MRI Assessment of Ablation-Induced Scarring in Atrial Fibrillation: Analysis from the DECAAF Study.

BACKGROUND: There is limited knowledge on the extent and location of scarring that results from catheter ablation and its role in suppressing atrial fibrillation (AF). We examined the effect of atrial fibrosis and ablation-induced scarring on catheter ablation outcomes in AF. METHODS: We conducted a prospective multicenter study that enrolled 329 AF patients presenting for catheter ablation. Delayed enhancement magnetic resonance imaging (DE-MRI) of the left atrium was obtained preablation. Scarring was evaluated in 177 patients with a DE-MRI scan obtained 90 days postablation. We evaluated residual fibrosis, defined as preablation atrial fibrosis not covered by ablation scar. The primary outcome was freedom from recurrent atrial arrhythmia. RESULTS: In the analysis cohort of 177 patients, preablation fibrosis was 18.7 ± 8.7% of the atrial wall. Ablation aimed at pulmonary vein (PV) isolation was performed in 163 patients (92.1%). Ablation-induced scar averaged 10.6 ± 4.4% of the atrial wall. Scarring completely encircled all 4 PVs only in 12 patients (7.3%). Residual fibrosis was calculated at 15.8 ± 8.0%. At 325 days follow-up, 35% of patients experienced recurrent arrhythmia. Multivariable Cox proportional hazards models demonstrated that baseline atrial fibrosis (HR and 95% CIs) (1.09 [1.06-1.12], P < 0.001) and residual fibrosis (1.09 [1.05-1.13], P < 0.001) were associated with atrial arrhythmia recurrence, while PV encirclement and overall scar were not. CONCLUSIONS: Catheter ablation of AF targeting PVs rarely achieves permanent encircling scar in the intended areas. Overall atrial fibrosis present at baseline and residual fibrosis uncovered by ablation scar are associated with recurrent arrhythmia.

Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge.

BACKGROUND: Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. METHODS: The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King's College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. RESULTS: Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. CONCLUSIONS: The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.

Entropy-based particle correspondence for shape populations.

PURPOSE: Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies. METHODS: In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics. RESULTS: We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques. CONCLUSIONS: Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.

Substrate Modification Is a Better Predictor of Catheter Ablation Success in Atrial Fibrillation Than Pulmonary Vein Isolation: An LGE-MRI Study.

OBJECTIVES: Outcomes of catheter ablation of atrial fibrillation (AF) vary widely. We used late-gadolinium enhancement MRI (LGE-MRI) to examine the relationship of ablation-induced scarring in the pulmonary vein (PV) region and overall atrium to evaluate the role of PV encirclement and substrate modification in predicting outcome of catheter ablation in AF. METHODS AND RESULTS: LGE-MRI was performed to quantify baseline atrial fibrosis, which was classified into four stages (stage I with fibrosis <10%, stage II with fibrosis 10-20%, stage III with fibrosis 20-30%, and stage IV with fibrosis ≥30%). Patients then underwent ablation and repeat LGE-MRI at three months to assess for ablation-induced scarring. PVs were studied to evaluate for complete encirclement with scar. Image processing was used to overlay the scar onto baseline fibrosis to assess the overlap and calculate residual fibrosis. A total of 172 patients were included with an average baseline fibrosis of 14.6 ± 8.4%. The average number of PVs encircled with scar at three months was 1.2 ± 1.3 with only 9% of patients having all four PVs completely encircled. The average residual fibrosis was 11.9 ± 7.3%. High residual fibrosis was defined as >10%. Recurrent AF was found in 60 patients (34.9%) over a follow-up of 346 ± 82 days. Baseline and high residual fibrosis were significant predictors of recurrence (hazard ratio [HR] of 2.2; P < 0.01 and HR of 3.2; P < 0.01, respectively). The number of PV encircled was not a significant predictor of recurrence. CONCLUSION: LGE-MRI of ablation-induced scarring demonstrates that chronic PV encirclement is rarely achieved. Procedural outcomes are better predicted by baseline atrial fibrosis and ablation-induced substrate modification.

Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI.

