National Yang Ming Chiao Tung University

Other

National Central University

Taipei Veterans General Hospital

Yenn-Jiang Lin

Shih-Ann Chen

Men-Tzung Lo

Yi-Chung Chang

Chen Lin

Chih-Min Liu

Norden E Huang

Wan-Hsin Hsieh

Hsiang-Chih Chang

Horng-Shing Lu

Wei-Shiang Chen

Fa-Po Chung

Ke-Hsin Hsu

Hung-Yi Lee

Shih-Lin Chang

Huan-Yu Kuo

Hung-Hsun Chen

The present disclosure relates to methods, apparatuses, and computer programs for processing computed tomography images. Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create a three-dimensional (3D) geometries. The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry. Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post atrial fibrillation (AF) ablation. Elimination of NPV triggers can reduce the post-ablation AF recurrence. The deep learning was applied in pre-ablation pulmonary vein computed tomography (PVCT) geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation (PAF). The deep learning model using pre-ablation PVCT can be applied to predict the trigger origins in PAF patients receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.

Methods, apparatuses, and computer programs for processing pulmonary vein computed tomography images

A computer-assisted method for quantitatively characterizing atrial fibrillation in a patient includes recording time series of bipolar atrial fibrillation signals at multiple sites in a patient's atria using two or more electrodes, calculating a similarity index vector by a computer system based on the bipolar atrial fibrillation signal between a first site and its neighboring sites, constructing an similarity-index vector field based on similarity-index vectors at different sites, calculating Curl and Divergence of the similarity-index vector field, calculating Rotor Identification using Curl and Divergence, calculating Focal Identification using Divergence, and determining one or more critical regions in the patient's atria if Rotor Identification is above a first predetermined threshold and Focal Identification is above a second predetermined threshold.

Accurately indetifying critcal regions in atrial fibrillation by identifying rotors in directional similarity vector field

A computer-assisted method for quantitative characterizing atrial fibrillation (AF) in a patient includes recording unipolar atrial fibrillation signals from multiple sites in a patient's atria, calculating bipolar electrograms using unipolar AF signals recorded at adjacent sites by a computer system, applying Empirical Mode Decomposition to remove a background from the bipolar electrogram signal to obtain a filtered bipolar electrogram signal, applying Hilbert transform to an envelope function of the filtered bipolar electrogram signal to obtain a time series of instantaneous phases of the filtered bipolar electrogram signal, and identifying a rotor region in patient's atria using the instantaneous phases in the filtered bipolar electrogram signal.

System and method for indentifying rotors in fractionated signals in persistent atrial fibrillation ablation

A computer-assisted method for quantitative characterization and treatment of ventricular fibrillation includes preprocessing a time series of an atrial fibrillation signal obtained from a patient, segmenting the time series of the AF signal into activation segments by the computer system, obtaining local activation waveforms (LAW) from the activation segments, determining degrees of similarity between the LAWs, and identifying one or more critical regions in the patient's atria if the LAWs have degrees of similarity exceeding a first threshold value.

System and method for improved complex fractionated electrogram ablation

Growing community of inventors

Taipei, Taiwan

Yenn-Jiang Lin