Développement et validation de méthodes visant une utilisation optimale d'antennes réceptrices en imagerie par résonance magnétique

Différentes méthodes ayant pour objectif une utilisation optimale d'antennes radio-fréquences spécialisées en imagerie par résonance magnétique sont développées et validées. Dans un premier temps, il est démontré qu'une méthode alternative de combinaison des signaux provenant des différents canaux de réception d'un réseau d'antennes mène à une réduction significative du biais causé par la présence de bruit dans des images de diffusion, en comparaison avec la méthode de la somme-des-carrés généralement utilisée. Cette réduction du biais engendré par le bruit permet une amélioration de l'exactitude de l'estimation de différents paramètres de diffusion et de diffusion tensorielle. De plus, il est démontré que cette méthode peut être utilisée conjointement avec une acquisition régulière sans accélération, mais également en présence d'imagerie parallèle. Dans une seconde perspective, les bénéfices engendrés par l'utilisation d'une antenne d'imagerie intravasculaire sont étudiés. Suite à une étude sur fantôme, il est démontré que l'imagerie par résonance magnétique intravasculaire offre le potentiel d'améliorer significativement l'exactitude géométrique lors de mesures morphologiques vasculaires, en comparaison avec les résultats obtenus avec des antennes de surface classiques. Il est illustré qu'une exactitude géométrique comparable à celle obtenue grâce à une sonde ultrasonique intravasculaire peut être atteinte. De plus, plusieurs protocoles basés sur une acquisition de type balanced steady-state free-precession sont comparés dans le but de mettre en évidence différentes relations entre les paramètres utilisés et l'exactitude géométrique obtenue. En particulier, des dépendances entre la taille du vaisseau, le rapport signal-sur-bruit à la paroi vasculaire, la résolution spatiale et l'exactitude géométrique atteinte sont mises en évidence. Dans une même optique, il est illustré que l'utilisation d'une antenne intravasculaire permet une amélioration notable de la visualisation de la lumière d'une endoprothèse vasculaire. Lorsque utilisée conjointement avec une séquence de type balanced steady-state free-precession utilisant un angle de basculement spécialement sélectionné, l'imagerie par résonance magnétique intravasculaire permet d'éliminer complètement les limitations normalement engendrées par l'effet de blindage radio-fréquence de l'endoprothèse.

Développement d'antennes RF pour l'imagerie du rat en résonance magnétique

Le présent mémoire porte sur la conception et le développement de deux antennes RF utilisées en imagerie par résonance magnétique. Ces antennes ont pour but de guider le futur développement d’une plateforme d’imagerie multi-animal qui servira les chercheurs du nouveau CRCHUM. Plus spécifiquement, ces antennes ont été conçues pour l’imagerie du proton à 1.5T. La première utilise une birdcage de type lowpass pour la partie émettrice et utilise 8 éléments de surface pour la partie réceptrice. La seconde antenne est une birdcage de type lowpass polarisée circulairement qui est utilisée à la fois pour l’émission et pour la réception. Cette dernière a présenté de bonnes performances, générant des images avec un SNR élevé et avec une bonne homogénéité, la rendant une bonne candidate pour la future plateforme. La première a présenté quelques problèmes au niveau de la désyntonisation de la birdcage et du couplage entre les éléments. Dans le cas où ces problèmes venaient à être surmontés, cette antenne aurait l’avantage de pouvoir utiliser des techniques d’imagerie parallèle et possiblement d’avoir un SNR plus élevé.

fMRI of Emotion

Recent brain imaging work has expanded our understanding of the mechanisms of perceptual, cognitive, and motor functions in human subjects, but research into the cerebral control of emotional and motivational function is at a much earlier stage. Important concepts and theories of emotion are briefly introduced, as are research designs and multimodal approaches to answering the central questions in the field. We provide a detailed inspection of the methodological and technical challenges in assessing the cerebral correlates of emotional activation, perception, learning, memory, and emotional regulation behavior in healthy humans. fMRI is particularly challenging in structures such as the amygdala as it is affected by susceptibility-related signal loss, image distortion, physiological and motion artifacts and colocalized Resting State Networks (RSNs). We review how these problems can be mitigated by using optimized echo-planar imaging (EPI) parameters, alternative MR sequences, and correction schemes. High-quality data can be acquired rapidly in these problematic regions with gradient compensated multiecho EPI or high resolution EPI with parallel imaging and optimum gradient directions, combined with distortion correction. Although neuroimaging studies of emotion encounter many difficulties regarding the limitations of measurement precision, research design, and strategies of validating neuropsychological emotion constructs, considerable improvement in data quality and sensitivity to subtle effects can be achieved. The methods outlined offer the prospect for fMRI studies of emotion to provide more sensitive, reliable, and representative models of measurement that systematically relate the dynamics of emotional regulation behavior with topographically distinct patterns of activity in the brain. This will provide additional information as an aid to assessment, categorization, and treatment of patients with emotional and personality disorders.

K-t GRAPPA-accelerated 4D flow MRI of liver hemodynamics: influence of different acceleration factors on qualitative and quantitative assessment of blood flow.

OBJECTIVE We sought to evaluate the feasibility of k-t parallel imaging for accelerated 4D flow MRI in the hepatic vascular system by investigating the impact of different acceleration factors. MATERIALS AND METHODS k-t GRAPPA accelerated 4D flow MRI of the liver vasculature was evaluated in 16 healthy volunteers at 3T with acceleration factors R = 3, R = 5, and R = 8 (2.0 × 2.5 × 2.4 mm(3), TR = 82 ms), and R = 5 (TR = 41 ms); GRAPPA R = 2 was used as the reference standard. Qualitative flow analysis included grading of 3D streamlines and time-resolved particle traces. Quantitative evaluation assessed velocities, net flow, and wall shear stress (WSS). RESULTS Significant scan time savings were realized for all acceleration factors compared to standard GRAPPA R = 2 (21-71 %) (p < 0.001). Quantification of velocities and net flow offered similar results between k-t GRAPPA R = 3 and R = 5 compared to standard GRAPPA R = 2. Significantly increased leakage artifacts and noise were seen between standard GRAPPA R = 2 and k-t GRAPPA R = 8 (p < 0.001) with significant underestimation of peak velocities and WSS of up to 31 % in the hepatic arterial system (p <0.05). WSS was significantly underestimated up to 13 % in all vessels of the portal venous system for k-t GRAPPA R = 5, while significantly higher values were observed for the same acceleration with higher temporal resolution in two veins (p < 0.05). CONCLUSION k-t acceleration of 4D flow MRI is feasible for liver hemodynamic assessment with acceleration factors R = 3 and R = 5 resulting in a scan time reduction of at least 40 % with similar quantitation of liver hemodynamics compared with GRAPPA R = 2.

An object-oriented designed finite-difference time-domain simulator for electromagnetic analysis and design in MRI - application to high field analyses

This paper presents a finite-difference time-domain (FDTD) simulator for electromagnetic analysis and design applications in MRI. It is intended to be a complete FDTD model of an MRI system including all RF and low-frequency field generating units and electrical models of the patient. The pro-ram has been constructed in an object-oriented framework. The design procedure is detailed and the numerical solver has been verified against analytical solutions for simple cases and also applied to various field calculation problems. In particular, the simulator is demonstrated for inverse RF coil design, optimized source profile generation, and parallel imaging in high-frequency situations. The examples show new developments enabled by the simulator and demonstrate that the proposed FDTD framework can be used to analyze large-scale computational electromagnetic problems in modern MRI engineering. (C) 2004 Elsevier Inc. All rights reserved.

Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T.

PURPOSE: The goal of the present study was to use a three-dimensional (3D) gradient echo volume in combination with a fat-selective excitation as a 3D motion navigator (3D FatNav) for retrospective correction of microscopic head motion during high-resolution 3D structural scans of extended duration. The fat excitation leads to a 3D image that is itself sparse, allowing high parallel imaging acceleration factors - with the additional advantage of a minimal disturbance of the water signal used for the host sequence. METHODS: A 3D FatNav was inserted into two structural protocols: an inversion-prepared gradient echo at 0.33 × 0.33 × 1.00 mm resolution and a turbo spin echo at 600 μm isotropic resolution. RESULTS: Motion estimation was possible with high precision, allowing retrospective motion correction to yield clear improvements in image quality, especially in the conspicuity of very small blood vessels. CONCLUSION: The highly accelerated 3D FatNav allowed motion correction with noticeable improvements in image quality, even for head motion which was small compared with the voxel dimensions of the host sequence. Magn Reson Med 75:1030-1039, 2016. © 2015 Wiley Periodicals, Inc.

C-slow retimed parallel histogram architectures for consumer imaging devices

A parallel pipelined array of cells suitable for real-time computation of histograms is proposed. The cell architecture builds on previous work obtained via C-slow retiming techniques and can be clocked at 65 percent faster frequency than previous arrays. The new arrays can be exploited for higher throughput particularly when dual data rate sampling techniques are used to operate on single streams of data from image sensors. In this way, the new cell operates on a p-bit data bus which is more convenient for interfacing to camera sensors or to microprocessors in consumer digital cameras.

