title: Use of perfusion- and diffusion-weighted imaging in differential diagnosis of acute and chronic ischemic stroke and multiple sclerosis.
abstract: OBJECTIVE
  To investigate differences in lesions and surrounding normal appearing white matter (NAWM) by perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) in patients with acute and chronic ischemic stroke and multiple sclerosis (MS).
  
  
  METHODS
  Study subjects included 45 MS patients, 22 patients with acute ischemic stroke and 20 patients with chronic ischemic stroke. All subjects underwent T2-weighted imaging (WI), flair attenuated inversion recovery (FLAIR), DWI and dynamic contrast enhanced PWI. Apparent diffusion coefficient (ADC) and mean transit time (MTT) maps were generated and values were calculated in the acute and chronic ischemic and demyelinating lesions, and in NAWM for distances of 5, 10 and 15 mm. Fifty-three acute ischemic and 33 acute demyelinating lesions, and 775 chronic ischemic and 998 chronic demyelinating lesions, were examined. Univariate, multivariate and data mining analyses were used to examine the feasibility of a prediction model between different lesion types. Correctly and incorrectly classified lesions, true positive (TP), false positive (FP) and precision rates were calculated.
  
  
  RESULTS
  Patients with acute ischemic lesions presented more prolonged mean MTT values in lesions (p=0.002) and surrounding NAWM for distances of 5, 10 and 15 mm (all p<0.0001) than those with acute demyelinating lesions. In multinomial logistic regression analysis, 65 of 86 acute lesions were correctly classified (75.6%). The TP rates were 81.1% for acute ischemic lesions and 66.7% for acute demyelinating lesions. The FP rates were 33.3% for acute ischemic and 18.9% for acute demyelinating lesions. The precision was 79.6% for classification of acute ischemic lesions and 68.8% for prediction of acute demyelinating lesions. The logistic model tree decision algorithm revealed that prolonged MTT of surrounding NAWM for a distance of 15 mm (> or =7459.2 ms) was the best classifier of acute ischemic versus acute demyelinating lesions. Patients with chronic ischemic lesions presented higher mean ADC (p<0.0001) and prolonged MTT (p=0.013) in lesions, and in surrounding NAWM for distances of 5, 10 and 15 mm (all p<0.0001), compared to the patients with chronic demyelinating lesions. Data mining analyses did not show reliable predictability for correctly discerning between chronic ischemic and chronic demyelinating lesions. The precision was 56.7% for classification of chronic ischemic and 58.9% for prediction of chronic demyelinating lesions.
  
  
  CONCLUSIONS
  We found prolonged MTT values in lesions and surrounding NAWM of patients with acute and chronic ischemic stroke when compared to MS patients. The use of PWI is a promising tool for differential diagnosis between acute ischemic and acute demyelinating lesions. The results of this study contribute to a better understanding of the extent of hemodynamic abnormalities in lesions and surrounding NAWM in patients with MS.
authors.affiliation: Buffalo Neuroimaging Analysis Center, Department of Neurology, State University of New York, Buffalo, NY, USA. rzivadinov@bnac.net
authors.forename: Robert
authors.initials: R
authors.lastname: Zivadinov
authors.forename: Niels
authors.initials: N
authors.lastname: Bergsland
authors.forename: Milena
authors.initials: M
authors.lastname: Stosic
authors.forename: Jitendra
authors.initials: J
authors.lastname: Sharma
authors.forename: Fernando
authors.initials: F
authors.lastname: Nussenbaum
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Durfee
authors.forename: Nima
authors.initials: N
authors.lastname: Hani
authors.forename: Nadir
authors.initials: N
authors.lastname: Abdelrahman
authors.forename: Zeenat
authors.initials: Z
authors.lastname: Jaisani
authors.forename: Alireza
authors.initials: A
authors.lastname: Minagar
authors.forename: Romy
authors.initials: R
authors.lastname: Hoque
authors.forename: Frederick E
authors.initials: FE
authors.lastname: Munschauer
authors.forename: Michael G
authors.initials: MG
authors.lastname: Dwyer
mesh_terms: D000368:Aged; D002545:Brain Ischemia / Q000150:complications / Q000175:diagnosis*; D002544:Cerebral Infarction / Q000175:diagnosis; D002908:Chronic Disease; D003937:Diagnosis, Differential; D003952:Diagnostic Imaging / Q000379:methods; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000175:diagnosis*; D010477:Perfusion / Q000379:methods; D020521:Stroke / Q000175:diagnosis* / Q000209:etiology