OBJECTIVE: To assess feasibility of automated edge detection in magnetic resonance (MR) flow calculations in a clinical setting with critically ill patients. MATERIAL AND METHODS: Velocity encoded cine-MR (VEC-MR) flow measurements cross-sectional area (CSA), mean spatial velocity (MSV), instantaneous flow (IF), flow (F), 0.5 T Philips, TR 800-800, TE=8 ms, 30 degrees flip angle, FOV 280 mm, 128 x 256 matrix, temporal resolution 16 time frames/RR, VENC=120 cm/s) were obtained in 20 major thoracic human vessels (ascending aorta, main, right and left pulmonary artery-AAO, MPA, RPA, LPA) of five patients, suffering from severe chronic thromboembolic pulmonary hypertension (CTEPH). Flow maps were evaluated by two independent observers using conventional manual edge detection (INTER m/m). Flow calculations were performed by one observer using both, manual and automated edge detection (INTRA m/a), by a second observer using automated edge detection two times (INTRA a/a) and by two independent observers using automated edge detection (INTER a/a). Evaluation time was measured. Linear regression analysis and Student's t-test were performed. RESULTS: Overall regression coefficients (r2) for INTER m/m, INTRA m/a, INTER a/a and INTRA a/a, respectively, were as follows: CSA, 0.91, 0.91, 0.96, 0.98; MSV, 0.97, 0.99, 0.99, 0.99; IF, 0.98, 0.99, 0.99, 0.99; F, 0.98, 0.99, 0.99, 0.99. Manual CSA values differed significantly from automated data in MPA (P=0.01), RPA (P=0.0008) and LPA (P=0.02). No difference was found for the other assessed parameters of the pulmonary circulation. Average evaluation time per vessel was 20.2+/-2.6 min for manual and 2.1+/-0.7 min for automated edge detection (P<0.00001). CONCLUSION: The software program used provided reproducible data, lead to a 90% reduction in evaluation and calculation time and, therefore, might excel the utilization of VEC-MR flow measurements. Despite variations in the evaluation of the pulmonary circulation CSAs, flow assessment is feasible in critically ill patients.
OBJECTIVE: To assess feasibility of automated edge detection in magnetic resonance (MR) flow calculations in a clinical setting with critically illpatients. MATERIAL AND METHODS: Velocity encoded cine-MR (VEC-MR) flow measurements cross-sectional area (CSA), mean spatial velocity (MSV), instantaneous flow (IF), flow (F), 0.5 T Philips, TR 800-800, TE=8 ms, 30 degrees flip angle, FOV 280 mm, 128 x 256 matrix, temporal resolution 16 time frames/RR, VENC=120 cm/s) were obtained in 20 major thoracic human vessels (ascending aorta, main, right and left pulmonary artery-AAO, MPA, RPA, LPA) of five patients, suffering from severe chronic thromboembolic pulmonary hypertension (CTEPH). Flow maps were evaluated by two independent observers using conventional manual edge detection (INTER m/m). Flow calculations were performed by one observer using both, manual and automated edge detection (INTRA m/a), by a second observer using automated edge detection two times (INTRA a/a) and by two independent observers using automated edge detection (INTER a/a). Evaluation time was measured. Linear regression analysis and Student's t-test were performed. RESULTS: Overall regression coefficients (r2) for INTER m/m, INTRA m/a, INTER a/a and INTRA a/a, respectively, were as follows: CSA, 0.91, 0.91, 0.96, 0.98; MSV, 0.97, 0.99, 0.99, 0.99; IF, 0.98, 0.99, 0.99, 0.99; F, 0.98, 0.99, 0.99, 0.99. Manual CSA values differed significantly from automated data in MPA (P=0.01), RPA (P=0.0008) and LPA (P=0.02). No difference was found for the other assessed parameters of the pulmonary circulation. Average evaluation time per vessel was 20.2+/-2.6 min for manual and 2.1+/-0.7 min for automated edge detection (P<0.00001). CONCLUSION: The software program used provided reproducible data, lead to a 90% reduction in evaluation and calculation time and, therefore, might excel the utilization of VEC-MR flow measurements. Despite variations in the evaluation of the pulmonary circulation CSAs, flow assessment is feasible in critically illpatients.
Authors: Giovanni Di Leo; Ida Daniela D'Angelo; Marco Alì; Paola Maria Cannaò; Giovanni Mauri; Francesco Secchi; Francesco Sardanelli Journal: Radiol Med Date: 2016-11-22 Impact factor: 3.469
Authors: Bas Versluis; Marjolein H G Dremmen; Patty J Nelemans; Joachim E Wildberger; Geert-Willem Schurink; Tim Leiner; Walter H Backes Journal: PLoS One Date: 2012-03-08 Impact factor: 3.240