AugmentA: Patient-specific Augmented Atrial model Generation Tool

Abstract

Digital twins of patients’ hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. A pipeline to standardize the generation of a patient’s atrial digital twin from clinical data is therefore desirable. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA consists firstly of a preprocessing step applied to the input geometry. Secondly, the atrial orifices are identified and labelled using only one reference point per atrium. If the user chooses to fit a statistical shape model (SSM) to the input geometry, it is first rigidly aligned with the given mean shape before a nonrigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4±5.7 s. The error between insilico and clinical LAT maps was on average 12.7 ms. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.

Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence

“…The only user interaction needed for the whole pipeline is the choice of one reference point per atrium. 17 The input can be atrial surfaces obtained from an electro-anatomical mapping system or derived from tomographic segmentation (e.g. MRI or CT).…”

“…Novel frameworks enabled the assessment of initiation, maintenance, and progression of AF via patient-specific computational modelling. 16–20 Nowadays, virtual replicas of human atria can be generated integrating information from multiple clinical data in an automated manner, 17 becoming a useful tool for clinicians in therapy planning. 20 , 21 …”

Comparison of Propagation Models and Forward Calculation Methods on Cellular, Tissue and Organ Scale Atrial Electrophysiology

“…These were smoothed and resampled with an average edge length of 0.5 mm, 5 mm, 5 mm, 7 mm and 15 mm, respectively, using Meshmixer (Autodesk, San Rafael, CA, USA) and InstantMeshes [13] whereby details were corrected manually in Blender (Blender Foundation, Amsterdam, The Netherlands) to avoid intersecting segments and ensure a sufficient mesh quality and topology. The segmented atrial endocardial surfaces were fed into the pipeline described in [10], [14], [15] to obtain a volumetric tetrahedral bi-atrial geometry with a homogeneous wall thickness of 3 mm and an average edge length of 523 µm augmented with inter-atrial connections, labels for anatomical structures and myocardial fiber orientation. In contrast to fully personalized approaches where fiber orientation can be defined based on information extracted from diffusion tensor imaging data, we defined myocardial fiber architecture in a rule-based way as described in [14] building on the solution of Laplace's equation [16], [17].…”

“…The segmented atrial endocardial surfaces were fed into the pipeline described in [10], [14], [15] to obtain a volumetric tetrahedral bi-atrial geometry with a homogeneous wall thickness of 3 mm and an average edge length of 523 µm augmented with inter-atrial connections, labels for anatomical structures and myocardial fiber orientation. In contrast to fully personalized approaches where fiber orientation can be defined based on information extracted from diffusion tensor imaging data, we defined myocardial fiber architecture in a rule-based way as described in [14] building on the solution of Laplace's equation [16], [17]. Meshtool [18] was used to generate a tetrahedral model of the full torso while preserving the surfaces of the considered organs.…”

How synergy between mechanistic and statistical models is impacting research in atrial fibrillation

“…AugmentA consists of a preprocessing step ( Azzolin et al, 2021a ), atrial orifices’ annotation, a statistical shape model fitting procedure, fiber generation ( Zheng et al, 2021 ) and conduction velocity (CV) estimation. AugmentA offers an automated and comprehensive pipeline delivering personalized atrial computer models from clinical data in procedural time ( Azzolin et al, 2022a ). This is a step forward toward standardized assessment of arrhythmia vulnerability and testing of ablation strategies.…”