Literature DB >> 17229540

Comparing diffusion-weighted and T2-weighted MR imaging for the quantification of infarct size in a neonatal rat hypoxic-ischemic model at 24h post-injury.

Yanxin Wang1, Pik-To Cheung, Gary X Shen, Inderjeet Bhatia, Ed Xue Wu, Deqiang Qiu, Pek-Lan Khong.   

Abstract

PURPOSE: In a neonatal rat model of hypoxic-ischemic (HI) brain injury, using T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), we aim to determine the best MRI method of lesion quantification that reflects infarct size.
MATERIALS AND METHODS: Twenty 7-day-old rats underwent MRI 24h after HI brain injury was induced. Lesion size relative to whole brain was measured using T2WI and apparent diffusion coefficient (ADC) maps, applying thresholds of 60%, 70% and 80% contralateral control hemisphere mean ADC, and at day 10 post-HI on pathology with TTC staining. Multiple linear regression analysis was used to study the relationships between lesion size at MRI and pathology.
RESULTS: Lesion size measurement using all MRI methods significantly correlated with infarct size at pathology; using T2WI, r=0.808 (p<0.001), using 80% ADC, 70% ADC and 60% ADC thresholds, r=0.888 (p<0.001), 0.761, (p<0.001) and 0.569 (p=0.014), respectively. Eighty percent ADC threshold was found to be the only significant independent predictor of final infarct volume (adjusted R(2)=0.775).
CONCLUSION: At 24h post-HI, lesion size on DWI, using 80% ADC threshold is the best predictor of final infarct volume. Although T2WI performed less well, it has the advantage of superior spatial resolution and is technically less demanding. These are important considerations for experiments which utilize MRI as a surrogate method for lesion quantification in the neonatal rat HI model.

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Year:  2006        PMID: 17229540     DOI: 10.1016/j.ijdevneu.2006.12.003

Source DB:  PubMed          Journal:  Int J Dev Neurosci        ISSN: 0736-5748            Impact factor:   2.457


  6 in total

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Authors:  Inge A Mulder; Artem Khmelinskii; Oleh Dzyubachyk; Sebastiaan de Jong; Nathalie Rieff; Marieke J H Wermer; Mathias Hoehn; Boudewijn P F Lelieveldt; Arn M J M van den Maagdenberg
Journal:  Front Neuroinform       Date:  2017-01-31       Impact factor: 4.081

2.  Predicting Mortality in Patients With "Malignant" Middle Cerebral Artery Infarction Using Susceptibility-Weighted Magnetic Resonance Imaging: Preliminary Findings.

Authors:  Shu-Ping Chao; Chia-Yuen Chen; Fong Y Tsai; Wing P Chan; Chin-I Chen
Journal:  Medicine (Baltimore)       Date:  2016-02       Impact factor: 1.889

Review 3.  Imaging Ischemic and Hemorrhagic Disease of the Brain in Dogs.

Authors:  Susan A Arnold; Simon R Platt; Karine P Gendron; Franklin D West
Journal:  Front Vet Sci       Date:  2020-05-27

4.  Machine learning identifies stroke features between species.

Authors:  Salvador Castaneda-Vega; Prateek Katiyar; Francesca Russo; Kristin Patzwaldt; Luisa Schnabel; Sarah Mathes; Johann-Martin Hempel; Ursula Kohlhofer; Irene Gonzalez-Menendez; Leticia Quintanilla-Martinez; Ulf Ziemann; Christian la Fougere; Ulrike Ernemann; Bernd J Pichler; Jonathan A Disselhorst; Sven Poli
Journal:  Theranostics       Date:  2021-01-01       Impact factor: 11.556

5.  RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation.

Authors:  Juan Miguel Valverde; Artem Shatillo; Riccardo De Feo; Olli Gröhn; Alejandra Sierra; Jussi Tohka
Journal:  Front Neurosci       Date:  2020-12-22       Impact factor: 4.677

6.  Development and characterization of a Yucatan miniature biomedical pig permanent middle cerebral artery occlusion stroke model.

Authors:  Simon R Platt; Shannon P Holmes; Elizabeth W Howerth; Kylee Jo J Duberstein; C Robert Dove; Holly A Kinder; Emily L Wyatt; Amie V Linville; Vivian W Lau; Steven L Stice; William D Hill; David C Hess; Franklin D West
Journal:  Exp Transl Stroke Med       Date:  2014-03-23
  6 in total

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