Medical ultrasound image reconstruction using distributed compressive sampling

International audienceThis paper investigates ultrasound (US) radiofrequency (RF) signal recovery using the distributed compressed sampling framework. The “correlation” between the RF signals forming a RF image is exploited by assuming that they have the same sparse support in the 1D Fourier transform, with different coefficient values. The method is evaluated using an experimental US image. The results obtained are shown to improve a previously proposed recovery method, where the correlation between RF signals was taken into account by assuming the 2D Fourier transform of the RF image sparse

OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images

Morphological component analysis for sparse regularization in plane wave imaging

Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-posed problem resulting from the acoustic wave propagation. In the following work, a new sparse regularization method is proposed which includes a component-based modelling of the radio-frequency images as well as a pointspread- function-adaptive sparsity prior. The proposed method, evaluated on the PICMUS dataset,outperforms the classical DAS in terms of contrast and resolution

A sparse reconstruction framework for Fourier-based plane wave imaging

International audienceUltrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct high-quality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality

Sparsity-Based Super Resolution for SEM Images

The scanning electron microscope (SEM) produces an image of a sample by scanning it with a focused beam of electrons. The electrons interact with the atoms in the sample, which emit secondary electrons that contain information about the surface topography and composition. The sample is scanned by the electron beam point by point, until an image of the surface is formed. Since its invention in 1942, SEMs have become paramount in the discovery and understanding of the nanometer world, and today it is extensively used for both research and in industry. In principle, SEMs can achieve resolution better than one nanometer. However, for many applications, working at sub-nanometer resolution implies an exceedingly large number of scanning points. For exactly this reason, the SEM diagnostics of microelectronic chips is performed either at high resolution (HR) over a small area or at low resolution (LR) while capturing a larger portion of the chip. Here, we employ sparse coding and dictionary learning to algorithmically enhance LR SEM images of microelectronic chips up to the level of the HR images acquired by slow SEM scans, while considerably reducing the noise. Our methodology consists of two steps: an offline stage of learning a joint dictionary from a sequence of LR and HR images of the same region in the chip, followed by a fast-online super-resolution step where the resolution of a new LR image is enhanced. We provide several examples with typical chips used in the microelectronics industry, as well as a statistical study on arbitrary images with characteristic structural features. Conceptually, our method works well when the images have similar characteristics. This work demonstrates that employing sparsity concepts can greatly improve the performance of SEM, thereby considerably increasing the scanning throughput without compromising on analysis quality and resolution.Comment: Final publication available at ACS Nano Letter

Clutter Mitigation in Echocardiography Using Sparse Signal Separation

In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. In this paper, we apply a method called Morphological Component Analysis (MCA) for sparse signal separation with the objective of reducing such clutter artifacts. The MCA approach assumes that the two signals in the additive mix have each a sparse representation under some dictionary of atoms (a matrix), and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning the dictionary from the echo data. MCA is compared to Singular Value Filtering (SVF), a Principal Component Analysis- (PCA-) based filtering technique, and to a high-pass Finite Impulse Response (FIR) filter. Each filter is applied to a simulated hypoechoic lesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter and obtain results comparable to the SVF method in terms of contrast-to-noise ratio (CNR). Furthermore, MCA shows a lower impact on tissue sections while removing the clutter artifacts. In experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB

Reconstruction par acquisition compressée en imagerie ultrasonore médicale 3D et Doppler

This thesis is dedicated to the application of the novel compressed sensing theory to the acquisition and reconstruction of 3D US images and Doppler signals. In 3D US imaging, one of the major difficulties concerns the number of RF lines that has to be acquired to cover the complete volume. The acquisition of each line takes an incompressible time due to the finite velocity of the ultrasound wave. One possible solution for increasing the frame rate consists in reducing the acquisition time by skipping some RF lines. The reconstruction of the missing information in post processing is then a typical application of compressed sensing. Another excellent candidate for this theory is the Doppler duplex imaging that implies alternating two modes of emission, one for B-mode imaging and the other for flow estimation. Regarding 3D imaging, we propose a compressed sensing framework using learned overcomplete dictionaries. Such dictionaries allow for much sparser representations of the signals since they are optimized for a particular class of images such as US images.We also focus on the measurement sensing setup and propose a line-wise sampling of entire RF lines which allows to decrease the amount of data and is feasible in a relatively simple setting of the 3D US equipment. The algorithm was validated on 3D simulated and experimental data. For the Doppler application, we proposed a CS based framework for randomly interleaving Doppler and US emissions. The proposed method reconstructs the Doppler signal using a block sparse Bayesian learning algorithm that exploits the correlation structure within a signal and has the ability of recovering partially sparse signals as long as they are correlated. This method is validated on simulated and experimental Doppler data.DL’objectif de cette thèse est le développement de techniques adaptées à l’application de la théorie de l’acquisition compressée en imagerie ultrasonore 3D et Doppler. En imagerie ultrasonore 3D une des principales difficultés concerne le temps d’acquisition très long lié au nombre de lignes RF à acquérir pour couvrir l’ensemble du volume. Afin d’augmenter la cadence d’imagerie une solution possible consiste à choisir aléatoirement des lignes RF qui ne seront pas acquises. La reconstruction des données manquantes est une application typique de l’acquisition compressée. Une autre application d’intérêt correspond aux acquisitions Doppler duplex où des stratégies d’entrelacement des acquisitions sont nécessaires et conduisent donc à une réduction de la quantité de données disponibles. Dans ce contexte, nous avons réalisé de nouveaux développements permettant l’application de l’acquisition compressée à ces deux modalités d’acquisition ultrasonore. Dans un premier temps, nous avons proposé d’utiliser des dictionnaires redondants construits à partir des signaux d’intérêt pour la reconstruction d’images 3D ultrasonores. Une attention particulière a aussi été apportée à la configuration du système d’acquisition et nous avons choisi de nous concentrer sur un échantillonnage des lignes RF entières, réalisable en pratique de façon relativement simple. Cette méthode est validée sur données 3D simulées et expérimentales. Dans un deuxième temps, nous proposons une méthode qui permet d’alterner de manière aléatoire les émissions Doppler et les émissions destinées à l’imagerie mode-B. La technique est basée sur une approche bayésienne qui exploite la corrélation et la parcimonie des blocs du signal. L’algorithme est validé sur des données Doppler simulées et expérimentales