BACKGROUND: Although catheter ablation therapy for atrial fibrillation (AF) is becoming more common, results vary widely, and patient selection criteria remain poorly defined. We hypothesized that late gadolinium enhancement MRI (LGE-MRI) can identify left atrial (LA) wall structural remodeling (SRM) and stratify patients who are likely or not to benefit from ablation therapy. METHODS AND RESULTS: LGE-MRI was performed on 426 consecutive patients with AF without contraindications to MRI before undergoing their first ablation procedure and on 21 non-AF control subjects. Patients were categorized by SRM stage (I-IV) based on the percentage of LA wall enhancement for correlation with procedure outcomes. Histological validation of SRM was performed comparing LGE-MRI with surgical biopsy. A total of 386 patients (91%) with adequate LGE-MRI scans were included in the study. After ablation, 123 patients (31.9%) experienced recurrent atrial arrhythmias during the 1-year follow-up. Recurrent arrhythmias (failed ablations) occurred at higher SRM stages with 28 of 133 (21.0%) in stage I, 40 of 140 (29.3%) in stage II, 24 of 71 (33.8%) in stage III, and 30 of 42 (71.4%) in stage IV. In multivariate analysis, ablation outcome was best predicted by advanced SRM stage (hazard ratio, 4.89; P<0.0001) and diabetes mellitus (hazard ratio, 1.64; P=0.036), whereas increased LA volume and persistent AF were not significant predictors. LA wall enhancement was significantly greater in patients with AF versus non-AF controls (16.6±11.2% versus 3.1±1.9%; P<0.0001). Histological evidence of remodeling from surgical biopsy specimens correlated with SRM on LGE-MRI. CONCLUSIONS: Atrial SRM is identified on LGE-MRI, and extensive LGE (≥30% LA wall enhancement) predicts poor response to catheter ablation therapy for AF.

Combined SPHARM-PDM and entropy-based particle systems shape analysis framework.

DESCRIPTION OF PURPOSE: The NA-MIC SPHARM-PDM Toolbox represents an automated set of tools for the computation of 3D structural statistical shape analysis. SPHARM-PDM solves the correspondence problem by defining a first order ellipsoid aligned, uniform spherical parameterization for each object with correspondence established at equivalently parameterized points. However, SPHARM correspondence has shown to be inadequate for some biological shapes that are not well described by a uniform spherical parameterization. Entropy-based particle systems compute correspondence by representing surfaces as discrete point sets that does not rely on any inherent parameterization. However, they are sensitive to initialization and have little ability to recover from initial errors. By combining both methodologies we compute reliable correspondences in topologically challenging biological shapes. DATA: Diverse subcortical structures cohorts were used, obtained from MR brain images. METHODS: The SPHARM-PDM shape analysis toolbox was used to compute point based correspondent models that were then used as initializing particles for the entropy-based particle systems. The combined framework was implemented as a stand-alone Slicer3 module, which works as an end-to-end shape analysis module. RESULTS: The combined SPHARM-PDM-Particle framework has demonstrated to improve correspondence in the example dataset over the conventional SPHARM-PDM toolbox. CONCLUSIONS: The work presented in this paper demonstrates a two-sided improvement for the scientific community, being able to 1) find good correspondences among spherically topological shapes, that can be used in many morphometry studies 2) offer an end-to-end solution that will facilitate the access to shape analysis framework to users without computer expertise.

Cortical correspondence with probabilistic fiber connectivity.

This paper presents a novel method of optimizing point-based correspondence among populations of human cortical surfaces by combining structural cues with probabilistic connectivity maps. The proposed method establishes a tradeoff between an even sampling of the cortical surfaces (a low surface entropy) and the similarity of corresponding points across the population (a low ensemble entropy). The similarity metric, however, isn't constrained to be just spatial proximity, but uses local sulcal depth measurements as well as probabilistic connectivity maps, computed from DWI scans via a stochastic tractography algorithm, to enhance the correspondence definition. We propose a novel method for projecting this fiber connectivity information on the cortical surface, using a surface evolution technique. Our cortical correspondence method does not require a spherical parameterization. Experimental results are presented, showing improved correspondence quality demonstrated by a cortical thickness analysis, as compared to correspondence methods using spatial metrics as the sole correspondence criterion.