title: Automated quantitation of pericardiac fat from noncontrast CT.
abstract: BACKGROUND
  Increased abdominal visceral fat has been shown to be a cardiovascular risk factor. Preliminary studies indicate that pericardiac fat (PF) may provide similar information. We aimed to develop new software (QFAT) for automatic quantitation of PF from noncontrast cardiac CT and compare PF measures to other cardiovascular risk factors.
  
  
  METHODS
  QFAT accepts user-defined range of noncontrast transverse cardiac CT slices, automatically segments the heart, and determines PF volume (PFV) as contiguous pericardial fat voxels. PFV normalized to cardiac volume defines PF ratio (PFR). QFAT and manual processing (MAN) was performed in 105 patients (mean BMI, 27; range, 17-41) by 2 observers.
  
  
  RESULTS
  Mean processing time was 20 +/- 4 seconds for QFAT, and 9 +/- 6 minutes for MAN. There was excellent agreement between QFAT and MAN for PFV (R = 0.98) and PFR (R = 0.98). MAN and QFAT interobserver variability were comparable. Interscan and interscanner variability for PFV and PFR were comparable to corresponding interobserver variability. PFV (R = 0.88, P < 0.0001) and PFR (R = 0.81, P < 0.0001) correlated strongly with abdominal visceral fat area, moderately with BMI (R = 0.58, P < 0.0001 and R = 0.48, P < 0.0001), and weakly with abdominal subcutaneous fat area (R = 0.33, P < 0.0001 and R = 0.32, P = 0.001).
  
  
  CONCLUSIONS
  PFV and PFR can be accurately and automatically quantified from noncontrast CT acquired for coronary calcium screening and may provide complementary information regarding cardiovascular risk.
authors.affiliation: Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.forename: Yasuyuki
authors.initials: Y
authors.lastname: Suzuki
authors.forename: Shoji
authors.initials: S
authors.lastname: Suzuki
authors.forename: Muneo
authors.initials: M
authors.lastname: Ohba
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.forename: Donna
authors.initials: D
authors.lastname: Polk
authors.forename: Leslee J
authors.initials: LJ
authors.lastname: Shaw
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
mesh_terms: D050153:Abdominal Fat / Q000473:pathology*; D050154:Adiposity; D000368:Aged; D000465:Algorithms; D002114:Calcinosis / Q000175:diagnosis* / Q000473:pathology; D003327:Coronary Disease / Q000175:diagnosis* / Q000473:pathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010496:Pericardium / Q000473:pathology* / Q000502:physiology; D012189:Retrospective Studies; D012307:Risk Factors; D013997:Time Factors; D014055:Tomography, Emission-Computed / Q000295:instrumentation*