title: VASO-based calculations of CBV change: accounting for the dynamic CSF volume.
abstract: The goal of the vascular space occupancy (VASO) imaging technique is to use selective nulling of the blood signal to infer relative changes in cerebral blood volume (CBV). In accordance with recent work, we show that changes in the local CSF fraction (x(c)) with activation can significantly impact the VASO signal, thereby limiting our ability to infer DeltaCBV from DeltaVASO alone. Here we calculate CBV change using a VASO-based method which ACcounts for the Dynamic Cerebrospinal (ACDC) fluid fraction. By combining data from two separate VASO acquisitions that eliminate either the blood signal (VASO(b)) or the CSF signal (VASO(c)), a nonlinear least-squares optimization may then be used to simultaneously solve for the relative changes in CBV and CSF with activation. The method is applied across the whole brain during a breath-holding task, offering insight into the relationship between changes in CBV and x(c) associated with global vasodilatation. Calculations of mean changes in CBV in different volumes of interest obtained from the proposed method compare much better with previous (gold-standard) PET data than traditional VASO methods that do not account for a nonzero Deltax(c) with activation. This confirms the necessity of incorporating the dynamic CSF volume into VASO-based calculations of DeltaCBV.
authors.affiliation: Department of Biomedical Engineering, Yale University School of Medicine, 300 Cedar Street, New Haven, CT 06520, USA. amy.scouten@yale.edu
authors.forename: A
authors.initials: A
authors.lastname: Scouten
authors.forename: R T
authors.initials: RT
authors.lastname: Constable
mesh_terms: D000328:Adult; D001811:Blood Volume Determination; D001931:Brain Mapping / Q000379:methods; D002555:Cerebrospinal Fluid / Q000502:physiology*; D002560:Cerebrovascular Circulation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015233:Models, Statistical