A sparse reconstruction framework for Fourier-based plane wave imaging

Ultrafast 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 to 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 images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of ultrasound 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 side lobes, compared to classical methods, leading to an increase of the image quality.

Sparse regularization methods in ultrafast ultrasound imaging

Olivier Bernard, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Jean-Philippe Thiran, Yves Wiaux

Ultrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstruction. Using a single PW, these methods lead to a lower image quality than DAS with multi-focused beams. In this paper we review recent beamforming approaches based on sparse regularization methods. The imaging problem, either spatial-based (DAS) or Fourier-based, is formulated as a linear inverse problem and convex optimization algorithms coupled with sparsity priors are used to solve the ill-posed problem. We describe two applications of the framework namely the sparse inversion of the beamforming problem and the compressed beamforming in which the framework is combined with compressed sensing. Based on numerical simulations and experimental studies, we show the advantage of the proposed methods in terms of image quality compared to classical methods.

Ieee2016

Data-driven Measurement Designs for Magnetic Resonance Imaging

Baran Gözcü

Despite being a powerful medical imaging technique which does not emit any ionizing radiation, magnetic resonance imaging (MRI) always had the major problem of long scanning times that can take up to an hour depending on the application. It also requires uncomfortable breath-holds due to the slow acquisition, sedation of children and repeated scans in the cases of degraded image quality due to body motion. Recent years have seen new image reconstruction techniques that need less amount of acquired data (i.e., accelerated scans) for reconstructing MRI images: parallel imaging and compressed sensing (CS). Although much work has been done on the reconstruction side, there has been relatively less work on experimental design, i.e., which parts of the Fourier domain to acquire during the scan, an essential factor that considerably affects the performance of image reconstructions. The state-of-the-art experimental designs use random subsampling either based on parametric models or heuristical adaptive models. The requirement of extensive parameter tuning and the random nature of the performance render these methods impractical and unreliable for clinical use. Can we systematically use the data acquired during past MRI scans for the design of accelerated scans with a reliable image quality? This problem is the focus of this thesis which proposes a data-driven scan design approach and training procedures which efficiently and effectively learn the structure inherent in the data, and accordingly, design the scans that directly acquire only the most relevant information during acquisition given an acceleration rate constraint. As a result, this boosts the performance of the existing state-of-the-art compressive sensing techniques on real-world datasets. Moreover, this approach provides strong theoretical guarantees by using tools from statistical learning theory. The intensive computational training procedures of our approach are made feasible by large-scale implementations on a parallel computing cluster. In return, this approach avoids any dependence on parametric or heuristic models and provides a reliably consistent image reconstruction performance for accelerated scans. Our approach is flexible and capable of giving deterministic scan designs specific to the anatomy, to the acceleration rate in use, to the reconstruction algorithm and the scan settings such as static/dynamic and parallel imaging. Apart from measurement designs for MRI, this thesis also considers the reconstruction problem. In particular, we focus on the inverse problems that involve a mixture of regularizers in the objective function, exploiting multiple structures at the same time. For these problems, we propose a reliable and systematic optimization framework and illustrate its effectiveness. Finally, in the last part of the thesis, we present a data-driven model and an optimization method for the design of nearly isometric, linear and dimensionality reducing embeddings.

EPFL2019

A sparse regularization approach for ultrafast ultrasound imaging

Olivier Bernard, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Jean-Philippe Thiran, Yves Wiaux

Ultrafast imaging based on plane-wave (PW) in- sonification is an active area of research due to its capability of reaching high frame rates. Several approaches have been proposed either based on either of Fourier-domain reconstruction or on delay-and-sum (DAS) reconstruction. Using a single PW, these techniques achieve low quality, in terms of resolution and contrast, compared to the classic DAS method with focused beams. To overcome this drawback, compounding of several steered PWs is needed, which currently decreases the high frame rate limit that could be reached by such techniques. Based on a compressed sensing (CS) framework, we propose a new method that allows the reconstruction of high quality ultrasound (US) images from only 1 PW at the expense of augmenting the computational complexity at the reconstruction.

IEEE2015

Data-driven Measurement Designs for Magnetic Resonance Imaging

Baran Gözcü

EPFL2019