Wanyu Liu

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.

Structure-adaptive sparse denoising for diffusion-tensor MRI

Diffusion tensor magnetic resonance imaging (DT-MRI) is becoming a prospective imaging technique in clinical applications because of its potential for in vivo and non-invasive characterization of tissue organization. However, the acquisition of diffusion-weighted images (DWIs) is often corrupted by noise and artifacts, and the intensity of diffusion-weighted signals is weaker than that of classical magnetic resonance signals. In this paper, we propose a new denoising method for DT-MRI, called structure-adaptive sparse denoising (SASD), which exploits self-similarity in DWIs. We define a similarity measure based on the local mean and on a modified structure-similarity index to find sets of similar patches that are arranged into three-dimensional arrays, and we propose a simple and efficient structure-adaptive window pursuit method to achieve sparse representation of these arrays. The noise component of the resulting structure-adaptive arrays is attenuated by Wiener shrinkage in a transform domain defined by two-dimensional principal component decomposition and Haar transformation. Experiments on both synthetic and real cardiac DT-MRI data show that the proposed SASD algorithm outperforms state-of-the-art methods for denoising images with structural redundancy. Moreover, SASD achieves a good trade-off between image contrast and image smoothness, and our experiments on synthetic data demonstrate that it produces more accurate tensor fields from which biologically relevant metrics can then be computed.

A classification of file placement and replication methods on grids

This paper presents a classification of file placement and replication methods on grids. The study is motivated by file transfer issues encountered in the Virtual Imaging Platform deployed on the European Grid Infrastructure. Approaches proposed in the last 6 years are classified using taxonomies of replication process, replication optimization, file models, resource models and replication validation. Most existing approaches implement file replication as a middleware service, using dynamic strategies. Production approaches are slightly different than works evaluated in simulation or in controlled conditions which (i) mostly assumes simplistic file models (undistinguished read-only files), (ii) rely on elaborated access patterns, (iii) assume clairvoyance of the infrastructure parameters and (iv) study file availability less than other metrics but insist on cost.

Sparse representation based MRI denoising with total variation

Diffusion tensor magnetic resonance imaging is a newly developed imaging technique; however, this technique is noise sensitive. This paper presents a novel method for sparse representation denoising of MR images that propose sparse representation of the corrupted images with the knowledge of the Rician noise model. The proposed model inferring the prior that MR images are composed of several separated regions with uniform intensity, therefore, total variation can be combined to further smooth every region. Since sparse representation performs well in extracting features from images, coupled with the total variation regularization, the method offers excellent combination of noise removal and edge preservation. The experiment results demonstrate that the proposed method preserves most of the fine structure in cardiac diffusion weighted images.

Multigrid-based reconstruction algorithm for quantitative photoacoustic tomography.

This paper proposes a multigrid inversion framework for quantitative photoacoustic tomography reconstruction. The forward model of optical fluence distribution and the inverse problem are solved at multiple resolutions. A fixed-point iteration scheme is formulated for each resolution and used as a cost function. The simulated and experimental results for quantitative photoacoustic tomography reconstruction show that the proposed multigrid inversion can dramatically reduce the required number of iterations for the optimization process without loss of reliability in the results.

Generalization of intravoxel incoherent motion model by introducing the notion of continuous pseudodiffusion variable.

A generalized intravoxel incoherent motion (IVIM) model, called the GIVIM, was proposed to better account for complex perfusion present in the tissues having various vessels and flow regimes, such as the liver.

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.

Analytical model of optical fluence inside multiple cylindrical inhomogeneities embedded in an otherwise homogeneous turbid medium for quantitative photoacoustic imaging

We present an analytical model of optical fluence for multiple cylindrical inhomogeneities embedded in an otherwise homogeneous turbid medium. The model is based on the diffusion equation and represents the optical fluence distribution inside and outside inhomogeneities as a series of modified Bessel functions. We take into account the interplay between cylindrical inhomogeneities by introducing new boundary conditions on the surface of inhomogeneities. The model is compared with the numerical solution of the diffusion equation with NIRFAST software. The fluences inside the inhomogeneities obtained by the two methods are in close agreement. This permits the use of the model as a forward model for quantitative photoacoustic imaging.

Feature-Preserving Smoothing of Diffusion Weighted Images Using Nonstationarity Adaptive Filtering

Although promising for studying the microstructure of in vivo tissues, the performance and the potentiality of diffusion tensor magnetic resonance imaging are hampered by the presence of high-level noise in diffusion weighted (DW) images. This paper proposes a novel smoothing approach, called the nonstationarity adaptive filtering, which estimates the intensity of a pixel by averaging intensities in its adaptive homogeneous neighborhood. The latter is determined according to five constraints and spatiodirectional nonstationarity measure maps. The proposed approach is compared with an anisotropic diffusion method used in DW image smoothing. Experimental results on both synthetic and real human DW images show that the proposed method achieves a better compromise between the smoothness of homogeneous regions and the preservation of desirable features such as boundaries, even for highly noisy data, thus leading to homogeneously consistent tensor fields and consequently more coherent fibers.

A statistic based approach for segmentation

Segmentation is one of the most active topics in image processing and computer vision fields. Although knowledge in this area is developing very fast [1][2 noisy images, especially the ultrasonic ones, remain difficult to process [3][4]. The main idea of this paper is to sort out the relevant changes of the local stationarity of the image with a statistical approach instead of a gradient based one [5][6]. One application case is reported.