title: ABC/2 for rapid clinical estimate of infarct, perfusion, and mismatch volumes.
abstract: BACKGROUND
  Rapid and easy clinical assessments for volumes of infarction and perfusion mismatch are needed. We tested whether simple geometric models generated accurate estimates of these volumes.
  
  
  METHODS
  Acute diffusion-weighted image (DWI) and perfusion (mean transit time [MTT]) in 63 strokes and established infarct volumes in 50 subacute strokes were measured by computerized planimetry. Mismatch was defined as MTT/DWI > or = 1.2. Observers, blinded to planimetric values, measured lesions in three perpendicular axes A, B, and C. Geometric estimates of sphere, ellipsoid, bicone, and cylinder were compared to planimetric volume by least-squares linear regression.
  
  
  RESULTS
  The ABC/2 formula (ellipsoid) was superior to other geometries for estimating volume of DWI (slope 1.16, 95% confidence interval [CI] 0.94 to 1.38; R(2) = 0.91, p = 0.001) and MTT (slope 1.11, 95% CI 0.99 to 1.23; R(2) = 0.89, p = 0.001). The intrarater and interrater reliability for ABC/2 was high for both DWI (0.992 and 0.965) and MTT (0.881 and 0.712). For subacute infarct, the ABC/2 formula also best estimated planimetric volume (slope 1.00, 95% CI 0.98 to 1.19; R(2) = 0.74, p = 0.001). In general, sphere and cylinder geometries overestimated all volumes and bicone underestimated all volumes. The positive predictive value for mismatch was 92% and negative predictive value was 33%.
  
  
  CONCLUSIONS
  Of the models tested, ABC/2 is reproducible, is accurate, and provides the best simple geometric estimate of infarction and mean transit time volumes. ABC/2 has a high positive predictive value for identifying mismatch greater than 20% and might be a useful tool for rapid determination of acute stroke treatment.
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, 02129, USA. jsims@partners.org
authors.forename: J R
authors.initials: JR
authors.lastname: Sims
authors.forename: L Rezai
authors.initials: LR
authors.lastname: Gharai
authors.forename: P W
authors.initials: PW
authors.lastname: Schaefer
authors.forename: M
authors.initials: M
authors.lastname: Vangel
authors.forename: E S
authors.initials: ES
authors.lastname: Rosenthal
authors.forename: M H
authors.initials: MH
authors.lastname: Lev
authors.forename: L H
authors.initials: LH
authors.lastname: Schwamm
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000098:blood supply / Q000473:pathology / Q000503:physiopathology; D020520:Brain Infarction / Q000473:pathology* / Q000503:physiopathology*; D002536:Cerebral Arteries / Q000503:physiopathology; D002543:Cerebral Hemorrhage / Q000209:etiology / Q000473:pathology / Q000503:physiopathology; D002560:Cerebrovascular Circulation / Q000502:physiology; D003198:Computer Simulation; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000473:pathology; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012720:Severity of Illness Index; D013997:Time Factors