Accurate quantification of right ventricular mass at MR imaging by using cine true fast imaging with steady-state precession: study in dogs

Radiology. 2004 Feb;230(2):383-8. doi: 10.1148/radiol.2302021309. Epub 2003 Dec 29.

Abstract

Purpose: To assess the accuracy of cine magnetic resonance (MR) imaging with a segmented true fast imaging with steady-state precession (FISP) technique for right ventricular (RV) mass quantification.

Materials and methods: Fourteen dogs were imaged with a 1.5-T clinical MR imaging unit by using an electrocardiographically gated true FISP sequence. Contiguous segmented k-space cine images were acquired from the base of the RV to the apex during suspended respiration (repetition time msec/echo time msec, 3.2/1.6; section thickness, 5 mm; in-plane resolution, 1.0 x 1.3 mm2). After imaging, each dog was sacrificed, and the RV free wall was isolated and weighed. Each MR imaging data set was analyzed twice by each of two independent observers who were blinded to the results of RV mass measurement at autopsy, and the mass measurements at MR imaging were compared with the autopsy results by using linear regression and Bland-Altman analysis.

Results: RV mass measurements calculated by using the true FISP cine MR images were nearly identical to those at autopsy (R = 0.82, standard error of the estimate = 1.7 g, P >.05), with a mean difference between the autopsy and MR imaging measurements of 0.3 g +/- 1.7 (1.9% +/- 8.2) (P >.05). Inter- and intraobserver variations were small, with a mean interobserver variability of -0.1 g +/- 2.3 and a mean intraobserver variability of 0.2 g +/- 1.6 at every-section analysis.

Conclusion: In this animal model, true FISP cine MR imaging enabled accurate quantification of RV mass.

MeSH terms

  • Animals
  • Cardiac Volume / physiology*
  • Dogs
  • Electrocardiography / methods
  • Heart Ventricles / anatomy & histology*
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Linear Models
  • Magnetic Resonance Imaging, Cine / methods*
  • Mathematical Computing
  • Observer Variation
  • Sensitivity and Specificity