title: Quantification of left ventricular indices from SSFP cine imaging: impact of real-world variability in analysis methodology and utility of geometric modeling.
abstract: OBJECTIVE
  To assess the impact of "real-world" practice variation in the process of quantifying left ventricular (LV) mass, volume indices, and ejection fraction (EF) from steady-state free precession cardiovascular magnetic resonance (CMR) images. The utility of LV geometric modeling techniques was also assessed.
  
  
  METHODS
  The effect of short-axis- versus long-axis-derived LV base identification, simplified versus detailed endocardial contouring, and visual versus automated identification of end-systole were evaluated using CMR images from 50 consecutive, prospectively recruited patients. Additionally, the performance of six geometric models was assessed. Repeated measurements were performed on 25 scans (50%) in order to assess observer variability.
  
  
  RESULTS
  Simplified endocardial contouring significantly overestimated volumes and underestimated EF (-6 ± 4%, P < 0.0005) compared to detailed contouring. A mean difference of -34g (P < 0.0005) was observed between mass measurements made using short-axis- versus long-axis-derived LV base positioning. A technique involving long-axis LV base identification, signal threshold-based detailed endocardial contouring, and automated identification of end-systole had significantly higher observer agreement. Geometric models showed poor agreement with conventional analysis and high variability.
  
  
  CONCLUSIONS
  Real-world variability in CMR image analysis leads to significant differences in LV mass, volume and EF measurements, and observer variability. Appropriate reference ranges should be applied. Use of geometric models should be discouraged.
authors.affiliation: Division of Cardiology and Cardiothoracic Surgery, University Hospital of South Manchester, Manchester, UK. chrismiller@doctors.org.uk
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Miller
authors.forename: Peter
authors.initials: P
authors.lastname: Jordan
authors.forename: Alex
authors.initials: A
authors.lastname: Borg
authors.forename: Rachel
authors.initials: R
authors.lastname: Argyle
authors.forename: David
authors.initials: D
authors.lastname: Clark
authors.forename: Keith
authors.initials: K
authors.lastname: Pearce
authors.forename: Matthias
authors.initials: M
authors.lastname: Schmitt
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013318:Stroke Volume; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis*