Multi‐spin echo T<sub>2</sub> relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging
Adam Dvorak, Vanessa Wiggermann, Guillaume Gilbert, Irene M. Vavasour, Erin L. MacMillan, Laura Barlow, Neale Wiley, Piotr Kozłowski, Alex L. MacKay, Alexander Rauscher, Shannon Kolind
Abstract
Purpose Myelin water imaging (MWI) provides a valuable biomarker for myelin, but clinical application has been restricted by long acquisition times. Accelerating the standard multi‐echo T 2 acquisition with gradient echoes (GRASE) or by 2D multi‐slice data collection results in image blurring, contrast changes, and other issues. Compressed sensing (CS) can vastly accelerate conventional MRI. In this work, we assessed the use of CS for in vivo human MWI, using a 3D multi spin‐echo sequence. Methods We implemented multi‐echo T 2 relaxation imaging with compressed sensing (METRICS) and METRICS with partial Fourier acceleration (METRICS‐PF). Scan‐rescan data were acquired from 12 healthy controls for assessment of repeatability. MWI data were acquired for METRICS in 9 m:58 s and for METRICS‐PF in 7 m:25 s, both with 1.5 × 2 × 3 mm 3 voxels, 56 echoes, 7 ms ΔTE, and 240 × 240 × 170 mm 3 FOV. METRICS was compared with a novel multi‐echo spin‐echo gold‐standard (MSE‐GS) MWI acquisition, acquired for a single additional subject in 2 h:2 m:40 s. Results METRICS/METRICS‐PF myelin water fraction had mean: repeatability coefficient 1.5/1.1, coefficient of variation 6.2/4.5%, and intra‐class correlation coefficient 0.79/0.84. Repeatability metrics comparing METRICS with METRICS‐PF were similar, and both sequences agreed with reference values from literature. METRICS images and quantitative maps showed excellent qualitative agreement with those of MSE‐GS. Conclusion METRICS and METRICS‐PF provided highly repeatable MWI data without the inherent disadvantages of GRASE or 2D multi‐slice acquisition. CS acceleration allows MWI data to be acquired rapidly with larger FOV, higher estimated SNR, more isotropic voxels and more echoes than with previous techniques. The approach introduced here generalizes to any multi‐component T 2 mapping application.