Literature DB >> 18826808

Use of perfusion- and diffusion-weighted imaging in differential diagnosis of acute and chronic ischemic stroke and multiple sclerosis.

Robert Zivadinov1, Niels Bergsland, Milena Stosic, Jitendra Sharma, Fernando Nussenbaum, Jacqueline Durfee, Nima Hani, Nadir Abdelrahman, Zeenat Jaisani, Alireza Minagar, Romy Hoque, Frederick E Munschauer, Michael G Dwyer.   

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. DISCUSSION: 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.

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Year:  2008        PMID: 18826808     DOI: 10.1179/174313208X341003

Source DB:  PubMed          Journal:  Neurol Res        ISSN: 0161-6412            Impact factor:   2.448


  9 in total

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Authors:  A Gupta; K Al-Dasuqi; F Xia; G Askin; Y Zhao; D Delgado; Y Wang
Journal:  AJNR Am J Neuroradiol       Date:  2017-05-18       Impact factor: 3.825

2.  The pattern of brain metabolism in chronic steno-occlusive cerebral artery disease.

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Journal:  Ann Transl Med       Date:  2022-09

3.  Intravascular contrast agent T2* relaxivity in brain tissue.

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4.  MRI characteristics of patients with antiphospholipid syndrome and multiple sclerosis.

Authors:  Milena Stosic; Julian Ambrus; Neeta Garg; Bianca Weinstock-Guttman; Murali Ramanathan; Bernadette Kalman; Alireza Minagar; Frederick E Munschauer; Timothy M Galey; Sara Hussein; Rohit Bakshi; Robert Zivadinov
Journal:  J Neurol       Date:  2009-07-26       Impact factor: 4.849

5.  Phase white matter signal abnormalities in patients with clinically isolated syndrome and other neurologic disorders.

Authors:  J Hagemeier; M Heininen-Brown; T Gabelic; T Guttuso; N Silvestri; D Lichter; L E Fugoso; N Bergsland; E Carl; J J G Geurts; B Weinstock-Guttman; R Zivadinov
Journal:  AJNR Am J Neuroradiol       Date:  2014-05-29       Impact factor: 3.825

6.  Perfusion and Diffusion Abnormalities of Multiple Sclerosis Lesions and Relevance of Classified Lesions to Disease Status.

Authors:  Lian Li; Michael Chopp; Siamak P Nejad-Davarani; Kourosh Jafari-Khouzani; Suresh C Patel; John Budaj; Mei Lu; Stanton B Elias; Mirela Cerghet; Quan Jiang
Journal:  J Neurol Neurophysiol       Date:  2014-04

7.  Cerebral circulation time is prolonged and not correlated with EDSS in multiple sclerosis patients: a study using digital subtracted angiography.

Authors:  Lucia Monti; Donatella Donati; Elisabetta Menci; Samuele Cioni; Matteo Bellini; Irene Grazzini; Sara Leonini; Paolo Galluzzi; Sandra Bracco; Sauro Severi; Luca Burroni; Alfredo Casasco; Lucia Morbidelli; Emiliano Santarnecchi; Pietro Piu
Journal:  PLoS One       Date:  2015-02-13       Impact factor: 3.240

8.  A semi-automated measuring system of brain diffusion and perfusion magnetic resonance imaging abnormalities in patients with multiple sclerosis based on the integration of coregistration and tissue segmentation procedures.

Authors:  Alfredo Revenaz; Massimiliano Ruggeri; Marcella Laganà; Niels Bergsland; Elisabetta Groppo; Marco Rovaris; Enrico Fainardi
Journal:  BMC Med Imaging       Date:  2016-01-14       Impact factor: 1.930

9.  The Diagnostic Ability of rs-DWI to Detect Subtle Acute Infarction Lesion in the Different Regions of the Brain and the Comparison between Different b-Values.

Authors:  Tanoj Bahadur Singh; Liwu Zhang; Xiaoting Huo; Guoping Liu; Hongyan Ni; Shun Zhang; Wenzhen Zhu; Jianzhong Yin
Journal:  Biomed Res Int       Date:  2018-06-05       Impact factor: 3.411

  9 in total

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