Monte Carlo simulation of water diffusion through cardiac tissue models.

Monte Carlo diffusion simulations are commonly used to establish a reliable ground truth of tissue microstructure, including for the validation of diffusion-weighted MRI. However, selecting simulation parameters is challenging and affects validity and reproducibility. We conducted experiments to investigate critical conditions in Monte Carlo simulations, such as tissue representation complexity, simulated molecules, update duration, and compartment size. Results show significant changes in microstructure characteristics when parameters are altered, emphasizing the importance of careful control for a reliable ground truth.

Measurement of local orientation of cardiomyocyte aggregates in human left ventricle free wall samples using X-ray phase-contrast microtomography.

Most cardiomyocytes in the left ventricle wall are grouped in aggregates of four to five units that are quasi-parallel to each other. When one or more "cardiomyocyte aggregates" are delimited by two cleavage planes, this defines a "sheetlet" that can be considered as a "work unit" that contributes to the thickening of the wall during the cardiac cycle. In this paper, we introduce the skeleton method to measure the local three-dimensional (3D) orientation of cardiomyocyte aggregates in the sheetlets in three steps: data segmentation; extraction of the skeleton of the sheetlets; and calculation of the local orientation of the cardiomyocyte aggregates inside the sheetlets. These data include a series of virtual tissue volumes and five transmural human left ventricle free wall samples, imaged with 3D synchrotron radiation phase-contrast microtomography, and reconstructed with a 3.5×3.5×3.5µm3 voxel size. We computed the local orientation of the cardiomyocyte aggregates inside the sheetlets with a working window of 112×112×112µm3 in size. These data demonstrate that the skeleton method can provide accurate 3D measurements and reliable screening of the 3D evolution of the orientation of cardiomyocyte aggregates within the sheetlets. We showed that in regions that contain one population of quasi-parallel sheetlets, the orientation of the cardiomyocyte aggregates undergo "oscillations" along the perpendicular direction of the sheetlets. In regions that contain two populations of sheetlets with a different angular range, we demonstrate some discontinuity of the helix angle of the cardiomyocyte aggregates at the interface between the two populations.

Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: Application to fast environmental transmission electron tomography.

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis.

Extraction of the 3D local orientation of myocytes in human cardiac tissue using X-ray phase-contrast micro-tomography and multi-scale analysis.

This paper presents a methodology to access the 3D local myocyte arrangements in fresh human post-mortem heart samples. We investigated the cardiac micro-structure at a high and isotropic resolution of 3.5 µm in three dimensions using X-ray phase micro-tomography at the European Synchrotron Radiation Facility. We then processed the reconstructed volumes to extract the 3D local orientation of the myocytes using a multi-scale approach with no segmentation. We created a simplified 3D model of tissue sample made of simulated myocytes with known size and orientations, to evaluate our orientation extraction method. Afterwards, we applied it to 2D histological cuts and to eight 3D left ventricular (LV) cardiac tissue samples. Then, the variation of the helix angles, from the endocardium to the epicardium, was computed at several spatial resolutions ranging from 3.63 mm3 to 1123 µm3. We measure an increased range of 20° to 30° from the coarsest resolution level to the finest level in the experimental samples. This result is in line with the higher values measured from histology. The displayed tractography demonstrates a rather smooth evolution of the transmural helix angle in six LV samples and a sudden discontinuity of the helix angle in two septum samples. These measurements bring a new vision of the human heart architecture from macro- to micro-scale.

Cardiac fiber tracking using adaptive particle filtering based on tensor rotation invariant in MRI.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method currently available for cardiac fiber tracking. However, accurate and efficient cardiac fiber tracking is still a challenge. This paper presents a probabilistic cardiac fiber tracking method based on particle filtering. In this framework, an adaptive sampling technique is presented to describe the posterior distribution of fiber orientations by adjusting the number and status of particles according to the fractional anisotropy of diffusion. An observation model is then proposed to update the weight of particles by rotating diffusion tensor from the primary eigenvector to a given fiber orientation while keeping the shape of the tensor invariant. The results on human cardiac dMRI show that the proposed method is robust to noise and outperforms conventional streamline and particle filtering techniques.

