AIMS: Various methods and post-processing software packages have been developed to quantify left atrial (LA) fibrosis using 3D late gadolinium-enhancement cardiac magnetic resonance (LGE-CMR) images. Currently, it remains unclear how the results of these methods and software packages interrelate. METHODS AND RESULTS: Forty-seven atrial fibrillation (AF) patients underwent 3D-LGE-CMR imaging prior to their AF ablation. LA fibrotic burden was derived from the images using open-source CEMRG software and commercially available ADAS 3D-LA software. Both packages were used to calculate fibrosis based on the image intensity ratio (IIR)-method. Additionally, CEMRG was used to quantify LA fibrosis using three standard deviations (3SD) above the mean blood pool signal intensity. Intraclass correlation coefficients were calculated to compare LA fibrosis quantification methods and different post-processing software outputs. The percentage of LA fibrosis assessed using IIR threshold 1.2 was significantly different from the 3SD-method (29.80 ± 14.15% vs. 8.43 ± 5.42%; P < 0.001). Correlation between the IIR-and SD-method was good (r = 0.85, P < 0.001) although agreement was poor [intraclass correlation coefficient (ICC) = 0.19; P < 0.001]. One-third of the patients were allocated to a different fibrosis category dependent on the used quantification method. Fibrosis assessment using CEMRG and ADAS 3D-LA showed good agreement for the IIR-method (ICC = 0.93; P < 0.001). CONCLUSIONS: Both, the IIR1.2 and 3SD-method quantify atrial fibrotic burden based on atrial wall signal intensity differences. The discrepancy in the amount of LA fibrosis between these methods may have clinical implications when patients are classified according to their fibrotic burden. There was no difference in results between post-processing software packages to quantify LA fibrosis if an identical quantification method including the threshold was used.
AIMS: Various methods and post-processing software packages have been developed to quantify left atrial (LA) fibrosis using 3D late gadolinium-enhancement cardiac magnetic resonance (LGE-CMR) images. Currently, it remains unclear how the results of these methods and software packages interrelate. METHODS AND RESULTS: Forty-seven atrial fibrillation (AF) patients underwent 3D-LGE-CMR imaging prior to their AF ablation. LA fibrotic burden was derived from the images using open-source CEMRG software and commercially available ADAS 3D-LA software. Both packages were used to calculate fibrosis based on the image intensity ratio (IIR)-method. Additionally, CEMRG was used to quantify LA fibrosis using three standard deviations (3SD) above the mean blood pool signal intensity. Intraclass correlation coefficients were calculated to compare LA fibrosis quantification methods and different post-processing software outputs. The percentage of LA fibrosis assessed using IIR threshold 1.2 was significantly different from the 3SD-method (29.80 ± 14.15% vs. 8.43 ± 5.42%; P < 0.001). Correlation between the IIR-and SD-method was good (r = 0.85, P < 0.001) although agreement was poor [intraclass correlation coefficient (ICC) = 0.19; P < 0.001]. One-third of the patients were allocated to a different fibrosis category dependent on the used quantification method. Fibrosis assessment using CEMRG and ADAS 3D-LA showed good agreement for the IIR-method (ICC = 0.93; P < 0.001). CONCLUSIONS: Both, the IIR1.2 and 3SD-method quantify atrial fibrotic burden based on atrial wall signal intensity differences. The discrepancy in the amount of LA fibrosis between these methods may have clinical implications when patients are classified according to their fibrotic burden. There was no difference in results between post-processing software packages to quantify LA fibrosis if an identical quantification method including the threshold was used.
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Authors: Nassir F Marrouche; David Wilber; Gerhard Hindricks; Pierre Jais; Nazem Akoum; Francis Marchlinski; Eugene Kholmovski; Nathan Burgon; Nan Hu; Lluis Mont; Thomas Deneke; Mattias Duytschaever; Thomas Neumann; Moussa Mansour; Christian Mahnkopf; Bengt Herweg; Emile Daoud; Erik Wissner; Paul Bansmann; Johannes Brachmann Journal: JAMA Date: 2014-02-05 Impact factor: 56.272
Authors: Eva M Benito; Alicia Carlosena-Remirez; Eduard Guasch; Susana Prat-González; Rosario J Perea; Rosa Figueras; Roger Borràs; David Andreu; Elena Arbelo; J Maria Tolosana; Felipe Bisbal; Josep Brugada; Antonio Berruezo; Lluis Mont Journal: Europace Date: 2017-08-01 Impact factor: 5.214
Authors: Orod Razeghi; José Alonso Solís-Lemus; Angela W C Lee; Rashed Karim; Cesare Corrado; Caroline H Roney; Adelaide de Vecchi; Steven A Niederer Journal: SoftwareX Date: 2020-07-31