title: Intracranial hematomas at a glance: advanced visualization for fast and easy detection.
abstract: OBJECTIVE
  To retrospectively assess the detection rate for intracranial hematomas achieved with use of curved maximum intensity projections (MIPs) that parallel the inner table of the skull compared with the rate achieved by reading transverse sections of computed tomography (CT) only.
  
  
  METHODS
  This retrospective study was approved by the institutional review board, which waived informed consent. A total of 314 consecutive patients who underwent CT for cranial trauma (155 male, 159 female; mean age ± standard deviation, 58 years ± 24 [range, 2-98 years]) were included. The algorithm unfolded the meningeal spaces into four images per patient. Four radiologists independently evaluated all cases. Hematomas less than 3 mm thick were considered thin. Radiologists were blinded to patient names, and patient and group orders were randomly assigned. The results were compared with a reference standard built by two experts. Logistic regression with repeated measurements was used for statistical analysis.
  
  
  RESULTS
  Use of the reference standard helped confirm 121 intracranial hematomas in 39 patients. For all readers, reading time for hematoma detection was significantly shorter (3-5 times shorter, P < .001) for curved MIPs. Mean lesion-based detection rate for all readers was 80% (193 of 242) for transverse sections and 83% (200 of 242) for curved MIPs. For thin hematomas, the mean detection rate increased from 20% (eight of 40) with transverse sections to 83% (33 of 40) with curved MIPs.
  
  
  CONCLUSIONS
  Curved MIPs of the meningeal spaces may shorten detection time for epidural and subdural hematomas, increase sensitivity (especially for thin hematomas), and reduce the required operator experience for detection.
authors.affiliation: Departments of Radiology, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria. helmut.ringl@meduniwien.ac.at
authors.forename: Helmut
authors.initials: H
authors.lastname: Ringl
authors.forename: Florian
authors.initials: F
authors.lastname: Stiassny
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Schima
authors.forename: Michael
authors.initials: M
authors.lastname: Toepker
authors.forename: Christian
authors.initials: C
authors.lastname: Czerny
authors.forename: Gerd
authors.initials: G
authors.lastname: Schueller
authors.forename: Ulrika
authors.initials: U
authors.lastname: Asenbaum
authors.forename: Julia
authors.initials: J
authors.lastname: Furtner
authors.forename: Stefan
authors.initials: S
authors.lastname: Hajdu
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Serles
authors.forename: Michael
authors.initials: M
authors.lastname: Weber
authors.forename: Christian J
authors.initials: CJ
authors.lastname: Herold
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D005260:Female; D006408:Hematoma, Subdural / Q000000981:diagnostic imaging*; D006801:Humans; D007223:Infant; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*