Probabilistic models and numerical calculation of system matrix and sensitivity in list-mode MLEM 3D reconstruction of Compton camera images.

This paper addresses the problem of evaluating the system matrix and the sensitivity for iterative reconstruction in Compton camera imaging. Proposed models and numerical calculation strategies are compared through the influence they have on the three-dimensional reconstructed images. The study attempts to address four questions. First, it proposes an analytic model for the system matrix. Second, it suggests a method for its numerical validation with Monte Carlo simulated data. Third, it compares analytical models of the sensitivity factors with Monte Carlo simulated values. Finally, it shows how the system matrix and the sensitivity calculation strategies influence the quality of the reconstructed images.

A stochastic framework for spot-scanning particle therapy.

In spot-scanning particle therapy, inverse treatment planning is usually limited to finding the optimal beam fluences given the beam trajectories and energies. We address the much more challenging problem of jointly optimizing the beam fluences, trajectories and energies. For this purpose, we design a simulated annealing algorithm with an exploration mechanism that balances the conflicting demands of a small mixing time at high temperatures and a reasonable acceptance rate at low temperatures. Numerical experiments substantiate the relevance of our approach and open new horizons to spot-scanning particle therapy.

Cardiac fiber unfolding by semidefinite programming.

Diffusion-tensor imaging allows noninvasive assessment of the myocardial fiber architecture, which is fundamental in understanding the mechanics of the heart. In this context, tractography techniques are often used for representing and visualizing cardiac fibers, but their output is only qualitative. We introduce here a new framework toward a more quantitative description of the cardiac fiber architecture from tractography results. The proposed approach consists in taking three-dimensional (3-D) fiber tracts as inputs, and then unfolding these fibers in the Euclidean plane under local isometry constraints using semidefinite programming. The solution of the unfolding problem takes the form of a Gram matrix which defines the two-dimensional (2-D) embedding of the fibers and whose spectrum provides quantitative information on their organization. Experiments on synthetic and real data show that unfolding makes it easier to observe and to study the cardiac fiber architecture. Our conclusion is that 2-D embedding of cardiac fibers is a promising approach to supplement 3-D rendering for understanding the functioning of the heart.

Cardiac characteristics and long-term outcome in Andersen-Tawil syndrome patients related to KCNJ2 mutation.

AIMS: Andersen-Tawil syndrome (ATS) is an uncommon form of channelopathy linked to mutations in the KCNJ2 gene. Currently, little is known about the long-term arrhythmic prognosis of this disease. METHODS AND RESULTS: We conducted a retrospective multicentre study in nine French hospitals. Patients were recruited only if they were KCNJ2 mutation carriers. Thirty-six patients (female n = 22, 61%) from 20 unrelated kindred were included with a mean follow-up of 9.5 ± 8.2 years. We found 12 distinct KCNJ2 mutations in the 20 probands. Three of them were novel. Thirteen patients (36%) experienced syncope and one patient was resuscitated from cardiac arrest before diagnosis. The mean QTc interval was 439 ± 57 ms and QUc was 642 ± 64 ms. All patients had normal ejection fraction. Holter recordings in 33 patients found 11 272 premature ventricular complexes (PVCs) per day on average, 25 patients had episodes of bigeminy, and 25 patients had polymorphic PVCs. Twenty-three patients (70%) had non-sustained polymorphic ventricular tachycardia (VT), and six sustained polymorphic VT. Only one patient presented with torsades de pointes. Patients were treated with beta-blocker (n = 20), beta-blocker and amiodarone (n = 2), beta-blocker and flecainide (n = 6), or acetazolamide (n = 6). Radiofrequency ablation was attempted in five patients without clinical success. An implantable cardiac defibrillator was implanted in three patients. During follow-up, none of the patients died, four patients experienced syncope under treatment, and one patient had non-fatal cardiac arrest. CONCLUSION: Despite a severe clinical presentation with a very high rate of ventricular arrhythmias, the arrhythmic prognosis of the ATS patients is relatively good under treatment.

