Dataset Information

Multimodal Examination of Atrial Fibrillation Substrate: Correlation of Left Atrial Bipolar Voltage Using Multi-Electrode Fast Automated Mapping, Point-by-Point Mapping, and Magnetic Resonance Image Intensity Ratio.

ABSTRACT: Background:Bipolar voltage mapping, as part of atrial fibrillation (AF) ablation, is traditionally performed in a point-by-point (PBP) approach using single-tip ablation catheters. Alternative techniques for fibrosis-delineation include fast-anatomical mapping (FAM) with multi-electrode circular catheters, and late gadolinium-enhanced magnetic-resonance imaging (LGE-MRI). The correlation between PBP, FAM, and LGE-MRI fibrosis assessment is unknown. Objective:In this study, we examined AF substrate using different modalities (PBP, FAM, and LGE-MRI mapping) in patients presenting for an AF ablation. Methods:LGE-MRI was performed pre-ablation in 26 patients (73% males, age 63±8years). Local image-intensity ratio (IIR) was used to normalize myocardial intensities. PBP- and FAM-voltage maps were acquired, in sinus rhythm, prior to ablation and co-registered to LGE-MRI. Results:Mean bipolar voltage for all 19,087 FAM voltage points was 0.88±1.27mV and average IIR was 1.08±0.18. In an adjusted mixed-effects model, each unit increase in local IIR was associated with 57% decrease in bipolar voltage (p<0.0001). IIR of >0.74 corresponded to bipolar voltage <0.5 mV. A total of 1554 PBP-mapping points were matched to the nearest FAM-point. In an adjusted mixed-effects model, log-FAM bipolar voltage was significantly associated with log-PBP bipolar voltage (ß=0.36, p<0.0001). At low-voltages, FAM-mapping distribution was shifted to the left compared to PBP-mapping; at intermediate voltages, FAM and PBP voltages were overlapping; and at high voltages, FAM exceeded PBP-voltages. Conclusion:LGE-MRI, FAM and PBP-mapping show good correlation in delineating electro-anatomical AF substrate. Each approach has fundamental technical characteristics, the awareness of which allows proper assessment of atrial fibrosis.

SUBMITTER: Zghaib T

PROVIDER: S-EPMC5836739 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Multimodal Examination of Atrial Fibrillation Substrate: Correlation of Left Atrial Bipolar Voltage Using Multi-Electrode Fast Automated Mapping, Point-by-Point Mapping, and Magnetic Resonance Image Intensity Ratio.

Zghaib Tarek T Keramati Ali A Chrispin Jonathan J Huang Dong D Balouch Muhammad A MA Ciuffo Luisa L Berger Ronald D RD Marine Joseph E JE Ashikaga Hiroshi H Calkins Hugh H Nazarian Saman S Spragg David D DD

JACC. Clinical electrophysiology 20171220 1

<h4>Background</h4>Bipolar voltage mapping, as part of atrial fibrillation (AF) ablation, is traditionally performed in a point-by-point (PBP) approach using single-tip ablation catheters. Alternative techniques for fibrosis-delineation include fast-anatomical mapping (FAM) with multi-electrode circular catheters, and late gadolinium-enhanced magnetic-resonance imaging (LGE-MRI). The correlation between PBP, FAM, and LGE-MRI fibrosis assessment is unknown.<h4>Objective</h4>In this study, we exam ...[more]

