Search

Reproducibility of Post-ablation atrial scar imaging- Supplementary Data. (DOCX 889 kb)

We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT). The work of S. Sanchez‐Martinez was supported by a fellowship from ‘la Caixa’ Banking Foundation. C. Butakoff was supported by a grant from the Fundació La Marató de TV3 (n. 20154031), Spain. N. Duchateau was supported by ‘Programme Avenir Lyon Saint‐Etienne’ (PALSE‐IMPULSION‐2016, Lyon, France). MADIT‐CRT was sponsored by Boston Scientific, while no additional funding was provided for this analysis. This study was also partially supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2014‐52923‐R; Maria de Maeztu Units of Excellence Programme ‐ MDM‐2015‐0502) and FEDER.

Reproducibility of Post-ablation atrial scar imaging- Supplementary Data. (DOCX 889 kb)

Many aspects related to the pathogenesis and progression of atrial fibrillation (AF) are still not fully understood. In this thesis we have contributed with (1) an automatic framework to segment the left atrium (LA) from late gadolinium enhancement magnetic resonance imaging (LGE-MRI) data, (2) a framework for obtaining a standardised two-dimensional representation of the left atrial cavity, and (3) a methodology to detect and measure the incompleteness of ablation lesions after pulmonary vein isolation (PVI). Finally, we applied our tools to real clinical datasets showing how our methods can be transferred and included into clinical research practice. We investigated the regional distribution of gaps in a cohort of AF patients who undergone radiofrequency PVI, the reproducibility of scar imaging with LGE-MRI, the intra- and inter-observer variability of manual LA segmentation tools, the preferential regional distribution of fibrosis in AF patients, and the relation between electroanatomical information and LGE-MRI data. Muchos aspectos relacionados con la patogénesis y progresión de la fibrilación auricular (FA) son todavía desconocidos. En esta tesis, hemos propuesto (1) un método para segmentar la aurícula izquierda (AI) en datos de resonancia magnética con realce tardío por gadolinio (LGE-MRI), (2) un framework para obtener una representación bidimensional estandarizada de la aurícula izquierda, y (3) una técnica para detectar y medir la incompletitud (gaps) de las lesiones de ablación después del aislamiento de las venas pulmonares (PVI). Finalmente, hemos aplicado nuestras herramientas a datos clínicos reales mostrando cómo nuestros métodos pueden transferirse e incluirse en investigación clínica. Concretamente, hemos investigado la distribución regional de gaps en pacientes con FA, la reproducibilidad de detección de cicatriz con LGE-MRI, la variabilidad intra e inter-observador de las herramientas de segmentación manual de AI, la distribución regional de fibrosis en pacientes con FA, y la relación entre la información electroanatómica y LGE-MRI.