Stress analysis in oral obturator prostheses over parallel and tilted implants: photoelastic imaging

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Dynamic Imaging in Electrical Impedance Tomography of the Human Chest With Online Transition Matrix Identification

One of the electrical impedance tomography objectives is to estimate the electrical resistivity distribution in a domain based only on electrical potential measurements at its boundary generated by an imposed electrical current distribution into the boundary. One of the methods used in dynamic estimation is the Kalman filter. In biomedical applications, the random walk model is frequently used as evolution model and, under this conditions, poor tracking ability of the extended Kalman filter (EKF) is achieved. An analytically developed evolution model is not feasible at this moment. The paper investigates the identification of the evolution model in parallel to the EKF and updating the evolution model with certain periodicity. The evolution model transition matrix is identified using the history of the estimated resistivity distribution obtained by a sensitivity matrix based algorithm and a Newton-Raphson algorithm. To numerically identify the linear evolution model, the Ibrahim time-domain method is used. The investigation is performed by numerical simulations of a domain with time-varying resistivity and by experimental data collected from the boundary of a human chest during normal breathing. The obtained dynamic resistivity values lie within the expected values for the tissues of a human chest. The EKF results suggest that the tracking ability is significantly improved with this approach.

Denisiuk-type reflection holography display with sillenite crystals for imaging and interferometry of small objects

A novel setup for imaging and interferometry through reflection holography with Bi12TiPO20(BTO) sillenite photorefractive crystals is proposed. A variation of the lensless Denisiuk arrangement was developed resulting in a compact, robust and simple interferometer. A red He-Ne laser was used as light source and the holographic recording occurred by diffusion with the grating vector parallel to the crystal [0 0 1]-axis. In order to enhance the holographic image quality and reduce noise a polarizing beam splitter (PBS) was positioned at the BTO input and the crystal was tilted around the [0 0 1]-axis. This enabled the orthogonally polarized transmission and diffracted beams to be separated by the PBS, providing the holographic image only. The possibility of performing deformation and strain analysis as well as vibration measurement of small objects was demonstrated. (C) 2007 Elsevier B.V. All rights reserved.

Parallel processing and image analysis in the eyes of mantis shrimps

The compound eyes of mantis shrimps, a group of tropical marine crustaceans, incorporate principles of serial and parallel processing of visual information that may be applicable to artificial imaging systems. Their eyes include numerous specializations for analysis of the spectral and polarizational properties of light, and include more photoreceptor classes for analysis of ultraviolet light, color, and polarization than occur in any other known visual system. This is possible because receptors in different regions of the eye are anatomically diverse and incorporate unusual structural features, such as spectral filters, not seen in other compound eyes. Unlike eyes of most other animals, eyes of mantis shrimps must move to acquire some types of visual information and to integrate color and polarization with spatial vision. Information leaving the retina appears to be processed into numerous parallel data streams leading into the central nervous system, greatly reducing the analytical requirements at higher levels. Many of these unusual features of mantis shrimp vision may inspire new sensor designs for machine vision

Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (SISAL) aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

Parallel hyperspectral coded aperture for compressive sensing on GPUs

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Optimization of breast tomosynthesis image reconstruction using parallel computing

Breast cancer is the most common cancer among women, being a major public health problem. Worldwide, X-ray mammography is the current gold-standard for medical imaging of breast cancer. However, it has associated some well-known limitations. The false-negative rates, up to 66% in symptomatic women, and the false-positive rates, up to 60%, are a continued source of concern and debate. These drawbacks prompt the development of other imaging techniques for breast cancer detection, in which Digital Breast Tomosynthesis (DBT) is included. DBT is a 3D radiographic technique that reduces the obscuring effect of tissue overlap and appears to address both issues of false-negative and false-positive rates. The 3D images in DBT are only achieved through image reconstruction methods. These methods play an important role in a clinical setting since there is a need to implement a reconstruction process that is both accurate and fast. This dissertation deals with the optimization of iterative algorithms, with parallel computing through an implementation on Graphics Processing Units (GPUs) to make the 3D reconstruction faster using Compute Unified Device Architecture (CUDA). Iterative algorithms have shown to produce the highest quality DBT images, but since they are computationally intensive, their clinical use is currently rejected. These algorithms have the potential to reduce patient dose in DBT scans. A method of integrating CUDA in Interactive Data Language (IDL) is proposed in order to accelerate the DBT image reconstructions. This method has never been attempted before for DBT. In this work the system matrix calculation, the most computationally expensive part of iterative algorithms, is accelerated. A speedup of 1.6 is achieved proving the fact that GPUs can accelerate the IDL implementation.