title: Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models.
abstract: OBJECTIVE
  Lesion detection in acute stroke by computed-tomography perfusion (CTP) can be affected by incomplete bolus coverage in veins and hypoperfused tissue, so-called bolus truncation (BT), and low contrast-to-noise ratio (CNR). We examined the BT-frequency and hypothesized that image down-sampling and a vascular model (VM) for perfusion calculation would improve normo- and hypoperfused tissue classification.
  
  
  METHODS
  CTP datasets from 40 acute stroke patients were retrospectively analysed for BT. In 16 patients with hypoperfused tissue but no BT, repeated 2-by-2 image down-sampling and uniform filtering was performed, comparing CNR to perfusion-MRI levels and tissue classification to that of unprocessed data. By simulating reduced scan duration, the minimum scan-duration at which estimated lesion volumes came within 10% of their true volume was compared for VM and state-of-the-art algorithms.
  
  
  RESULTS
  BT in veins and hypoperfused tissue was observed in 9/40 (22.5%) and 17/40 patients (42.5%), respectively. Down-sampling to 128 × 128 resolution yielded CNR comparable to MR data and improved tissue classification (p = 0.0069). VM reduced minimum scan duration, providing reliable maps of cerebral blood flow and mean transit time: 5 s (p = 0.03) and 7 s (p < 0.0001), respectively).
  
  
  CONCLUSIONS
  BT is not uncommon in stroke CTP with 40-s scan duration. Applying image down-sampling and VM improve tissue classification.
  
  
  CONCLUSIONS
  • Too-short imaging duration is common in clinical acute stroke CTP imaging. • The consequence is impaired identification of hypoperfused tissue in acute stroke patients. • The vascular model is less sensitive than current algorithms to imaging duration. • Noise reduction by image down-sampling improves identification of hypoperfused tissue by CTP.
authors.affiliation: Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Nørrebrogade 44, Building 10G, 5th Floor, DK-8000, Aarhus C, Denmark, irene@cfin.au.dk.
authors.forename: Irene Klærke
authors.initials: IK
authors.lastname: Mikkelsen
authors.forename: P Simon
authors.initials: PS
authors.lastname: Jones
authors.forename: Lars Riisgaard
authors.initials: LR
authors.lastname: Ribe
authors.forename: Josef
authors.initials: J
authors.lastname: Alawneh
authors.forename: Josep
authors.initials: J
authors.lastname: Puig
authors.forename: Susanne Lise
authors.initials: SL
authors.lastname: Bekke
authors.forename: Anna
authors.initials: A
authors.lastname: Tietze
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Gillard
authors.forename: Elisabeth A
authors.initials: EA
authors.lastname: Warburton
authors.forename: Salva
authors.initials: S
authors.lastname: Pedraza
authors.forename: Jean-Claude
authors.initials: JC
authors.lastname: Baron
authors.forename: Leif
authors.initials: L
authors.lastname: Østergaard
authors.forename: Kim
authors.initials: K
authors.lastname: Mouridsen
mesh_terms: D000465:Algorithms; D015982:Bias; D002560:Cerebrovascular Circulation / Q000502:physiology*; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D012189:Retrospective Studies; D059629:Signal-To-Noise Ratio; D020521:Stroke / Q000473:pathology / Q000503:physiopathology*; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000379:methods