title: Temporal dependence of in vivo USPIO-enhanced MRI signal changes in human carotid atheromatous plaques.
abstract: BACKGROUND
  Ultrasmall superparamagnetic iron oxide (USPIO)-enhanced MRI has been shown to be a useful modality to image activated macrophages in vivo, which are principally responsible for plaque inflammation. This study determined the optimum imaging time-window to detect maximal signal change post-USPIO infusion using T1-weighted (T1w), T2*-weighted (T2*w) and quantitative T2* (qT2*) imaging.
  
  
  METHODS
  Six patients with an asymptomatic carotid stenosis underwent high resolution T1w, T2*w and qT2* MR imaging of their carotid arteries at 1.5 T. Imaging was performed before and at 24, 36, 48, 72 and 96 h after USPIO (Sinerem, Guerbet, France) infusion. Each slice showing atherosclerotic plaque was manually segmented into quadrants and signal changes in each quadrant were fitted to an exponential power function to model the optimum time for post-infusion imaging.
  
  
  RESULTS
  The power function determining the mean time to convergence for all patients was 46, 41 and 39 h for the T1w, T2*w and qT2* sequences, respectively. When modelling each patient individually, 90% of the maximum signal intensity change was observed at 36 h for three, four and six patients on T1w, T2*w and qT2*, respectively. The rates of signal change decrease after this period but signal change was still evident up to 96 h.
  
  
  CONCLUSIONS
  This study showed that a suitable imaging window for T1w, T2*w and qT2* signal changes post-USPIO infusion was between 36 and 48 h. Logistically, this would be convenient in bringing patients back for one post-contrast MRI, but validation is required in a larger cohort of patients.
authors.affiliation: University Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 2QQ, UK.
authors.forename: T Y
authors.initials: TY
authors.lastname: Tang
authors.forename: A J
authors.initials: AJ
authors.lastname: Patterson
authors.forename: S R
authors.initials: SR
authors.lastname: Miller
authors.forename: M J
authors.initials: MJ
authors.lastname: Graves
authors.forename: S P S
authors.initials: SP
authors.lastname: Howarth
authors.forename: J M
authors.initials: JM
authors.lastname: U-King-Im
authors.forename: Z Y
authors.initials: ZY
authors.lastname: Li
authors.forename: U
authors.initials: U
authors.lastname: Sadat
authors.forename: V E
authors.initials: VE
authors.lastname: Young
authors.forename: S R
authors.initials: SR
authors.lastname: Walsh
authors.forename: J R
authors.initials: JR
authors.lastname: Boyle
authors.forename: M E
authors.initials: ME
authors.lastname: Gaunt
authors.forename: J H
authors.initials: JH
authors.lastname: Gillard
mesh_terms: D000368:Aged; D002339:Carotid Arteries / Q000473:pathology; D016893:Carotid Stenosis / Q000473:pathology*; D003198:Computer Simulation; D003911:Dextrans; D005260:Female; D052203:Ferrosoferric Oxide; D006801:Humans; D007501:Iron; D018810:Magnetic Resonance Angiography / Q000379:methods*; D058185:Magnetite Nanoparticles; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D010087:Oxides; D013997:Time Factors
chemical_list: D003911:Dextrans; D058185:Magnetite Nanoparticles; D010087:Oxides; C097921:ferumoxtran-10; D007501:Iron; D052203:Ferrosoferric Oxide