title: Validating a local Arterial Input Function method for improved perfusion quantification in stroke.
abstract: In bolus-tracking perfusion magnetic resonance imaging (MRI), temporal dispersion of the contrast bolus due to stenosis or collateral supply presents a significant problem for accurate perfusion quantification in stroke. One means to reduce the associated perfusion errors is to deconvolve the bolus concentration time-course data with local Arterial Input Functions (AIFs) measured close to the capillary bed and downstream of the arterial abnormalities causing dispersion. Because the MRI voxel resolution precludes direct local AIF measurements, they must be extrapolated from the surrounding data. To date, there have been no published studies directly validating these local AIFs. We assess the effectiveness of local AIFs in reducing dispersion-induced perfusion error by measuring the residual dispersion remaining in the local AIF deconvolved perfusion maps. Two approaches to locating the local AIF voxels are assessed and compared with a global AIF deconvolution across 19 bolus-tracking data sets from patients with stroke. The local AIF methods reduced dispersion in the majority of data sets, suggesting more accurate perfusion quantification. Importantly, the validation inherently identifies potential areas for perfusion underestimation. This is valuable information for the identification of at-risk tissue and management of stroke patients.
authors.affiliation: Brain Research Institute, Florey Neuroscience Institutes, Melbourne, Victoria, Australia. l.willats@brain.org.au
authors.forename: Lisa
authors.initials: L
authors.lastname: Willats
authors.forename: Soren
authors.initials: S
authors.lastname: Christensen
authors.forename: Henry K
authors.initials: HK
authors.lastname: Ma
authors.forename: Geoffrey A
authors.initials: GA
authors.lastname: Donnan
authors.forename: Alan
authors.initials: A
authors.lastname: Connelly
authors.forename: Fernando
authors.initials: F
authors.lastname: Calamante
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000098:blood supply*; D002536:Cerebral Arteries / Q000033:anatomy & histology / Q000502:physiology*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008875:Middle Aged; D008954:Models, Biological; D010477:Perfusion; D015203:Reproducibility of Results; D012189:Retrospective Studies; D020521:Stroke / Q000175:diagnosis* / Q000503:physiopathology