A regularized inverse approach to ultrasonic pulse-echo imaging

Lavarello, Roberto; Kamalabadi, F.; O’Brien, William D.

doi:10.1109/tmi.2006.873297

Cited by 39 publications

(36 citation statements)

References 32 publications

(42 reference statements)

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“…This kind of modeling has been investigated for general ultrasound imaging, as in [3,39], as well as for medical ultrasound imaging, as in [2,40], in order to allow image reconstruction by the IPB approach, as proposed in this study.…”

Section: The Ultrasonic Acquisition Modelmentioning

confidence: 99%

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An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Valente

Zibetti

Pipa

et al. 2017

Sensors

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Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an ℓ1-regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable.

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Section: The Ultrasonic Acquisition Modelmentioning

confidence: 99%

“…The quality enhancement made possible by this approach has been shown, for example, in [2,3,4], while [5] shows that significant improvement of axial and lateral resolutions with respect to beamforming may be achieved.…”

Section: Introductionmentioning

confidence: 99%

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Valente

Zibetti

Pipa

et al. 2017

Sensors

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“…The dTONE algorithm is built upon a simple linear model that has previously been applied to medical ultrasound (Lavarello, et al 2006; Lingvall 2004; Stoughton and Strait 1993). The region of interest (ROI) – which describes the output image – is first subdivided into a collection of hypothetical targets at arbitrary positions, as shown on the left panel of Fig.…”

Section: Tone Image Reconstructionmentioning

confidence: 99%

Super-Resolution Image Reconstruction Using Diffuse Source Models

Ellis

Viola

Walker

2010

Ultrasound in Medicine & Biology

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Image reconstruction is central to many scientific fields, from medical ultrasound and sonar to computed tomography and computer vision. While lenses play a critical reconstruction role in these fields, digital sensors enable more sophisticated computational approaches. A variety of computational methods have thus been developed, with the common goal of increasing contrast and resolution to extract the greatest possible information from raw data. This paper describes a new image reconstruction method named the Diffuse Time-domain Optimized Near-field Estimator (dTONE). dTONE represents each hypothetical target in the system model as a diffuse region of targets rather than a single discrete target, which more accurately represents the experimental data that arise from signal sources in continuous space, with no additional computational requirements at the time of image reconstruction. Simulation and experimental ultrasound images of animal tissues show that dTONE achieves image resolution and contrast far superior to those of conventional image reconstruction methods. We also demonstrate the increased robustness of the diffuse target model to major sources of image degradation, through the addition of electronic noise, phase aberration, and magnitude aberration to ultrasound simulations. Using experimental ultrasound data from a tissuemimicking phantom containing a 3 mm diameter anechoic cyst, the conventionally reconstructed image has a cystic contrast of −6.3 dB whereas the dTONE image has a cystic contrast of −14.4 dB.

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“…The resulting optimization problem is computationally challenging and was solved using Markov chain Monte Carlo techniques. Lavarello et al 15 investigated the feasibility of a generalized Tikhonov technique. They use time-domain data and estimate a realvalued reflectivity field and perform performance analysis on simulated two-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Sparsity driven ultrasound imaging

Tüysüzoğlu

Kracht

Cleveland

et al. 2012

The Journal of the Acoustical Society of America

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An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data.

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A regularized inverse approach to ultrasonic pulse-echo imaging

Cited by 39 publications

References 32 publications

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Super-Resolution Image Reconstruction Using Diffuse Source Models

Sparsity driven ultrasound imaging

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