PMID: 29520376

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Project description:Background: Presence of left atrial low voltage substrate in bipolar voltage mapping is associated with increased arrhythmia recurrences following pulmonary vein isolation for atrial fibrillation (AF). Besides local myocardial fibrosis, bipolar voltage amplitudes may be influenced by inter-electrode spacing and bipole-to-wavefront-angle. It is unclear to what extent these impact low voltage areas (LVA) in the clinical setting. Alternatively, unipolar electrogram voltage is not affected by these factors but requires advanced filtering. Objectives: To assess the relationship between bipolar and unipolar voltage mapping in sinus rhythm (SR) and AF and identify if the electrogram recording mode affects the quantification and localization of LVA. Methods: Patients (n = 28, 66±7 years, 46% male, 82% persistent AF, 32% redo-procedures) underwent high-density (>1,200 sites, 20 ± 10 sites/cm2, using a 20-pole 2-6-2 mm-spaced Lasso) voltage mapping in SR and AF. Bipolar LVA were defined using four different thresholds described in literature: <0.5 and <1 mV in SR, <0.35 and <0.5 mV in AF. The optimal unipolar voltage threshold resulting in the highest agreement in both unipolar and bipolar mapping modes was determined. The impact of the inter-electrode distance (2 vs. 6 mm) on the correlation was assessed. Regional analysis was performed using an 11-segment left atrial model. Results: Patients had relevant bipolar LVA (23 ± 23 cm2 at <0.5 mV in SR and 42 ± 26 cm2 at <0.5 mV in AF). 90 ± 5% (in SR) and 85 ± 5% (AF) of mapped sites were concordantly classified as high or low voltage in both mapping modes. Discordant mapping sites located to the border zone of LVA. Bipolar voltage mapping using 2 vs. 6 mm inter-electrode distances increased the portion of matched mapping points by 4%. The unipolar thresholds (y) which resulted in a high spatial concordance can be calculated from the bipolar threshold (x) using following linear equations: y = 1.06x + 0.26mV (r = 0.994) for SR and y = 1.22x + 0.12mV (r = 0.998) for AF. Conclusion: Bipolar and unipolar voltage maps are highly correlated, in SR and AF. While bipole orientation and inter-electrode spacing are theoretical confounders, their impact is unlikely to be of clinical importance for localization of LVA, when mapping is performed at high density with a 20-polar Lasso catheter.

Project description:With the introduction of multi-camera systems in modern plant phenotyping new opportunities for combined multimodal image analysis emerge. Visible light (VIS), fluorescence (FLU) and near-infrared images enable scientists to study different plant traits based on optical appearance, biochemical composition and nutrition status. A straightforward analysis of high-throughput image data is hampered by a number of natural and technical factors including large variability of plant appearance, inhomogeneous illumination, shadows and reflections in the background regions. Consequently, automated segmentation of plant images represents a big challenge and often requires an extensive human-machine interaction. Combined analysis of different image modalities may enable automatisation of plant segmentation in "difficult" image modalities such as VIS images by utilising the results of segmentation of image modalities that exhibit higher contrast between plant and background, i.e. FLU images. For efficient segmentation and detection of diverse plant structures (i.e. leaf tips, flowers), image registration techniques based on feature point (FP) matching are of particular interest. However, finding reliable feature points and point pairs for differently structured plant species in multimodal images can be challenging. To address this task in a general manner, different feature point detectors should be considered. Here, a comparison of seven different feature point detectors for automated registration of VIS and FLU plant images is performed. Our experimental results show that straightforward image registration using FP detectors is prone to errors due to too large structural difference between FLU and VIS modalities. We show that structural image enhancement such as background filtering and edge image transformation significantly improves performance of FP algorithms. To overcome the limitations of single FP detectors, combination of different FP methods is suggested. We demonstrate application of our enhanced FP approach for automated registration of a large amount of FLU/VIS images of developing plant species acquired from high-throughput phenotyping experiments.

Project description:BackgroundAtrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.MethodsTemporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm2 resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning were extracted from each Fourier spectrum.ResultsTargeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P<0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together).ConclusionsThe machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Dataset Information

Multimodal Examination of Atrial Fibrillation Substrate: Correlation of Left Atrial Bipolar Voltage Using Multi-Electrode Fast Automated Mapping, Point-by-Point Mapping, and Magnetic Resonance Image Intensity Ratio.

Publications

Multimodal Examination of Atrial Fibrillation Substrate: Correlation of Left Atrial Bipolar Voltage Using Multi-Electrode Fast Automated Mapping, Point-by-Point Mapping, and Magnetic Resonance Image Intensity Ratio.

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