title: Automated perfusion-weighted MRI using localized arterial input functions.
abstract: OBJECTIVE
  To investigate the utility of an automated perfusion-weighted MRI (PWI) method for estimating cerebral blood flow (CBF) based on localized arterial input functions (AIFs) as compared to the standard method of manual global AIF selection, which is prone to deconvolution errors due to the effects of delay and dispersion of the contrast bolus.
  
  
  METHODS
  Analysis was performed on spin- and gradient-echo EPI images from 36 stroke patients. A local AIF algorithm created an AIF for every voxel in the brain by searching out voxels with the lowest delay and dispersion, and then interpolating and spatially smoothing them for continuity. A generalized linear model (GLM) for predicting tissue outcome, and MTT lesion volumes were used to quantify the performance of the localized AIF method in comparison with global methods using ipsilateral and contralateral AIFs.
  
  
  RESULTS
  The algorithm found local AIFs in each case without error and generated a higher area under the receiver operating characteristic (ROC) curve compared to both global-AIF methods. Similarly, the local MTT lesion volumes had the least mean squared error (MSE).
  
  
  CONCLUSIONS
  Automated CBF calculation using local AIFs is feasible and appears to produce more useful CBF maps.
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Division of Health Sciences and Technology, Harvard/MIT, Charlestown, Massachusetts 08129, USA.
authors.forename: Cory
authors.initials: C
authors.lastname: Lorenz
authors.forename: Thomas
authors.initials: T
authors.lastname: Benner
authors.forename: Poe Jou
authors.initials: PJ
authors.lastname: Chen
authors.forename: Chloe Joan
authors.initials: CJ
authors.lastname: Lopez
authors.forename: Hakan
authors.initials: H
authors.lastname: Ay
authors.forename: Ming Wang
authors.initials: MW
authors.lastname: Zhu
authors.forename: Nina M
authors.initials: NM
authors.lastname: Menezes
authors.forename: Hannu
authors.initials: H
authors.lastname: Aronen
authors.forename: Jari
authors.initials: J
authors.lastname: Karonen
authors.forename: Yawu
authors.initials: Y
authors.lastname: Liu
authors.forename: Juho
authors.initials: J
authors.lastname: Nuutinen
authors.forename: A Gregory
authors.initials: AG
authors.lastname: Sorensen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001783:Blood Flow Velocity / Q000502:physiology; D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D002536:Cerebral Arteries / Q000033:anatomy & histology / Q000502:physiology*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity