Navigator-Guided Motion and B0 Correction of T2*-Weighted Magnetic Resonance Imaging Improves Multiple Sclerosis Cortical Lesion Detection

Abstract

Background: Cortical lesions are common in multiple sclerosis (MS). T 2 *weighted (T 2 *w) imaging at 7 T is relatively sensitive for cortical lesions, but quality is often compromised by motion and main magnetic field (B 0 ) fluctuations. Purpose: The aim of this study was to determine whether motion and B 0 correction with a navigator-guided gradient-recalled echo sequence can improve cortical lesion detection in T 2 *w magnetic resonance imaging. Materials and Methods: In this prospective study, a gradient-recalled echo sequence incorporating a navigator allowing for motion and B 0 field correction was applied to collect T 2 *w images at 7 T from adults with MS between August 2019 and March 2020. T 2 *-weighted images were acquired in 1 to 3 partially overlapping scans per individual and were reconstructed using global average B 0 correction ("uncorrected") or motion correction and spatially linear B 0 correction ("corrected"). Image quality rating and manual segmentation of cortical lesions were performed on uncorrected and corrected images. Lesions seen on a single scan were retrospectively evaluated on the complementary scan. The association of cortical lesions with clinical disability was assessed. Mixed models were used to determine the effect of correction on lesion detection as well as on the relationship between disability and lesion count. Results: A total of 22 T 2 *w scans were performed on 11 adults with MS (mean [SD] age, 49 [11] years; 8 women). Quality improved for 20 of 22 scans (91%) after correction. A total of 69 cortical lesions were identified on uncorrected images (median per scan, 2; range, 0-11) versus 148 on corrected images (median per scan, 4.5; range, 0-25; rate ratio [RR], 2.1; P < 0.0001). For low-quality uncorrected scans with moderate to severe motion artifact (18/22, 82%), there was an improvement in cortical lesion detection with correction (RR, 2.5; P < 0.0001), whereas there was no significant change in cortical lesion detection for high-quality scans (RR, 1.3; P = 0.43). Conclusions: Navigator-guided motion and B 0 correction substantially improves the overall image quality of T 2 *w magnetic resonance imaging at 7 T and increases its sensitivity for cortical lesions.

A Deep Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging

“…11 Artificial intelligence and, in particular, deep learning approaches have rapidly become popular mathematical models to be applied on MRI data, to make predictions or decisions without the need to identify a priori the critical features that will be used in the model. [12][13][14][15] In the field of MS, deep learning approaches have been mainly applied for tasks related to the improvement of white matter (WM) lesions detection [16][17][18][19] and segmentation, 20 brain tissue segmentation, 21 and, only recently, for differential diagnosis with other WM diseases. 22,23 Conversely, the application of deep learning algorithms to predict disease progression in MS remains largely unexplored.…”

Real‐time shimming with FID navigators

“…This approach may be also combined with prospective motion correction using motion measurements from an external tracking system or additional navigators for simultaneous field stabilization and motion control 20 . Joint retrospective correction for head motion and motion‐induced field changes using dual‐echo image‐based navigators has been shown to substantially improve $$$ {\mathrm{T}}_2^{\ast } $$$ ‐weighted image quality 25,26 and SWI, 10 with applications to improved cortical lesion detection in multiple sclerosis 8 . A previous study combined prospective optical tracking with field measurements from FIDnavs 44 ; however, since no calibration data were acquired, field measurements were limited to global frequency offsets measured from the phase difference between FIDnav measurements.…”

“…20 Joint retrospective correction for head motion and motion-induced field changes using dual-echo image-based navigators has been shown to substantially improve T * 2 -weighted image quality 25,26 and SWI, 10 with applications to improved cortical lesion detection in multiple sclerosis. 8 A previous study combined prospective optical tracking with field measurements from FIDnavs 44 ; however, since no calibration data were acquired, field measurements were limited to global frequency offsets measured from the phase difference between FIDnav measurements. Since head motion induces spatially-varying field changes, 45 updating first-order shim terms is expected to yield improved results.…”

“…Hardware imperfections, such as gradient heating and eddy currents, are further sources of artifacts 6,7 . These effects are particularly evident in gradient echo sequences with long echo times, such as $$$ {\mathrm{T}}_2^{\ast } $$$‐weighted imaging, 2,8 which forms the basis of SWI 9,10 and quantitative susceptibility mapping 11–13 . While imaging at ultra‐high field enhances sensitivity to microscopic susceptibility variations, which improves contrast in both magnitude and phase images, 14,15 the impact of macroscopic perturbations in the background magnetic field also increases with field strength, which is detrimental to image quality 1 …”

New Prospects for Ultra-High-Field Magnetic Resonance Imaging in Multiple Sclerosis

“…One such approach used B 0 correction with a navigator-guided GRE sequence to enhance sensitivity at UHF imaging to detect cortical lesions. 157 By applying this image correction method, more than double the number of cortical lesions could be detected using a T2*w sequence. Similar techniques using navigator echoes have been applied to correct for resonance frequency variations.…”