MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

Aims:Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations. Methods:A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI). The ventricular model includes membrane kinetics represented by the biophysically detailed O’Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology,duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. Results:QRS morphologies in limb leads are mainly determined by ventricular anatomy,while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration ismainly influenced by myocardial volume, while it is hardly affected by the torso anatomyor position. An average increase of 0.12±0.05 ms in QRS duration is obtained for eachcm3of myocardial volume across all the leads while it hardly changed due to changes in torso volume. Conclusion:Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave theway toward their use as testbeds in silico clinical trial

Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation

The left atrium (LA) can change in size and shape due to atrial fibrillation (AF)-induced remodeling. These alterations can be linked to poorer outcomes of AF ablation. In this study, we propose a novel comprehensive computational analysis of LA anatomy to identify what features of LA shape can optimally predict post-ablation AF recurrence. To this end, we construct smooth 3D geometrical models from the segmentation of the LA blood pool captured in pre-procedural MR images. We first apply this methodology to characterize the LA anatomy of 144 AF patients and build a statistical shape model that includes the most salient variations in shape across this cohort. We then perform a discriminant analysis to optimally distinguish between recurrent and non-recurrent patients. From this analysis, we propose a new shape metric called vertical asymmetry, which measures the imbalance of size along the anterior to posterior direction between the superior and inferior left atrial hemispheres. Vertical asymmetry was found, in combination with LA sphericity, to be the best predictor of post-ablation recurrence at both 12 and 24 months (area under the ROC curve: 0.71 and 0.68, respectively) outperforming other shape markers and any of their combinations. We also found that model-derived shape metrics, such as the anterior-posterior radius, were better predictors than equivalent metrics taken directly from MRI or echocardiography, suggesting that the proposed approach leads to a reduction of the impact of data artifacts and noise. This novel methodology contributes to an improved characterization of LA organ remodeling and the reported findings have the potential to improve patient selection and risk stratification for catheter ablations in AF

Transferring facial expressions to different face models

We introduce a facial deformation system that helps the character setup process and gives artists the possibility to manipulate models as if they were using a puppet. The method uses a set of labels that define specific facial features and deforms the rig anthropometrically. We find the correspondence of the main attributes of a generic rig, transfer them to different 3D face models and automatically generate a sophisticated facial rig based on an anatomical structure. We show how the labels, combined with other deformation methods, can adapt muscles and skeletons from a generic rig to individual face models, allowing high quality physics-based animations. We describe how it is possible to deform the generic facial rig, apply the same deformation parameters to different face models and obtain unique expressions. We show how our method can easily be integrated in an animation pipeline. We end with different examples that show the strength of our proposal.Peer ReviewedPostprint (published version

Transferring facial expressions to different face models

We introduce a facial deformation system that helps the character setup process and gives artists the possibility to manipulate models as if they were using a puppet. The method uses a set of labels that define specific facial features and deforms the rig anthropometrically. We find the correspondence of the main attributes of a generic rig, transfer them to different 3D face models and automatically generate a sophisticated facial rig based on an anatomical structure. We show how the labels, combined with other deformation methods, can adapt muscles and skeletons from a generic rig to individual face models, allowing high quality physics-based animations. We describe how it is possible to deform the generic facial rig, apply the same deformation parameters to different face models and obtain unique expressions. We show how our method can easily be integrated in an animation pipeline. We end with different examples that show the strength of our proposal.Peer Reviewe

Automated 3D whole-heart mesh reconstruction from 2D cine MR slices using statistical shape model

Cardiac magnetic resonance (CMR) imaging is the one of the gold standard imaging modalities for the diagnosis and characterization of cardiovascular diseases. The clinical cine protocol of the CMR typically generates high-resolution 2D images of heart tissues in a finite number of separated and independent 2D planes, which are appropriate for the 3D reconstruction of biventricular heart surfaces. However, they are usually inadequate for the whole-heart reconstruction, specifically for both atria. In this regard, the paper presents a novel approach for automated patient-specific 3D whole-heart mesh reconstruction from limited number of 2D cine CMR slices with the help of a statistical shape model (SSM). After extracting the heart contours from 2D cine slices, the SSM is first optimally fitted over the sparse heart contours in 3D space to provide the initial representation of the 3D whole-heart mesh, which is further deformed to minimize the distance from the heart contours for generating the final reconstructed mesh. The reconstruction performance of the proposed approach is evaluated on a cohort of 30 subjects randomly selected from the UK Biobank study, demonstrating the generation of high-quality 3D whole-heart meshes with average contours to surface distance less than the underlying image resolution and the clinical metrics within acceptable ranges reported in previous literature. Clinical Relevance- Automated patient-specific 3D whole-heart mesh reconstruction has numerous applications in car-diac diagnosis and multimodal visualization, including treatment planning, virtual surgery, and biomedical simulations

AV@CAR:A Spanish multichannel multimodal corpus for in-vehicle automatic audio-visual speech recognition

This paper describes the acquisition of the multichannel multimodal database AV@CAR for automatic audio-visual speech recognition in cars. Automatic speech recognition (ASR) plays an important role inside vehicles to keep the driver away from distraction. It is also known that visual information (lip-reading) can improve accuracy in ASR under adverse conditions as those within a car. The corpus described here is intended to provide training and testing material for several classes of audiovisual speech recognizers including isolated word system, word-spotting systems, vocabulary independent systems, and speaker dependent or speaker independent systems for a wide range of applications. The audio database is composed of seven audio channels including, clean speech (captured using a close talk microphone), noisy speech from several microphones placed on the overhead of the cabin, noise only signal coming from the engine compartment and information about the speed of the car. For the video database, a small video camera sensible to the visible and the near infrared bands is placed on the windscreen and used to capture the face of the driver. This is done under different light conditions both during the day and at night. Additionally, the same individuals are recorded in laboratory, under controlled environment conditions to obtain noise free speech signals, 2D images and 3D + texture face models. 1