Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T2 decay data.

Multi-compartment T2 mapping has gained particular relevance for the study of myelin water in the brain. As a facilitator of rapid saltatory axonal signal transmission, myelin is a cornerstone indicator of white matter development and function. Regularized non-negative least squares fitting of multi-echo T2 data has been widely employed for the computation of the myelin water fraction (MWF), and the obtained MWF maps have been histopathologically validated. MWF measurements depend upon the quality of the data acquisition, B1+ homogeneity and a range of fitting parameters. In this special issue article, we discuss the relevance of these factors for the accurate computation of multi-compartment T2 and MWF maps. We generated multi-echo spin-echo T2 decay curves following the Carr-Purcell-Meiboom-Gill approach for various myelin concentrations and myelin T2 scenarios by simulating the evolution of the magnetization vector between echoes based on the Bloch equations. We demonstrated that noise and imperfect refocusing flip angles yield systematic underestimations in MWF and intra-/extracellular water geometric mean T2 (gmT2 ). MWF estimates were more stable than myelin water gmT2 time across different settings of the T2 analysis. We observed that the lower limit of the T2 distribution grid should be slightly shorter than TE1 . Both TE1 and the acquisition echo spacing also have to be sufficiently short to capture the rapidly decaying myelin water T2 signal. Among all parameters of interest, the estimated MWF and intra-/extracellular water gmT2 differed by approximately 0.13-4 percentage points and 3-4 ms, respectively, from the true values, with larger deviations observed in the presence of greater B1+ inhomogeneities and at lower signal-to-noise ratio. Tailoring acquisition strategies may allow us to better characterize the T2 distribution, including the myelin water, in vivo.

FLAIR2 improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images.

BACKGROUND: Accurate segmentation of MS lesions on MRI is difficult and, if performed manually, time consuming. Automatic segmentations rely strongly on the image contrast and signal-to-noise ratio. Literature examining segmentation tool performances in real-world multi-site data acquisition settings is scarce. OBJECTIVE: FLAIR2, a combination of T2-weighted and fluid attenuated inversion recovery (FLAIR) images, improves tissue contrast while suppressing CSF. We compared the use of FLAIR and FLAIR2 in LesionTOADS, OASIS and the lesion segmentation toolbox (LST) when applied to non-homogenized, multi-center 2D-imaging data. METHODS: Lesions were segmented on 47 MS patient data sets obtained from 34 sites using LesionTOADS, OASIS and LST, and compared to a semi-automatically generated reference. The performance of FLAIR and FLAIR2 was assessed using the relative lesion volume difference (LVD), Dice coefficient (DSC), sensitivity (SEN) and symmetric surface distance (SSD). Performance improvements related to lesion volumes (LVs) were evaluated for all tools. For comparison, LesionTOADS was also used to segment lesions from 3 T single-center MR data of 40 clinically isolated syndrome (CIS) patients. RESULTS: Compared to FLAIR, the use of FLAIR2 in LesionTOADS led to improvements of 31.6% (LVD), 14.0% (DSC), 25.1% (SEN), and 47.0% (SSD) in the multi-center study. DSC and SSD significantly improved for larger LVs, while LVD and SEN were enhanced independent of LV. OASIS showed little difference between FLAIR and FLAIR2, likely due to its inherent use of T2w and FLAIR. LST replicated the benefits of FLAIR2 only in part, indicating that further optimization, particularly at low LVs is needed. In the CIS study, LesionTOADS did not benefit from the use of FLAIR2 as the segmentation performance for both FLAIR and FLAIR2 was heterogeneous. CONCLUSIONS: In this real-world, multi-center experiment, FLAIR2 outperformed FLAIR in its ability to segment MS lesions with LesionTOADS. The computation of FLAIR2 enhanced lesion detection, at minimally increased computational time or cost, even retrospectively. Further work is needed to determine how LesionTOADS and other tools, such as LST, can optimally benefit from the improved FLAIR2 contrast.

FLAIR2: A Combination of FLAIR and T2 for Improved MS Lesion Detection.

BACKGROUND AND PURPOSE: FLAIR and double inversion recovery are important MR imaging scans for MS. The suppression of signal from CSF in FLAIR and the additional suppression of WM signal in double inversion recovery improve contrast between lesions, WM and GM, albeit at a reduced SNR. However, whether the acquisition of double inversion recovery is necessary is still debated. Here, we present an approach that allows obtaining CSF-suppressed images with improved contrast between lesions, WM and GM without strongly penalizing SNR. MATERIALS AND METHODS: 3D T2-weighted and 3D-FLAIR data acquired from September 2014 to April 2015 in healthy volunteers (23.4 ± 2.4 years of age; female/male ratio, 3:2) and patients (44.1 ± 14.0 years of age; female/male ratio, 4:5) with MS were coregistered and multiplied (FLAIR(2)). SNR and contrast-to-noise measurements were performed for focal lesions and GM and WM. Furthermore, data from 24 subjects with relapsing-remitting and progressive MS were analyzed retrospectively (52.7 ± 8.1 years of age; female/male ratio, 14:10). RESULTS: The GM-WM contrast-to-noise ratio was by 133% higher in FLAIR(2) than in FLAIR and improved between lesions and WM by 31%, 93%, and 158% compared with T2, DIR, and FLAIR, respectively. Cortical and juxtacortical lesions were more conspicuous in FLAIR(2). Furthermore, the 3D nature of FLAIR(2) allowed reliable visualization of callosal and infratentorial lesions. CONCLUSIONS: We present a simple approach for obtaining CSF suppression with an improved contrast-to-noise ratio compared with conventional FLAIR and double inversion recovery without the acquisition of additional data. FLAIR(2) can be computed retrospectively if T2 and FLAIR scans are available.