A compressed-sensing approach for ultrasound imaging

Ultrasonography uses multiple piezo-electric element probes to image tissues. Current time-domain beamforming techniques require the signal at each transducer-element to be sampled at a rate higher than the Nyquist criterion, resulting in an extensive amount of data to be received, stored and processed. In this work, we propose to exploit sparsity of the signal received at each transducer-element. The proposed approach uses multiple compressive multiplexers for signal encoding and solves an l1-minimization in the decoding step, resulting in the reduction of 75 % of the amount of data, the number of cables and the number of analog-to-digital converters required to perform high quality reconstruction

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-sparse recovery algorithms, e.g. simultaneous normalized iterative hard thresholding, subspace greedy methods and subspace-augmented multiple signal classification (MUSIC) techniques. We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. We validate the proposed algorithms on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without partially known support. The supporting code is available on https://github.com/AdriBesson/ spl2018_joint_sparse

Pulse-Stream Models In Time-Of-Flight Imaging

This paper considers the problem of reconstructing raw signals from random projections in the context of time-of-flight imaging with an array of sensors. It presents a new signal model, coined as multi-channel pulse-stream model, which exploits pulse-stream models and accounts for additional structure induced by inter-sensor dependencies. We propose a sampling theorem and a reconstruction algorithm, based on l1-minimization, for signals belonging to such a model. We demonstrate the benefits of the proposed approach by means of numerical simulations and on a real nondestructive- evaluation application where the peak-signal-to-noise ratio is increased by 3 dB compared to standard compressed-sensing strategies

A Deep Learning Approach to Ultrasound Image Recovery

Based on the success of deep neural networks for image recovery, we propose a new paradigm for the compression and decompression of ultrasound~(US) signals which relies on stacked denoising autoencoders. The first layer of the network is used to compress the signals and the remaining layers perform the reconstruction. We train the network on simulated US signals and evaluate its quality on images of the publicly available PICMUS dataset. We demonstrate that such a simple architecture outperforms state-of-the art methods, based on the compressed sensing framework, both in terms of image quality and computational complexity

Learning the weight matrix for sparsity averaging in compressive imaging

We propose to map the fast iterative shrinkage-thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal weight matrix of the corresponding reweighted l1-minimization problem. We later use the learned weight matrix for the image reconstruction process, which is recast as a simple l1-minimization problem. The approach, denoted as learned extended FISTA, shows promising results in terms of image quality, compared to state-of-the-art algorithms, and significantly reduces the reconstruction time required to solve the reweighted l1-minimization problem

Compressive Multiplexing of Ultrasound Signals

High-quality 3D ultrasound (US) imaging requires dense matrix-array probes with thousands of elements and necessitates an unrealistic number of coaxial cables to connect such probes to back-end systems. To address this issue, many techniques have been developed such as sparse arrays, mechanical scanning, multiplexing and micro-beamforming, which permit to achieve 3D imaging with existing 2D imaging systems but with a degradation in image quality. We propose a novel multiplexing method which relies on compressed-sensing (CS) principles to significantly reduce the number of coaxial cables. We exploit the compressive multiplexer (CMUX) introduced for radio-frequency signals to multiplex US signals in the probe head. The CMUX considers a set of signals as inputs, modulates them with chipping sequences and sums them to form a single output. On the reconstruction side, we propose two methods: one solving a CS-based problem exploiting sparsity of US signals in a pulse-stream model (CS-PS) and another one solving a least-squares problem in the Fourier domain based on bandlimited signal properties of US signals. We demonstrate through simulations and in vivo experiments that the proposed techniques lead to high-quality reconstruction with significantly fewer coaxial cables, up to 12x less with CS-PS

USSR: An ultrasound sparse regularization framework

Ultrafast ultrasound (US) imaging uses unfocused waves to insonify the whole medium of interest at once, allowing pulse-echo US imaging to achieve very high frame rates, at the cost of a lower image quality. In this paper, we present USSR, an UltraSound Sparse Regularization framework which permits high-quality imaging at fast rates and with a very low memory footprint. The framework, based on highly parallelizable, parametric, matrix-free formulations of the measurement model and its adjoint as well as on well-chosen sparsity priors, is implemented on multi-threaded architectures and evaluated on the publicly available PICMUS dataset

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

In this paper we show that it is sufficient to recover the locations of K strong reflectors within an insonified medium from three receive elements and 2K+1 samples per element. The proposed approach leverages advances in sampling signals with a finite rate of innovation along each element and rank properties from the Euclidean distance matrix construction across elements. With the proposed approach, it is not necessary to construct an image in order to identify strong reflective sources, which is why much fewer receive elements are needed. However, the assumed transmit scheme still uses a standard linear array in order to excite the entire medium with sufficient energy. The approach is validated with simulated data and a measurement that emulates a scenario in non-destructive evaluation

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