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Objective:Multiple sclerosis is an autoimmune disease which is characterized by demyelination of the white matter in central nervous system; its pathogenesis is unclear now. The onsets of symptoms are acute or sub acute and it is more common in young women, the lesions are also scattered in the brain, and can be expressed as the course of relapsing and remitting. In reality, sometimes some lesions are difficult to identify with cerebral infarction lesions in a traditional MR, so it leads to misdiagnosis or missed diagnosis easily, and causes great losses to the patients. Susceptibility weighted imaging (SWI) is a relatively new magnetic resonance (MR) technique that exploits the magnetic susceptibility differences of various tissues, such as blood, iron and calcification, as a new source of contrast enhancement. We can obtain both magnitude and phase images after post-processing. Phase image can be good to show iron deposition in the brain, and is currently the only MR technology which can measure iron quantitatively. In this study, we use this advantage to compare the phase values of region of interests among multiple sclerosis (MS), cerebral infarction and normal control subjects, and also observe the performance on SWI between multiple sclerosis (MS) and cerebral infarction, and explore the diagnostic and differential diagnostic value of SWI in the patients between MS and cerebral infarction.Methods:Twelve patients with MS, which was confirmed by clinical diagnosis and/or laboratory tests,20patients with cerebral infarction and30healthy volunteers as control group underwent brain scans using magnetic resonance imaging, which included conventional MR imaging (T1WI, T2WI, T2FLAIR) and SWI. When the raw data map we obtained is reprocessed, and we obtain amplitude and phase images, after filter processing, the phase image obtains phase mask, then repeatedly multiplied by amplitude image, and then SWI is obtained by minimal intensity projection of three dimensional data reconstruction. Phase of regions of interest were measured on the SWI phase diagram (region of interest, ROI), the software automatically calculate the phase value of each pixel within the ROI and obtain its mean value.Results:A total of227lesions were found with SWI in20patients with MS. One hundred and seven (46%) lesions showed homogeneous low signal,86(38%) lesions inhomogeneous low signal and37(16%) lesions inhomogeneous high signal. However, all lesions of20patients with cerebral infarction appeared inhomogeneous high or equal signal on SWI. The phase values of white matters of bilateral frontal lobe, parietal lobe, occipital lobe, brain stem and cerebellum were no significantly difference between gender and sides (p>0.05). Pearson correlation analysis presented no significant correlation between different ages and the phase values. The phase values of bilateral frontal white matter were lower than other regions, although there was no significantly statistical difference between them. Paired t-test analysis showed the phase values of MS in the white matter of bilateral frontal lobe, parietal lobe, occipital lobe, brain stem and cerebellum were lower than the control group, but the phase values of frontal lobe and occipital lobe were significantly lower (p<0.05). The phase values of the lesions of cerebral infarction were lower than the control group, but there was no significantly difference (p>0.05) between them, while the phase values between MS and cerebral infarction were no significantly differences (p>0.05). Compared with the cerebral infarction, the phase values of lesions in MS were significantly lower than those of the control group (p<0.05). In addition, among the227lesions of12patients with MS, there were126(56%) lesions with veins in the surrounding area,(30(13%) lesions with one or more venous bypass,49(22%) lesions with crossing veins).56(25%) lesions exhibited low signal in the surrounding area and the lesions of all20patients with cerebral infarction had no similar findings.Conclusion:SWI can improve awareness of the pathological features of the multiple sclerosis and cerebral infarction in vivo and understanding of differential diagnosis between them. To some extent, it also can be used as a quantitative complement to guide the clinical treatment options.
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