory and Methods: The theory of steady-state MT in the fast-exchange approximation is used to provide measurements of BPF, and bound pool transverse relaxation time ( T 2 B ). A sequence that allows sampling of the signal during steady-state MT saturation is used to perform BPF mapping with a 10-min-long fully echo planar imaging-based MRI protocol, including inversion recovery T 1 mapping and B 1 error mapping. The approach is applied in 6 healthy subjects and 1 multiple sclerosis patient, and validated against a single-slice full qMT reference acquisition., Results: BPF measurements are in agreement with literature values using off-resonance MT, with average BPF of 0.114(0.100-0.128) in white matter and 0.068(0.054-0.085) in gray matter. Median voxel-wise percentage error compared with standard single slice qMT is 4.6%. Slope and intercept of linear regression between new and reference BPF are 0.83(0.81-0.85) and 0.013(0.11-0.16). Bland-Altman plot mean bias is 0.005. In the multiple sclerosis case, the BPF is sensitive to pathological changes in lesions., Conclusion: The method developed provides accurate BPF estimates and enables shorter scan time compared with currently available approaches, demonstrating the potential of bringing myelin sensitive measurement closer to the clinic., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a fast and robust method for measuring T 1 in the whole cervical spinal cord in vivo, and to assess its reproducibility., Methods: A spatially nonselective adiabatic inversion pulse is combined with zonally oblique-magnified multislice echo-planar imaging to produce a reduced field-of-view inversion-recovery echo-planar imaging protocol. Multi- inversion time data are obtained by cycling slice order throughout sequence repetitions. Measurement of T 1 is performed using 12 inversion times for a total protocol duration of 7 min. Reproducibility of regional T 1 estimates is assessed in a scan-rescan experiment on five heathy subjects., Results: Regional mean (standard deviation) T 1 was: 1108.5 (±77.2) ms for left lateral column, 1110.1 (±83.2) ms for right lateral column, 1150.4 (±102.6) ms for dorsal column, and 1136.4 (±90.8) ms for gray matter. Regional T 1 estimates showed good correlation between sessions (Pearson correlation coefficient = 0.89 (P value < 0.01); mean difference = 2 ms, 95% confidence interval ± 20 ms); and high reproducibility (intersession coefficient of variation approximately 1% in all the regions considered, intraclass correlation coefficient = 0.88 (P value < 0.01, confidence interval 0.71-0.95))., Conclusions: T 1 estimates in the cervical spinal cord are reproducible using inversion-recovery zonally oblique-magnified multislice echo-planar imaging. The short acquisition time and large coverage of this method paves the way for accurate T 1 mapping for various spinal cord pathologies. Magn Reson Med 79:2142-2148, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited., (© 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)