A model-based regularized inverse method for ultrasonic B-scan image reconstruction

Wu, Haiteng; Chen, Jian; Wu, Shiwei; Jin, Haoran; Yang, Kun

doi:10.1088/0957-0233/26/10/105401

Cited by 21 publications

(10 citation statements)

References 30 publications

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“…The distance between the transducer and the object depends on the TOF of the ultrasonic pulses [1]. The shape of the object can be reconstructed using a scanning imaging system or an array of transducers [2,3].…”

Section: Introductionmentioning

confidence: 99%

A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method

et al. 2019

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Ultrasound has been proven to be a valid tool for ranging, especially in water. In this paper, we design a high-resolution ultrasonic ranging system that uses a thin laser beam as an ultrasonic sensor. The laser sensing provides a noncontact method for ultrasound detection based on acousto-optic diffraction. Unlike conventional methods, the ultrasound transmitted from the transducer is recorded as the reference signal when it first passes through the laser. It can be used to improve the accuracy and resolution of the time-of-flight (TOF) by a cross-correlation method. Transducers with a central frequency of 1 MHz and diameters of 20 mm and 28 mm are used in the experiment. Five targets and a test piece are used to evaluate the ranging performance. The sound velocity is measured by the sound velocity profiler (SVP). The repeatability error of TOF is less than 4 ns, and the theoretical resolution of TOF is 0.4 ns. The results show a measurement resolution within one-tenth of the wavelength of ultrasound and an accuracy better than 0.3 mm for targets at a distance up to 0.8 m. The proposed system has potential applications in underwater ranging and thickness detection.

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Section: Introductionmentioning

confidence: 99%

A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method

et al. 2019

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“…Some recent results on the topic may be found, for instance, in [20,47,48,49,50] and in the references therein. Ultrasonic-specific methods have also been developed, as e.g., in [51]. …”

Section: Review Of the Algorithmsmentioning

confidence: 99%

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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“…The data fidelity term encodes a physics based model to reduce the error between the measurements and the projected reconstruction while the regularizer forces certain constraints on the reconstruction itself. For the data fidelity term, regularized iterative techniques for one-sided UNDE, such as [13], [14], use a simple linear model that models the propagation of the ultrasonic wave to reconstruct the reflectance B-mode images. A technique that uses the same forward model, but shows 2D images for a fixed depth (c-mode), is shown in [15].…”

Section: Introductionmentioning

confidence: 99%

“…In [14], [16]- [18], the authors used a simple regularization terms, such as l 1 or l 2 . This regularization is suitable for imaging point scatters or sparse regions.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Model-Based Iterative Reconstruction with Spatially Variant Regularization for One-Sided Non-Destructive Evaluation

Almansouri¹,

Venkatakrishnan²,

Clayton³

et al. 2018

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One-sided ultrasonic non-destructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could affect the performance of power plants, such as nuclear power plants. Most UNDE systems send acoustic pulses into the structure of interest, measure the received waveform and use an algorithm to reconstruct the quantity of interest. The most widely used algorithm in UNDE systems is the synthetic aperture focusing technique (SAFT) because it produces acceptable results in real time. A few regularized inversion techniques with linear models have been proposed which can improve on SAFT, but they tend to make simplifying assumptions that do not address how to obtain reconstructions from large real data sets. In this paper, we propose a model-based iterative reconstruction (MBIR) algorithm designed for scanning UNDE systems. To further reduce some of the artifacts in the results, we enhance the forward model to account for the transmitted beam profile, the occurrence of direct arrival signals, and the correlation between scans from adjacent regions. Next, we combine the forward model with a spatially variant prior model to account for the attenuation of deeper regions. We also present an algorithm to jointly reconstruct measurements from large data sets. Finally, using simulated and extensive experimental data, we show MBIR results and demonstrate how we can improve over SAFT as well as existing regularized inversion techniques. 1

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A model-based regularized inverse method for ultrasonic B-scan image reconstruction

Cited by 21 publications

References 30 publications

A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method

A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Ultrasonic Model-Based Iterative Reconstruction with Spatially Variant Regularization for One-Sided Non-Destructive Evaluation

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