A Computational Framework to Benchmark Basket Catheter Guided Ablation in Atrial Fibrillation.

Catheter ablation is a curative therapeutic approach for atrial fibrillation (AF). Ablation of rotational sources based on basket catheter measurements has been proposed as a promising approach in patients with persistent AF to complement pulmonary vein isolation. However, clinically reported success rates are equivocal calling for a mechanistic investigation under controlled conditions. We present a computational framework to benchmark ablation strategies considering the whole cycle from excitation propagation to electrogram acquisition and processing to virtual therapy. Fibrillation was induced in a patient-specific 3D volumetric model of the left atrium, which was homogeneously remodeled to sustain reentry. The resulting extracellular potential field was sampled using models of grid catheters as well as realistically deformed basket catheters considering the specific atrial anatomy. The virtual electrograms were processed to compute phase singularity density maps to target rotor tips with up to three circular ablations. Stable rotors were successfully induced in different regions of the homogeneously remodeled atrium showing that rotors are not constrained to unique anatomical structures or locations. Density maps of rotor tip trajectories correctly identified and located the rotors (deviation < 10 mm) based on catheter recordings only for sufficient resolution (inter-electrode distance ≤3 mm) and proximity to the wall (≤10 mm). Targeting rotor sites with ablation did not stop reentries in the homogeneously remodeled atria independent from lesion size (1-7 mm radius), from linearly connecting lesions with anatomical obstacles, and from the number of rotors targeted sequentially (≤3). Our results show that phase maps derived from intracardiac electrograms can be a powerful tool to map atrial activation patterns, yet they can also be misleading due to inaccurate localization of the rotor tip depending on electrode resolution and distance to the wall. This should be considered to avoid ablating regions that are in fact free of rotor sources of AF. In our experience, ablation of rotor sites was not successful to stop fibrillation. Our comprehensive simulation framework provides the means to holistically benchmark ablation strategies in silico under consideration of all steps involved in electrogram-based therapy and, in future, could be used to study more heterogeneously remodeled disease states as well.

Towards a repository of synthetic electrograms for atrial activation detection in atrial fibrillation.

BACKGROUND: Recently, the analysis of the spatio-temporal behavior of atrial fibrillation activation patterns has been widely investigated with the aim to better understand the arrhythmia implications on the heart electrical activity. Most of the proposed techniques are based on atrial activation timing detections. Unfortunately atrial activation timings are not easily recognizable on the electrograms (EGMs) and an approach to support the validation of such techniques is highly desirable. The aim of this study is to provide an effective workflow for the generation of synthetic unipolar atrial electrograms (SEGMs) in atrial fibrillation (AF) condition and with different levels of noise. METHOD: Real EGMs signals were obtained from a dataset of 6 subjects that underwent ablation. Each SEGM was obtained by modeling the three principal components of an EGM starting from real signals: atrial far-field (Afar), atrial near-field (Anear) and the ventricular far-field (Vfar). Afar was generated using an autoregressive model applied on segments from real EGMs not characterized by ventricular or atrial activations; Anear and Vfar were extracted directly from the real signals. A Gamma distribution and an atrio-ventricular node model were used to locate both Anear and Vfar on Afar, respectively. Three electrophysiologists with different levels of expertise evaluated the realism of the SEGMs on a set of 100 randomly selected signals including 50 EGMs and 50 SEGMs. Analysis was repeated by the three experts on a subset of 21 signals. RESULTS: The time required to generate the synthetic EGMs was less than 1 min once annotated EGMs are available. The cardiologists succeeded in distinguishing real from synthetic EGMs in 45%, 43% and 35% of the signals, respectively. By repeating the evaluation, 28%, 0% and 48% of signals were classified differently, including 67%, 52% and 36% of correct classifications. CONCLUSION: The proposed approach proved to be effective in producing SEGMs which are difficult to distinguish from real EGMs. This study provides a tool for realistic SEGM generation from real EGMs in AF condition with different levels of noise and at different AF rates. The tool may be easily adopted to obtain SEGMs in different arrhythmic conditions. SEGMs generated in this study are shared with the scientific community as a first step towards a repository of synthetic and real atrial signals supporting the benchmarking of new approaches to investigate AF.

Fully Automated Segmentation of Polycystic Kidneys From Noncontrast Computed Tomography: A Feasibility Study and Preliminary Results.

