# Impact of LGE-MRI in Arrhythmia Ablation
## 3 Image Post-Processing (Pre-Procedural)  
### Image Processing: Segmentation and Fibrosis Detection  
Post-processing is necessary to acquire a 3D anatomical reconstruction of the chamber of interest and to identify, analyse and evaluate the scarring tissue. To acquire this 3D anatomical segmentation, there is a great deal of established open-source and commercial software for image post-processing. The two steps required to achieve this 3D anatomical structure are segmentation of the anatomical chamber (LA and/or RA and PPVV in the case of AF and LV and/or RV in the case of VT) and detection of fibrotic and scarring areas inside the segmented anatomical structure. 1. Segmentation of anatomical structures Accurate segmentation is required for scar analysis and fibrosis visualization. The segmentation process was performed manually. Clinicians or engineers segment the atrial wall or the myocardium manually: An accurate slice-by-slice 2D tracing of the LA wall and endocardial and epicardial myocardium to confine the region of interest (ROI) while avoiding anatomical structures (aortic ring, valves, papillary muscles, etc. ), the blood pool, fat, etc. Currently, the different segmentation software programs have semiautomatic tools available. Therefore, the manual process may be used after this automatic segmentation to refine the results. 2. Detection of fibrotic tissue Once the anatomy is properly segmented, the fibrotic regions (LGE) can be assessed qualitatively by visual assessment or quantitatively by using different thresholding techniques. To apply thresholding techniques, different approaches with different algorithms have been improved for the detection of arrhythmogenic areas. To date, there is limited reproducibility across centres because there is no single standardized method for LGE image analysis. Obtaining a consistent internal reference for normalization and validated signal intensity thresholds that can accurately differentiate between healthy and scar tissue are crucial for quantifying LGE. The reason is that T1-weighted imaging relies on signal intensity contrast instead of directly measured absolute values. Different methods have been validated for atrial fibrosis quantification (Table 1) [29; 30; 31; 43; 44; 45; 46; 47]. Each method uses distinct internal references and thresholds. As an internal reference, the mean signal intensity of the blood pool is used extensively by numerous groups. Our group has recently validated a method quantifying signal intensity ratios using the mean signal intensity of the left atrial (LA) blood pool as a reference (signal intensity of each given voxel/mean signal intensity of the blood) [29]. The atrium thresholds to characterize healthy myocardium (signal intensity ratio \(\leq\) 1.2) and fibrotic tissue (signal intensity ratio \(>\) 1.32) were derived from distinct cohorts including both young healthy individuals and post-AF ablation patients. Subsequently these cut-offs were verified in various studies in comparison with voltage mapping during ablation procedures and they were correlated with clinical endpoints [6; 48; 49] (Figure 1). The most interesting approaches to detect and quantify fibrosis in LGE-MRI of the left atrial wall are carefully benchmarked by [PERSON] et al. [2]. This review provides a critical analysis of the different methods to detect and quantify fibrosis in LGE-MRI, stating their advantages and limitations. Likewise, for ventricular fibrosis quantification, various thresholds and methods have been verified (Table 1) [50; 51; 52; 53; 54]. Table 1 summarizes the main studies defining thresholds for LV. The most commonly used approach is the full width at half maximum (FWHM), which is a fixed thresholding method in which a fixed intensity threshold is defined as half of the maximum intensity of a user-selected hyperenhanced region. Another method is to define remote \"healthy\" myocardial segments as an internal reference for normalization.