Titlebook: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges; 7th International Wo Tommaso Mansi,Kristin McL

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elastic

Phase-Based Registration of Cardiac Tagged MR Images by Incorporating Anatomical Constraints, we found that the constraint improved both longitudinal and circumferential strains accuracies; (2) on 15 healthy volunteers, the proposed method achieved better tracking accuracy compared to three state-of-the-art methods; (3) on one patient dataset, we show that our method is able to distinguish the infarcted segments from the normal ones.

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Image-Based Real-Time Motion Gating of 3D Cardiac Ultrasound Imagesd gating of live images. We have developed a novel and potentially clinically useful real-time three-dimensional (3D) cardiac motion gating technique that facilitates and supports 3D US-guided procedures. Our proposed real-time 3D-Masked-PCA technique uses the Principal Component Analysis (PCA) stat

EXPEL

Novel Framework to Integrate Real-Time MR-Guided EP Data with T1 Mapping-Based Computational Heart Mially lethal scar-related arrhythmia. Furthermore, although cardiac MR can provide excellent structural information (i.e., anatomy and scar), these catheter-based procedures have limited electrical information due to sparse electrical maps recorded from endocardial surfaces. In this paper, we propos

入伍儀式

Left Atrial Appendage Segmentation Based on Ranking 2-D Segmentation Proposalsy help doctors diagnose thrombosis and plan LAA closure surgery. Considering large anatomical variations of the LAA, we present a non-model based semi-automated approach for LAA segmentation on CTA data. The method requires only manual selection of four fiducial points to obtain the bounding box for

Brocas-Area

Induction

Phase-Based Registration of Cardiac Tagged MR Images by Incorporating Anatomical Constraintsroving the motion/strain quantification from tagged MR images. The “tag number constant” concept used in Gabor-based non-tracking methods is integrated into a recent phase-based registration framework. We evaluated our method on both synthetic and real data: (1) on a synthetic data of a normal heart

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