Reproducibility of clinical late gadolinium enhancement magnetic resonance imaging in detecting left atrial scar after atrial fibrillation ablation. (28th September 2020) - Kamali, Roya; Schroeder, Joyce; DiBella, Edward; Steinberg, Benjamin; Han, Frederick; Dosdall, Derek J.; Macleod, Rob S.; Ranjan, Ravi
Abstract: Introduction: Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (MRI) can be used to detect postablation atrial scar (PAAS) but its reproducibility and reliability in clinical scans across different magnetic flux densities and scar detection methods are unknown. Methods: Patients ( n  = 45) having undergone two consecutive MRIs (3 months apart) on 3T and 1.5T scanners were studied. We compared PAAS detection reproducibility using four methods of thresholding: simple thresholding, Otsu thresholding, 3.3 standard deviations ( SD ) above blood pool (BP) mean intensity, and image intensity ratio (IIR). We performed a texture study by dividing the left atrial wall intensity histogram into deciles and evaluated the correlation of the same decile of the two scans as well as to a randomized distribution of intensities, quantified using Dice Similarity Coefficient (DSC). Results: The choice of scanner did not significantly affect the reproducibility. The scar detection performed by Otsu thresholding (DSC of 71.26 ± 8.34) resulted in a better correlation of the two scans compared with the methods of 3.3  SD above BP mean intensity (DSC of 57.78 ± 21.2, p  < .001) and IIR above 1.61 (DSC of 45.76 ± 29.55, p  <.001). Texture analysis showed that correlation only for voxels with intensities in deciles above the 70th percentile of wall intensity histogram was better than random distribution ( p  < .001). Conclusions: Our results demonstrate that clinical LGE‐MRI can be reliably used for visualizing PAAS across different magnetic flux densities if the threshold is greater than 70th percentile of the wall intensity distribution. Also, atrial wall‐based thresholding is better than BP‐based thresholding for reproducible PAAS detection.