From 4D medical images (CT, MRI, and Ultrasound) to 4D structured mesh models of the left ventricular endocardium for patient-specific simulations

With cardiovascular disease (CVD) remaining the primary cause of death worldwide, early detection of CVDs becomes essential. The intracardiac flow is an important component of ventricular function, motion kinetics, wash-out of ventricular chambers, and ventricular energetics. Coupling between Computational Fluid Dynamics (CFD) simulations and medical images can play a fundamental role in terms of patient-specific diagnostic tools. From a technical perspective, CFD simulations with moving boundaries could easily lead to negative volumes errors and the sudden failure of the simulation. The generation of high-quality 4D meshes (3D in space + time) with 1-to-l vertex becomes essential to perform a CFD simulation with moving boundaries. In this context, we developed a semiautomatic morphing tool able to create 4D high-quality structured meshes starting from a segmented 4D dataset. To prove the versatility and efficiency, the method was tested on three different 4D datasets (Ultrasound, MRI, and CT) by evaluating the quality and accuracy of the resulting 4D meshes. Furthermore, an estimation of some physiological quantities is accomplished for the 4D CT reconstruction. Future research will aim at extending the region of interest, further automation of the meshing algorithm, and generating structured hexahedral mesh models both for the blood and myocardial volume

Assessment of hemodynamic conditions in the aorta following root replacement with composite valve-conduit graft

This paper presents the analysis of detailed hemodynamics in the aortas of four patients following replacement with a composite bio-prosthetic valve-conduit. Magnetic resonance image-based computational models were set up for each patient with boundary conditions comprising subject-specific three-dimensional inflow velocity profiles at the aortic root and central pressure waveform at the model outlet. Two normal subjects were also included for comparison. The purpose of the study was to investigate the effects of the valve-conduit on flow in the proximal and distal aorta. The results suggested that following the composite valve-conduit implantation, the vortical flow structure and hemodynamic parameters in the aorta were altered, with slightly reduced helical flow index, elevated wall shear stress and higher non-uniformity in wall shear compared to normal aortas. Inter-individual analysis revealed different hemodynamic conditions among the patients depending on the conduit configuration in the ascending aorta, which is a key factor in determining post-operative aortic flow. Introducing a natural curvature in the conduit to create a smooth transition between the conduit and native aorta may help prevent the occurrence of retrograde and recirculating flow in the aortic arch, which is particularly important when a large portion or the entire ascending aorta needs to be replaced

Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images

Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions

DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods

Oscillating shear index, wall shear stress and low density lipoprotein accumulation in human RCAs

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.Atherosclerosis shows predilection in regions of coronary arteries with hemodynamic particularities as, local disturbances of Wall Shear Stress (WSS) in space and time, and locally high concentrations of lipoprotein. Six, image-based human deceased, Right Coronary Arteries (RCA) are used to elucidate, a) Low Density Lipoprotein (LDL) transport under steady flow and b) oscillating flow (no mass transfer). A semi-permeable nature of the arterial wall computational model is incorporated with hydraulic conductivity and permeability treated as WSS dependent. The 3D reconstruction technique is a combination of angiography and IVUS. LDL is elevated at locations where WSS is low. Under steady flow conditions the area-averaged normalized LDL concentration over the RCAs, using shear dependent water infiltration and endothelial permeability is 9.6 % higher than at entrance. However, under constant water infiltration and endothelial permeability this value is only 3.2 %. High Oscillating Shear Index (OSI) and low average WSS nearly co-locate. Approximately 630000 grid nodes proved to be sufficient enough to accurately describe the oscillating flow and the LDL concentration within the RCAs

Intersubject Regularity in the Intrinsic Shape of Human V1

Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results

Computational fluid dynamicaccuracy in mimicking changes in blood hemodynamics in patients with acute type IIIb aortic dissection treated with TEVAR

Background: We aimed to verify the accuracy of the Computational Fluid Dynamics (CFD) algorithm for blood flow reconstruction for type IIIb aortic dissection (TBAD) before and after thoracic endovascular aortic repair (TEVAR). Methods: We made 3D models of the aorta and its branches using pre- and post-operative CT data from five patients treated for TBAD. The CFD technique was used to quantify the displacement forces acting on the aortic wall in the areas of endograft, mass flow rate/velocity and wall shear stress (WSS). Calculated results were verified with ultrasonography (USG-Doppler) data. Results: CFD results indicated that the TEVAR procedure caused a 7-fold improvement in overall blood flow through the aorta (p = 0.0001), which is in line with USG-Doppler data. A comparison of CFD results and USG-Doppler data indicated no significant change in blood flow through the analysed arteries. CFD also showed a significant increase in flow rate for thoracic trunk and renal arteries, which was in accordance with USG-Doppler data (accuracy 90% and 99.9%). Moreover, we observed a significant decrease in WSS values within the whole aorta after TEVAR compared to pre-TEVAR (1.34 ± 0.20 Pa vs. 3.80 ± 0.59 Pa, respectively, p = 0.0001). This decrease was shown by a significant reduction in WSS and WSS contours in the thoracic aorta (from 3.10 ± 0.27 Pa to 1.34 ± 0.11Pa, p = 0.043) and renal arteries (from 4.40 ± 0.25 Pa to 1.50 ± 0.22 Pa p = 0.043). Conclusions: Post-operative remodelling of the aorta after TEVAR for TBAD improved hemodynamic patterns reflected by flow, velocity and WSS with an accuracy of 99%