Lihui Wang

Assessment of Cardiac Motion Effects on the Fiber Architecture of the Human Heart In Vivo

The use of diffusion tensor imaging (DTI) for studying the human heart in vivo is very challenging due to cardiac motion. This paper assesses the effects of cardiac motion on the human myocardial fiber architecture. To this end, a model for analyzing the effects of cardiac motion on signal intensity is presented. A Monte-Carlo simulation based on polarized light imaging data is then performed to calculate the diffusion signals obtained by the displacement of water molecules, which generate diffusion weighted (DW) images. Rician noise and in vivo motion data obtained from DENSE acquisition are added to the simulated cardiac DW images to produce motion-induced datasets. An algorithm based on principal components analysis filtering and temporal maximum intensity projection (PCATMIP) is used to compensate for motion-induced signal loss. Diffusion tensor parameters derived from motion-reduced DW images are compared to those derived from the original simulated DW images. Finally, to assess cardiac motion effects on in vivo fiber architecture, in vivo cardiac DTI data processed by PCATMIP are compared to those obtained from one trigger delay (TD) or one single phase acquisition. The results showed that cardiac motion produced overestimated fractional anisotropy and mean diffusivity as well as a narrower range of fiber angles. The combined use of shifted TD acquisitions and postprocessing based on image registration and PCATMIP effectively improved the quality of in vivo DW images and subsequently, the measurement accuracy of fiber architecture properties. This suggests new solutions to the problems associated with obtaining in vivo human myocardial fiber architecture properties in clinical conditions.

Multiscale Modeling and Simulation of the Cardiac Fiber Architecture for DMRI

Cardiac fiber architecture plays an important role in the study of mechanical and electrical properties of the wall of the human heart, but still remains to be elucidated. This paper proposes to investigate, in a multiscale manner, how the arrangement patterns and morphological heterogeneity of cardiac myocytes influence the fibers orientation. To this end, different virtual cardiac fiber structures are modeled, and diffusion tensor imaging at multiple scales are simulated using the Monte Carlo method. The results show that the proposed modeling and simulation allow us to quantitatively describe the variation of the measured tissue properties (fiber orientation and fractional anisotropy) as a function of the observation scale.

High resolution extraction of local human cardiac fibre orientations

Diffusion tensor magnetic resonance imaging (DTMRI) is usually used to detect the displacement distribution of water molecules in biological structure. However, in post-mortem heart fibre imaging, the low spatial resolution does not allow investigating the cardiac fibre structure at microscopic scale. In this paper, the myocyte arrangement of a human heart is investigated at a high resolution of 3.5 μm using the European Synchrotron Radiation Facility (ESRF). The orientation of the myocytes is then computed and extracted at various depths of the heart sample with a multi-scale approach. The helix arrangement of the fibre is obtained at a higher resolution compared to DTMRI. The results show that the measured elevation angles are in good agreement with knowledge of cardiac muscle anatomy. Such high-resolution cardiac fibre orientation information can be used to validate DTMRI measurements and analyze the evolution of cardiac fibre orientations from microscopic level to macroscopic one.

Simulation of diffusion anisotropy in DTI for virtual cardiac fiber structure

Diffusion anisotropy is the most fundamental and important parameter in the description of cardiac fibers using diffusion tensor magnetic resonance imaging (DTI), by reflecting the microstructure variation of the fiber. It is, however still not clear how the diffusion anisotropy is influenced by different contiguous structures (collagen, cardiac myocyte, etc.). In this paper, a virtual cardiac fiber structure is modeled, and diffusion weighted imaging (DWI) and DTI are simulated by the Monte Carlo method at various scales. The influences of the water content ratio in the cytoplasm and the extracellular space and the membrane permeability upon diffusion anisotropy are investigated. The simulation results show that the diffusion anisotropy increases with the increase of the ratio of water content between the intracellular cytoplasm and the extracellular medium. We show also that the anisotropy decreases with the increase of myocyte membrane permeability.

Stochastic diffusion equation with singular diffusivity and gradient-dependent noise in binary tomography

In this work, we use stochastic diffusion equation with a singular diffusivity and a gradient-dependent noise to improve the reconstruction of binary tomography cross-sections obtained from a small number of projections. A first reconstruction image is obtained with the Total Variation regularization method. The reconstruction is then refined with this stochastic approach. The method is applied to a noisy bone cross-section with 10 projection angles.

