BACKGROUND: Conventional magnetic resonance imaging (MRI) methods do not quantify the severity of multiple sclerosis (MS) white matter lesions or measure pathology within normal-appearing white matter (NAWM). OBJECTIVE: Gradient Echo Plural Contrast Imaging (GEPCI), a fast MRI technique producing inherently co-registered images for qualitative and quantitative assessment of MS, was used to 1) correlate with disability; 2) distinguish clinical MS subtypes; 3) determine prevalence of veins co-localized within lesions in WM. METHODS: Thirty subjects representing relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS) subtypes were scanned with clinical and GEPCI protocols. Standard measures of physical disability and cognition were correlated with magnetic resonance metrics. Lesions with central veins were counted for RRMS subjects. RESULTS: Tissue damage load (TDL-GEPCI) and lesion load (LL-GEPCI) derived with GEPCI correlated better with MS functional composite (MSFC) measures and most other neurologic measures than lesion load derived with FLAIR (LL-FLAIR). GEPCI correctly classified clinical subtypes in 70% subjects. A central vein could be identified in 76% of WM lesions in RRMS subjects on GEPCI T2*-SWI images. CONCLUSION: GEPCI lesion metrics correlated better with neurologic disability than lesion load derived using FLAIR imaging, and showed promise in classifying clinical subtypes of MS. These improvements are likely attributable to the ability of GEPCI to quantify tissue damage.
BACKGROUND: Conventional magnetic resonance imaging (MRI) methods do not quantify the severity of multiple sclerosis (MS) white matter lesions or measure pathology within normal-appearing white matter (NAWM). OBJECTIVE: Gradient Echo Plural Contrast Imaging (GEPCI), a fast MRI technique producing inherently co-registered images for qualitative and quantitative assessment of MS, was used to 1) correlate with disability; 2) distinguish clinical MS subtypes; 3) determine prevalence of veins co-localized within lesions in WM. METHODS: Thirty subjects representing relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS) subtypes were scanned with clinical and GEPCI protocols. Standard measures of physical disability and cognition were correlated with magnetic resonance metrics. Lesions with central veins were counted for RRMS subjects. RESULTS: Tissue damage load (TDL-GEPCI) and lesion load (LL-GEPCI) derived with GEPCI correlated better with MS functional composite (MSFC) measures and most other neurologic measures than lesion load derived with FLAIR (LL-FLAIR). GEPCI correctly classified clinical subtypes in 70% subjects. A central vein could be identified in 76% of WM lesions in RRMS subjects on GEPCI T2*-SWI images. CONCLUSION: GEPCI lesion metrics correlated better with neurologic disability than lesion load derived using FLAIR imaging, and showed promise in classifying clinical subtypes of MS. These improvements are likely attributable to the ability of GEPCI to quantify tissue damage.
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Keywords:
CNS white matter; MS clinical subtypes; Multiple Sclerosis; perivascular cuffs; quantitative MRI
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