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.

Human atlas of the cardiac fiber architecture: study on a healthy population.

Cardiac fibers, as well as their local arrangement in laminar sheets, have a complex spatial variation of their orientation that has an important role in mechanical and electrical cardiac functions. In this paper, a statistical atlas of this cardiac fiber architecture is built for the first time using human datasets. This atlas provides an average description of the human cardiac fiber architecture along with its variability within the population. In this study, the population is composed of ten healthy human hearts whose cardiac fiber architecture is imaged ex vivo with DT-MRI acquisitions. The atlas construction is based on a computational framework that minimizes user interactions and combines most recent advances in image analysis: graph cuts for segmentation, symmetric log-domain diffeomorphic demons for registration, and log-Euclidean metric for diffusion tensor processing and statistical analysis. Results show that the helix angle of the average fiber orientation is highly correlated to the transmural depth and ranges from -41° on the epicardium to +66° on the endocardium. Moreover, we find that the fiber orientation dispersion across the population (±13°) is lower than for the laminar sheets (±31°) . This study, based on human hearts, extends previous studies on other mammals with concurring conclusions and provides a description of the cardiac fiber architecture more specific to human and better suited for clinical applications. Indeed, this statistical atlas can help to improve the computational models used for radio-frequency ablation, cardiac resynchronization therapy, surgical ventricular restoration, or diagnosis and followups of heart diseases due to fiber architecture anomalies.

MRI reconstruction from 2D truncated k-space.

PURPOSE: To shorten acquisition time by means of both partial scanning and partial echo acquisition and to reconstruct images from such 2D partial k-space acquisitions. MATERIALS AND METHODS: We propose an approach to reconstructing magnetic resonance images from 2D truncated k-space in which the k-space is truncated in both phase- and frequency-encoding directions. Unlike conventional reconstruction techniques, the proposed approach is based on a newly developed 2D singularity function analysis (SFA) model and a sparse representation of an image whose parameters can be estimated from the 2D partial k-space data. Such a sparse representation leads to an accurate recovery of the missing k-space data and, hence, an accurate reconstruction of the image. RESULTS: The proposed approach can reconstruct an image from as little as 20%-30% of the k-space data and the quality of the reconstructed image is comparable to the reference image that is reconstructed from the complete k-space data. CONCLUSION: Despite the high asymmetry of a 2D truncated k-space, the proposed approach allows for accurate reconstruction without the need of phase correction and, thus, overcomes the assumption of slow phase variations that is usually required by the existing reconstruction methods. It provides a new way of fast imaging for applications that require a significant reduction of the acquisition time.

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.

Feature-based interpolation of diffusion tensor fields and application to human cardiac DT-MRI.

Diffusion tensor interpolation is an important issue in the application of diffusion tensor magnetic resonance imaging (DT-MRI) to the human heart, all the more as the points representing the myocardium of the heart are often sparse. We propose a feature-based interpolation framework for the tensor fields from cardiac DT-MRI, by taking into account inherent relationships between tensor components. In this framework, the interpolation consists in representing a diffusion tensor in terms of two tensor features, eigenvalues and orientation, interpolating the Euler angles or the quaternion relative to tensor orientation and the logarithmically transformed eigenvalues, and reconstructing the tensor to be interpolated from the interpolated eigenvalues and tensor orientations. The results obtained with the aid of both synthetic and real cardiac DT-MRI data demonstrate that the feature-based schemes based on Euler angles or quaternions not only maintain the advantages of Log-Euclidean and Riemannian interpolation as for preserving the tensor's symmetric positive-definiteness and the monotonic determinant variation, but also preserve, at the same time, the monotonicity of fractional anisotropy (FA) and mean diffusivity (MD) values, which is not the case with Euclidean, Cholesky and Log-Euclidean methods. As a result, both interpolation schemes remove the phenomenon of FA collapse, and consequently avoid introducing artificial fiber crossing, with the difference that the quaternion is independent of coordinate system while Euler angles have the property of being more suitable for sophisticated interpolations.

Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines.

PURPOSE: The goal is to automatically detect anomalous vascular cross-sections to attract the radiologist's attention to possible lesions and thus reduce the time spent to analyze the image volume. MATERIALS AND METHODS: We assume that both lesions and calcifications can be considered as local outliers compared to a normal cross-section. Our approach uses an intensity metric within a machine learning scheme to differentiate normal and abnormal cross-sections. It is formulated as a Density Level Detection problem and solved using a Support Vector Machine (DLD-SVM). The method has been evaluated on 42 synthetic phantoms and on 9 coronary CT data sets annotated by 2 experts. RESULTS: The specificity of the method was 97.57% on synthetic data, and 86.01% on real data, while its sensitivity was 82.19 and 81.23%, respectively. The agreement with the observers, measured by the kappa coefficient, was substantial (κ = 0.72). After the learning stage, which is performed off-line, the average processing time was within 10 s per artery. CONCLUSIONS: To our knowledge, this is the first attempt to use the DLD-SVM approach to detect vascular abnormalities. Good specificity, sensitivity and agreement with experts, as well as a short processing time, show that our method can facilitate medical diagnosis and reduce evaluation time by attracting the reader's attention to suspect regions.

Denoising by averaging reconstructed images: application to magnetic resonance images.

A novel denoising approach is proposed that is based on averaging reconstructed images. The approach first divides the spectrum of the image to be denoised into different parts. From every such partial spectrum is then reconstructed an image using a 2-D singularity function analysis model. By expressing each of the reconstructed images as the sum of the same noise-free image and a different smaller noise, the denoising is achieved through averaging the reconstructed images. The theoretical formulation and experimental results on both simulated and real images consistently demonstrated that the proposed approach can efficiently denoise while maintaining high image quality, and presents significant advantages over conventional denoising methods.

Phase correction-based singularity function analysis for partial k-space reconstruction.

Partial k-space acquisition is a conventional method in magnetic resonance imaging (MRI) for reducing imaging time while maintaining image quality. In this field, image reconstruction from partial k-space is a key issue. This paper proposes an approach fundamentally different from traditional techniques for reconstructing magnetic resonance (MR) images from partial k-space. It uses a so-called singularity function analysis (SFA) model based on phase correction. With such a reconstruction approach, some nonacquired negative spatial frequencies are first recovered by means of phase correction and Hermitian symmetry property, and then the other nonacquired negative and/or positive spatial frequencies are estimated using the mathematical SFA model. The method is particularly suitable for asymmetrical partial k-space acquisition owing to its ability of overcoming reconstruction limitations due to k-space truncations. The performance of this approach is evaluated using both simulated and real MR brain images, and compared with existing techniques. The results demonstrate that the proposed SFA based on phase correction achieves higher image quality than the initial SFA or the projection-onto-convex sets (POCS) method.

SIMONE: a realistic neural network simulator to reproduce MEA-based recordings.

Contemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy.

Analysis of cardiac diffusion tensor magnetic resonance images using sparse representation.

In cardiac diffusion tensor magnetic resonance imaging (DT-MRI), low signal-to-noise ratio (SNR) inherently hampers the measurement accuracy of myocardium fiber structures. This paper presents a new method for filtering diffusion weighted (DW) images in cardiac DT-MRI. The method is based on sparse representation through using basis pursuit denoising (BPDN) algorithm allowing seeking overall sparest solution. It decomposes useful structures in DW images into sparsely representing atoms with Heaviside dictionary, while yielding nonsparse representation on noise, which leads to the separation of the noise from the image's useful structures. The proposed method is evaluated on both simulated and real cardiac DW images.