RATIONALE AND OBJECTIVES: Total kidney volume is an important biomarker for the evaluation of autosomal dominant polycystic kidney disease progression. In this study, we present a novel approach for automated segmentation of polycystic kidneys from non-contrast-enhanced computed tomography (CT) images. MATERIALS AND METHODS: Non-contrast-enhanced CT images were acquired from 21 patients with a diagnosis of autosomal dominant polycystic kidney disease. Kidney volumes obtained from the fully automated method were compared to volumes obtained by manual segmentation and evaluated using linear regression and Bland-Altman analyses. Dice coefficient was used for performance evaluation. RESULTS: Kidney volumes from the automated method well correlated with the ones obtained by manual segmentation. Bland-Altman analysis showed a low percentage bias (-0.3%) and narrow limits of agreements (11.0%). The overlap between the three-dimensional kidney surfaces obtained with our approach and by manual tracing, expressed in terms of Dice coefficient, showed good agreement (0.91 ± 0.02). CONCLUSIONS: This preliminary study showed the proposed fully automated method for renal volume assessment is feasible, exhibiting how a correct use of biomedical image processing may allow polycystic kidney segmentation also in non-contrast-enhanced CT. Further investigation on a larger dataset is needed to confirm the robustness of the presented approach.

3D patient-specific models for left atrium characterization to support ablation in atrial fibrillation patients.

BACKGROUND: Radiofrequency ablation (RFA) is an important and promising therapy for atrial fibrillation (AF) patients. Optimization of patient selection and the availability of an accurate anatomical guide could improve RFA success rate. In this study we propose a unified, fully automated approach to build a 3D patient-specific left atrium (LA) model including pulmonary veins (PVs) in order to provide an accurate anatomical guide during RFA and without PVs in order to characterize LA volumetry and support patient selection for AF ablation. METHODS: Magnetic resonance data from twenty-six patients referred for AF RFA were processed applying an edge-based level set approach guided by a phase-based edge detector to obtain the 3D LA model with PVs. An automated technique based on the shape diameter function was designed and applied to remove PVs and compute LA volume. 3D LA models were qualitatively compared with 3D LA surfaces acquired during the ablation procedure. An expert radiologist manually traced the LA on MR images twice. LA surfaces from the automatic approach and manual tracing were compared by mean surface-to-surface distance. In addition, LA volumes were compared with volumes from manual segmentation by linear and Bland-Altman analyses. RESULTS: Qualitative comparison of 3D LA models showed several inaccuracies, in particular PVs reconstruction was not accurate and left atrial appendage was missing in the model obtained during RFA procedure. LA surfaces were very similar (mean surface-to-surface distance: 2.3±0.7mm). LA volumes were in excellent agreement (y=1.03x-1.4, r=0.99, bias=-1.37ml (-1.43%) SD=2.16ml (2.3%), mean percentage difference=1.3%±2.1%). CONCLUSIONS: Results showed the proposed 3D patient-specific LA model with PVs is able to better describe LA anatomy compared to models derived from the navigation system, thus potentially improving electrograms and voltage information location and reducing fluoroscopic time during RFA. Quantitative assessment of LA volume derived from our 3D LA model without PVs is also accurate and may provide important information for patient selection for RFA.

Analysis of the electrical patterns and structural remodeling in atrial fibrillation.

Catheter ablation of atrial fibrillation (AF) is a promising therapy, whose success is limited by uncertainty in the knowledge of the mechanisms sustaining the arrhythmia. Many theories based on atrial electrical activation or on atrial structural remodeling have been proposed to target AF mechanisms. We hypothesized two prospective approaches could be linked and both computational analysis of atrial electrical patterns and fibrotic tissue location and extent could give further insights on the role of rotors and spatial relationship between them and atrial fibrosis. This paper presents some preliminary results aimed at the integration of information derived from electrical patterns and structural remodeling in AF patients. Electrical patterns were analyzed by applying the standard procedure based on the Hilbert transform (HT) and with sinusoidal wavelet recomposition (SR). In addition, a new technique based on the detection of maximum negative derivative of the unipolar electrograms and a modified version of signal recomposition (NDSR) was tested.A patient-specific anatomical model was derived by segmenting magnetic resonance angiographic (MRA) data applying an edge based level set approach guided by a phase-based edge detector. A multimodality affine registration was applied to register MRA and delayed-enhanced MR imaging (DE-MRI). Following this registration step, gray intensity levels from DE-MRI were used asa texture of the 3D model to visualize fibrosis location and quantify its extent.In view of a future integration of electrical activation patterns onthe patient-specific anatomical model, detected atrial activation timings (AAT) and derived parameters were validated with manual annotation performed by an expert cardiologist and the atrial model was compared with the anatomical map used to guide the ablation procedure.