Ventricle Surface Reconstruction from Cardiac MR Slices Using Deep Learning

Reconstructing 3D ventricular surfaces from 2D cardiac MR data is challenging due to the sparsity of the input data and the presence of interslice misalignment. It is usually formulated as a 3D mesh fitting problem often incorporating shape priors and smoothness regularization, which might affect accuracy when handling pathological cases. We propose to formulate the 3D reconstruction as a volumetric mapping problem followed by isosurfacing from dense volumetric data. Taking advantage of deep learning algorithms, which learn to predict each voxel label without explicitly defining the shapes, our method is capable of generating anatomically meaningful surfaces with great flexibility. The sparse 3D volumetric input can process contours with any orientations and thus can utilize information from multiple short- and long-axis views. In addition, our method can provide correction of motion artifacts. We have validated our method using a statistical shape model on reconstructing 3D shapes from both spatially consistent and misaligned input data

Correction of Slice Misalignment in Multi-breath-hold Cardiac MRI Scans

Cardiac Magnetic Resonance (CMR) provides unique functional and anatomical visualisation of the macro and micro-structures of the heart. However, CMR acquisition times usually necessitate slices to be acquired at different breath holds, which results in potential misalignment of the acquired slices. Correcting for this spatial misalignment is required for accurate three-dimensional (3D) reconstruction of the heart chambers allowing robust metrics for shape analysis among populations as well as precise representations of individual geometries and scars. While several methods have been proposed to realign slices, their use in other important protocols such as late gadolinium enhancement (LGE) is yet to be demonstrated. We propose a registration framework based on local phase to correct for slice misalignment. Our registration framework is a group registration technique combining long- and short-axis slices. Validation was performed on LGE slices using expert-traced ventricular contours. For 15 clinical multi-breath-hold datasets our method reduced the median discrepancy of moderately misaligned slices from 2.19 mm to 1.63 mm, and of severely misaligned from 7.33 mm to 1.96 mm

Surface mesh reconstruction from cardiac MRI contours

We introduce a tool to build a surface mesh able to deal with sparse, heterogeneous, non-parallel, cross-sectional, non-coincidental contours and show its application to reconstruct surfaces of the heart. In recent years, much research has looked at creating personalised 3D anatomical models of the heart. These models usually incorporate a geometrical reconstruction of the anatomy in order to better understand cardiovascular functions as well as predict different cardiac processes. As MRIs are becoming the standard for cardiac medical imaging, we tested our methodology on cardiac MRI data from standard acquisitions. However, the ability to accurately reconstruct heart anatomy in three dimensions commonly comes with fundamental challenges—notably, the trade-off between data fitting and expected visual appearance. Most current techniques can either require contours from parallel slices or, if multiple slice orientations are used, require an exact match between these contours. In addition, some methods introduce a bias by the use of prior shape models or by trade-offs between the data matching terms and the smoothing terms. Our approach uses a composition of smooth approximations towards the maximization of the data fitting, ensuring a good matching to the input data as well as pleasant interpolation characteristics. To assess our method in the task of cardiac mesh generations, we evaluated its performance on synthetic data obtained from a cardiac statistical shape model as well as on real data. Using a statistical shape model, we simulated standard cardiac MRI acquisitions planes and contour data. We performed a multi-parameter evaluation study using plausible cardiac shapes generated from the model. We also show that long axes contours as well as the most extremal slices (basal and apical) contain the most amount of structural information, and thus should be taken into account when generating anatomically relevant geometrical cardiovascular surfaces. Our method is both used on epicardial and endocardial left ventricle surfaces as well as on the right ventricle

Modeling and quantification of repolarization feature dependency on heart rate

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems”.Objectives: This work aims at providing an efficient method to estimate the parameters of a non linear model including memory, previously proposed to characterize rate adaptation of repolarization indices.Methods: The physiological restrictions on the model parameters have been included in the cost function in such a way that unconstrained optimization techniques such as descent optimization methods can be used for parameter estimation. The proposed method has been evaluated on electrocardiogram (ECG) recordings of healthy subjects performing a tilt test, where rate adaptation of QT and Tpeak-to-Tend (Tpe) intervals has been characterized.Results: The proposed strategy results in an efficient methodology to characterize rate adaptation of repolarization features, improving the convergence time with respect to previous strategies. Moreover, Tpe interval adapts faster to changes in heart rate than the QT interval.Conclusions: In this work an efficient estimation of the parameters of a model aimed at characterizing rate adaptation of repolarization features has been proposed. The Tpe interval has been shown to be rate related and with a shorter memory lag than the QT interval.</jats:p

Surface mesh reconstruction from cardiac MRI contours

We introduce a tool to build a surface mesh able to deal with sparse, heterogeneous, non-parallel, cross-sectional, non-coincidental contours and show its application to reconstruct surfaces of the heart. In recent years, much research has looked at creating personalised 3D anatomical models of the heart. These models usually incorporate a geometrical reconstruction of the anatomy in order to better understand cardiovascular functions as well as predict different cardiac processes. As MRIs are becoming the standard for cardiac medical imaging, we tested our methodology on cardiac MRI data from standard acquisitions. However, the ability to accurately reconstruct heart anatomy in three dimensions commonly comes with fundamental challenges—notably, the trade-off between data fitting and expected visual appearance. Most current techniques can either require contours from parallel slices or, if multiple slice orientations are used, require an exact match between these contours. In addition, some methods introduce a bias by the use of prior shape models or by trade-offs between the data matching terms and the smoothing terms. Our approach uses a composition of smooth approximations towards the maximization of the data fitting, ensuring a good matching to the input data as well as pleasant interpolation characteristics. To assess our method in the task of cardiac mesh generations, we evaluated its performance on synthetic data obtained from a cardiac statistical shape model as well as on real data. Using a statistical shape model, we simulated standard cardiac MRI acquisitions planes and contour data. We performed a multi-parameter evaluation study using plausible cardiac shapes generated from the model. We also show that long axes contours as well as the most extremal slices (basal and apical) contain the most amount of structural information, and thus should be taken into account when generating anatomically relevant geometrical cardiovascular surfaces. Our method is both used on epicardial and endocardial left ventricle surfaces as well as on the right ventricle

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