Fluid and White Matter Suppression: New Sensitive 3 T Magnetic Resonance Imaging Contrasts for Cortical Lesion Detection in Multiple Sclerosis

Objective

Cortical lesions are common in multiple sclerosis (MS), but their visualization is challenging on conventional magnetic resonance imaging. The uniform image derived from magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE_uni) detects cortical lesions with a similar rate as the criterion standard sequence, double inversion recovery. Fluid and white matter suppression (FLAWS) provides multiple reconstructed contrasts acquired during a single acquisition. These contrasts include FLAWS minimum image (FLAWS_min), which provides an exquisite sensitivity to the gray matter signal and therefore may facilitate cortical lesion identification, as well as high contrast FLAWS (FLAWS_hco), which gives a contrast that is similar to one of MP2RAGE_uni. In this study, we compared the manual detection rate of cortical lesions on MP2RAGE_uni, FLAWS_min, and FLAWS_hco in MS patients. Furthermore, we assessed whether the combined detection rate on FLAWS_min and FLAWS_hco was superior to MP2RAGE_uni for cortical lesions identification. Last, we compared quantitative T1 maps (qT1) provided by both MP2RAGE and FLAWS in MS lesions.

Materials and Methods

We included 30 relapsing-remitting MS patients who underwent MP2RAGE and FLAWS magnetic resonance imaging with isotropic spatial resolution of 1 mm at 3 T. Cortical lesions were manually segmented by consensus of 3 trained raters and classified as intracortical or leukocortical lesions on (1) MP2RAGE uniform/flat images, (2) FLAWS_min, and (3) FLAWS_hco. In addition, segmented lesions on FLAWS_min and FLAWS_hco were merged to produce a union lesion map (FLAWS_{min + hco}). Number and volume of all cortical, intracortical, and leukocortical lesions were compared among MP2RAGE_uni, FLAWS_min, and FLAWS_hco using Friedman test and between MP2RAGE_uni and FLAWS_{min + hco} using Wilcoxon signed rank test. The FLAWS T1 maps were then compared with the reference MP2RAGE T1 maps using relative differences in percentage. In an exploratory analysis, individual cortical lesion counts of the 3 raters were compared, and interrater variability was quantified using Fleiss ϰ.

Results

In total, 633 segmentations were made on the 3 contrasts, corresponding to 355 cortical lesions. The median number and volume of single cortical, intracortical, and leukocortical lesions were comparable among MP2RAGE_uni, FLAWS_min, and FLAWS_hco. In patients with cortical lesions (22/30), median cumulative lesion volume was larger on FLAWS_min (587 μL; IQR, 1405 μL) than on MP2RAGE_uni (490 μL; IQR, 990 μL; P = 0.04), whereas there was no difference between FLAWS_min and FLAWS_hco, or FLAWS_hco and MP2RAGE_uni. FLAWS_{min + hco} showed significantly greater numbers of cortical (median, 4.5; IQR, 15) and leukocortical (median, 3.5; IQR, 12) lesions than MP2RAGE_uni (median, 3; IQR, 10; median, 2.5; IQR, 7; both P < 0.001). Interrater agreement was moderate on MP2RAGE_uni (ϰ = 0.582) and FLAWS_hco (ϰ = 0.584), but substantial on FLAWS_min (ϰ = 0.614). qT1 in lesions was similar between MP2RAGE and FLAWS.

Conclusions

Cortical lesions identification in FLAWS_min and FLAWS_hco was comparable to MP2RAGE_uni. The combination of FLAWS_min and FLAWS_hco allowed to identify a higher number of cortical lesions than MP2RAGE_uni, whereas qT1 maps did not differ between the 2 acquisition schemes.