Diffusion MRI simulation for human brain based on the atlas

Diffusion magnetic resonance imaging (dMRI), including diffusion tensor imaging (DTI) and various high-angular-resolution imaging (HARDI) techniques, has been widely used in the research of neuroscience and clinical applications. With the development of new acquisition and imaging schemes, numerous novel post-processing methods are also proposed. However, it remains unclear whether the newly proposed methods are better than the existing ones since there is no ground-truth. Therefore, providing a realistic digital phantom and the corresponding simulated diffusion weighted (DW) image data sets is essential for evaluating quantitatively the performance of different modalities and subsequent analysis methods. In this work, we propose to use the human brain DTI atlas to construct a digital brain phantom, and then combine the Bloch equation and Mont-Carlo method to simulate DW images and diffusion tensor images. The simulation results are validated by DTI atlas. The experimental results demonstrate that such simulation tool is able to generate realistic DW images along any directions, which permits for the quantitative comparison of different acquisition, reconstruction and processing schemes by using our simulation data sets.

Cardiac diffusion tensor imaging based on compressed sensing using joint sparsity and low-rank approximation

Diffusion tensor magnetic resonance (DTMR) imaging and diffusion tensor imaging (DTI) have been widely used to probe noninvasively biological tissue structures. However, DTI suffers from long acquisition times, which limit its practical and clinical applications. This paper proposes a new Compressed Sensing (CS) reconstruction method that employs joint sparsity and rank deficiency to reconstruct cardiac DTMR images from undersampled k-space data. Diffusion-weighted images acquired in different diffusion directions were firstly stacked as columns to form the matrix. The matrix was row sparse in the transform domain and had a low rank. These two properties were then incorporated into the CS reconstruction framework. The underlying constrained optimization problem was finally solved by the first-order fast method. Experiments were carried out on both simulation and real human cardiac DTMR images. The results demonstrated that the proposed approach had lower reconstruction errors for DTI indices, including fractional anisotropy (FA) and mean diffusivities (MD), compared to the existing CS-DTMR image reconstruction techniques.

Combining total variation with nonlocal self-similarity constraint for compressed sensing MRI

Undersampling k-space data is an efficient way to reduce the acquisition time of magnetic resonance imaging (MRI) technique. As a promising signal recovery method, compressed sensing (CS) is able to reconstruct magnetic resonance images using a few samples and therefore has great potential in speeding up MRI process. The traditional total variation (TV) based CS approaches tend to over-smooth local image details. This paper proposes an improved CS reconstruction method for MR images by combining local TV regularization, wavelet sparsity regularization and nonlocal (NL) self-similarity constraint together. The experimental results demonstrate that the local TV model and NL self-similarity constraint are complementary to each other, making the proposed approach highly effective in reducing noise and preserving image edges and details.

Simulation of dynamic DTI of 3D cardiac fiber structures

In-vivo three-dimensional (3D) DTI acquisition of an entire human heart for a complete cardiac cycle is currently technically impossible. We propose an alternative to this problem by simulating DTI of dynamic cardiac fiber structures. We first model a virtual dynamic cardiac fiber structure by integrating cardiac motion information into an ex vivo referential cardiac fiber structure and then use a Monte-Carlo method to simulate the corresponding diffusion weighted (DW) images, from which the diffusion tensors are derived and the relationship between diffusion image properties and cardiac motion is analyzed. The proposed approach enables us to investigate dynamic diffusion image properties at different cardiac phases. The results show that during the diastole, the cardiac fiber orientation distribution is rather regular, the fractional anisotropy (FA) increases, and the mean diffusivity (MD) decreases, and that this trend is reversed in the systole.

Statistical analysis of transmural laminar microarchitecture of the human left ventricle

A good knowledge of the cardiac microarchitecture is essential for better understanding the function of the human heart. This paper investigates the transmural 3D microstructure of the left ventricle of the human heart. An ex-vivo sample (7 × 7 × 15 mm3) extracted from the anterior wall of the myocardium is imaged using X-rays phase contrast micro-tomography. Sampling the volume at high isotropic resolution of 3.5 × 3.5 × 3.5 μm3 allows a clear reveal of the laminar structure of collagen wrapping the myocytes groups. An image processing protocol is developed to automatically extract cleavage planes and compute statistics of their thickness and distance separating them. The results show the clear presence of cleavage planes in the myocardium and their variation in terms of thickness, interplanes distances and local orientation, which contribute to a better understanding of the human heart function.