Maddalena Valinoti

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.

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

Atrial fibrillation (AF) ablation guided by basket catheter mapping has shown to be beneficial. Yet, the initial excitement is mitigated by a growing skepticism due to the difficulty in verifying the protocol in multicenter studies. Overall, the underlying assumptions of rotor ablation require further verification. The aim of this study was therefore to test such hypotheses by using computational modeling. The 3D left atrial geometry of an AF patient was segmented from a pre-operative MR scan. Atrial activation was simulated on the 3D anatomy using the monodomain approach and a variant of the Courtemanche action potential model. Ablated tissue was assigned zero conductivity. Reentry was successfully initialized by applying a single suitably delayed extra stimulus. Unipolar electrograms were computed at the simulated electrode positions. The final dataset was generated by varying location of reentry and catheter position within the LA. The effect of inter-electrode distance and distance to the atrial wall was studied in relation to the ability to recover rotor trajectory, as computed by a novel algorithm described here. The effect of rotor ablation was also assessed.

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.

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.

Improved detection of activation timings in endoatrial electrograms through a modified sinusoidal recomposition method

Atrial fibrillation (AF) is the most common type of arrhythmia and the mechanisms that sustain it are not yet clearly identified. To target AF mechanisms, many theories on atrial electrical activation have been proposed. Since phase distribution of electrogram (EGM) changes over time is less affected by noise than EGM amplitude, phase analysis is one of the most robust method for identifying and quantifying spatiotemporal organization of fibrillation. In this paper we propose a new phase-based technique to detect atrial activation timings (AATs) and compared its performance versus manual annotation of AATs annotated by an expert cardiologist and versus classical methods based on Hilbert transform and sinusoidal recomposition. Detection of AATs from EGM signals in sinus rhythm applying the proposed technique overcomes the performance of classical methods and set the basis for its application on EGM in AF condition and phase map reconstruction.