Background While 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 magnetic resonance imaging (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 AF patients without contraindications to MRI and before undergoing their first ablation procedure and on 21 non-AF control subjects. Patients were categorized by SRM stage (I–IV) based on percentage of LA wall enhancement for correlation with procedure outcomes. Histological validation of SRM was performed comparing LGE-MRI to surgical biopsy. A total of 386 patients (91%) with adequate LGE-MRI scans were included in the study. Post-ablation, 123 (31.9%) experienced recurrent atrial arrhythmias over one-year follow-up. Recurrent arrhythmias (failed ablations) occurred at higher SRM stages with 28/133 (21.0%) stage I, 40/140 (29.3%) stage II, 24/71 (33.8%) stage III, and 30/42 (71.4%) stage IV. In multi-variate analysis, ablation outcome was best predicted by advanced SRM stage (hazard ratio (HR) 4.89; p<0.0001) and diabetes (HR 1.64; p=0.036) while increased LA volume and persistent AF were not significant predictors. LA wall enhancement was significantly greater in AF patients vs. non-AF controls (16.6±11.2% vs. 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.

BackgroundLate 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.MethodsThe 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.ResultsSome 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.ConclusionsThe 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.

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.

This course introduces attendees to select open-source efforts in the field of medical image analysis. Opportunities for users and developers are presented. The course particularly focuses on the open-source Insight Toolkit (ITK) for medical image segmentation and registration. The course describes the procedure for downloading and installing the toolkit and covers the use of its data representation and filtering classes. Attendees are shown how ITK can be used in their research, rapid prototyping, and application development.LEARNING OUTCOMES After completing this course, attendees will be able to: contribute to and benefit from open-source software for medical image analysis download and install the ITK toolkit start their own software project based on ITK design and construct an image processing pipeline combine ITK filters for medical image segmentation combine ITK components for medical image registrationINTENDED AUDIENCE This course is intended for anyone involved in medical image analysis. In particular it targets graduate students, researchers and professionals in the areas of computer science and medicine. Attendees should have an intermediate level on object oriented programming with C++ and must be familiar with the basics of medical image processing and analysis.