Sparsity-Aware Low-Power ADC Architecture with Advanced Reconstruction Algorithms

Compressive sensing (CS) technique enables a universal sub-Nyquist sampling of sparse and compressible signals, while still guaranteeing the reliable signal recovery. Its potential lies in the reduced analog-to-digital conversion rate in sampling broadband and/or multi-channel sparse signals, where conventional Nyquist-rate sampling are either technology impossible or extremely hardware costly. Nevertheless, there are many challenges in the CS hardware design. In coherent sampling, state-of-the-art mixed-signal CS front-ends, such as random demodulator and modulated wideband converter, suffer from high power and nonlinear hardware. In signal recovery, state-of-the-art CS reconstruction methods have tractable computational complexity and probabilistically guaranteed performance. However, they are still high cost (basis pursuit) or noise sensitive (matching pursuit). In this dissertation, we propose an asynchronous compressive sensing (ACS) front-end and advanced signal reconstruction algorithms to address these challenges. The ACS front-end consists of a continuous-time ternary encoding (CT-TE) scheme which converts signal amplitude variations into high-rate ternary timing signal, and a digital random sampler (DRS) which captures the ternary timing signal at sub-Nyquist rate. The CT-TE employs asynchronous sampling mechanism for pulsed-like input and has signal-dependent conversion rate. The DRS has low power, ease of massive integration, and excellent linearity in comparison to state-of-the-art mixed-signal CS front-ends. We propose two reconstruction algorithms. One is group-based total variation, which exploits piecewise-constant characteristics and achieves better mean squared error and faster convergence rate than the conventional TV scheme with moderate noise. The second algorithm is split-projection least squares (SPLS), which relies on a series of low-complexity and independent l2-norm problems with the prior on ternary-valued signal. The SPLS scheme has good noise robustness, low-cost signal reconstruction and facilitates a parallel hardware for real-time signal recovery. In application study, we propose multi-channel filter banks ACS front-end for the interference-robust radar. The proposed receiver performs reliable target detection with nearly 8-fold data compression than Nyquist-rate sampling in the presence of -50dBm wireless interference. We also propose an asynchronous compressed beamformer (ACB) for low-power portable diagnostic ultrasound. The proposed ACB achieves 9-fold data volume compression and only 4.4% contrast-to-noise ratio loss on the imaging results when compared with the Nyquist-rate ADCs

Échantillonnage spatiotemporel parcimonieux pour l’angiographie de localisation ultrasonore compressée du cerveau à faible coût

Au cours de la dernière décennie, la microscopie de localisation ultrasonore (ULM) a permis d’imager le système vasculaire cérébral in vivo comme jamais auparavant, avec une résolution d’environ dix microns. Cependant, avec une cadence d’imagerie pouvant atteindre 20.000 images par seconde, cette méthode nécessite l’acquisition, la transmission, le stockage et le traitement d’une grande quantité de données. Chacune de ces étapes peut devenir difficile sans les ordinateurs et échographes adaptés à cette application. Nous proposons ici une nouvelle méthode de reconstruction, baptisée Sparse-ULM, pour diminuer cette quantité de données et la complexité du matériel nécessaire, en sous-échantillonnant de manière aléatoire les canaux d’une sonde linéaire. L’évaluation des performances de la méthode ainsi que l’optimisation des paramètres ont été principalement réalisées in silico dans un fantôme anatomiquement réaliste, puis comparées aux acquisitions sur un cerveau de rat avec craniotomie. La réduction du nombre d’éléments actifs en réception détériore le rapport signal à bruit des données post reconstruction et peut conduire à de fausses détections de microbulles, diminuant le contraste des angiogrammes obtenus. Cependant, cela n’impacte que faiblement la précision de localisation des microbulles. Ces résultats montrent qu’il est possible de trouver un compromis entre le nombre de canaux et la qualité du réseau vasculaire reconstruit, et démontrent la faisabilité de réaliser la microscopie de localisation avec un nombre de canaux en réception considérablement réduit, ouvrant la voie à des dispositifs peu coûteux permettant une cartographie vasculaire à haute résolution.----------ABSTRACT Over the past decade, Ultrasound Localisation Microscopy (ULM) has made it possible to image cerebral vasculature in vivo as never before, with a resolution of about ten microns. However, with frame rate up to 20.000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. Each of these steps can become challenging without computers or ultrasound scanners provided for this application. Herein, we propose a novel reconstruction framework, named Sparse-ULM for decrease this quantity of data and the complexity of the required hardware by randomly sub-sampling the channels of a linear probe. Method’s performance evaluation as well as parameters optimization were mainly performed in silico in an anatomically realistic phantom and then compared to the acquisitions on a rat brain with craniotomy. Reducing the number of active elements deteriorates the signal-to-noise ratio of post-beamforming data, and could lead to false microbubbles detections, decreasing the contrast of the angiograms obtained. However, it has little effect on localization accuracy of microbubbles. These results show that a compromise can be found between the number of channels and the quality of the reconstructed vascular network, and demonstrate feasibility of performing ULM with a drastically reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping