Sparsity driven ultrasound imaging

Tüysüzoğlu, Ahmet; Kracht, Jonathan M.; Cleveland, Robin O.; Çetin, Müjdat; Karl, W.C.

doi:10.1121/1.3675002

Cited by 30 publications

(24 citation statements)

References 45 publications

(42 reference statements)

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“…While fluctuation-based approaches eliminate the need for isolating individual sources, they remain limited in terms of spatial resolution improvement and temporal resolution. It was also shown that model-based reconstruction with sparsity constraints on the sample, an approach originating from the field of compressed sensing 9 , could yield super-resolved US 30,31 and PA 32 images. A major advantage of model-based reconstruction over the previously mentioned localization-based and fluctuation-based techniques is that by requiring (in principle) only a single-shot acquisition it permits a high temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution photoacoustic and ultrasound imaging with sparse arrays

Vilov

Arnal

Hojman

et al. 2020

Sci Rep

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It has previously been demonstrated that model-based reconstruction methods relying on a priori knowledge of the imaging point spread function (PSF) coupled to sparsity priors on the object to image can provide super-resolution in photoacoustic (PA) or in ultrasound (US) imaging. Here, we experimentally show that such reconstruction also leads to super-resolution in both PA and US imaging with arrays having much less elements than used conventionally (sparse arrays). As a proof of concept, we obtained super-resolution PA and US cross-sectional images of microfluidic channels with only 8 elements of a 128-elements linear array using a reconstruction approach based on a linear propagation forward model and assuming sparsity of the imaged structure. Although the microchannels appear indistinguishable in the conventional delay-and-sum images obtained with all the 128 transducer elements, the applied sparsity-constrained model-based reconstruction provides super-resolution with down to only 8 elements. We also report simulation results showing that the minimal number of transducer elements required to obtain a correct reconstruction is fundamentally limited by the signal-to-noise ratio. The proposed method can be straigthforwardly applied to any transducer geometry, including 2D sparse arrays for 3D super-resolution PA and US imaging.

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Section: Introductionmentioning

confidence: 99%

Super-resolution photoacoustic and ultrasound imaging with sparse arrays

Vilov

Arnal

Hojman

et al. 2020

Sci Rep

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show abstract

“…The regularization parameter λ balances the first term and second term of Equation (2), which means the value is affected by the fidelity of the measurements, the noise level, and the sparse level of the image [26]. For a given C-scan image, a large value will result in a least squares solution which may be unstable, while a small value may over smooth the solution due to excessive regularization [18,26,27,28]. Here, we set λ according to the method described in [28].…”

Section: Methodsmentioning

confidence: 99%

“…For a given C-scan image, a large value will result in a least squares solution which may be unstable, while a small value may over smooth the solution due to excessive regularization [18,26,27,28]. Here, we set λ according to the method described in [28]. …”

Section: Methodsmentioning

confidence: 99%

“…Then y and

\overset{false^}{x}

are evaluated using a target-to-clutter ratio (TCR) metric adapted from [28], which is a measure of the signal in the target region relative to the signal from the background region. The TCR represents the signal-to-noise ratio of the image,

T C R = 20 \log_{10} (false(\frac{1}{N_{T}} true \sum_{T} f_{i j} false) / false(\frac{1}{N_{b}} true \sum_{b} f_{i j} false)),

where

f_{i j}

denotes the pixel of the image, T denotes the target region in the image, N T is the number of pixels in the target region, b denotes the background region, and N b is the number of pixels in the background region.…”

Section: Methodsmentioning

confidence: 99%

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Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging

Zhang

Shi

et al. 2016

Sensors

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Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging.

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“…This trick is not directly transposable to the migration technique. Finally, another advantage of migration approach is the easier possibility to use filtering and regularization techniques [13].…”

Section: Migration Tfm Methodsmentioning

confidence: 99%

Fast total focusing method for ultrasonic imaging

Carcreff¹,

Dao²,

Braconnier³

2016

AIP Conference Proceedings

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Abstract. Synthetic aperture focusing technique (SAFT) and total focusing method (TFM) have become popular tools in the field of ultrasonic non destructive testing. In particular, they are employed for detection and characterization of flaws. From data acquired with a transducer array, those techniques aim at reconstructing an image of the inspected object from coherent summations. In this paper, we make a comparison between the standard technique and a migration approach. Using experimental data, we show that the developed approach is faster and offers a better signal to noise ratio than the standard total focusing method. Moreover, the migration is particularly effective for near-surface imaging where standard methods used to fail. On the other hand, the migration approach is only adapted to layered objects whereas the standard technique can fit complex geometries. The methods are tested on homogeneous pieces containing artificial flaws such as side drilled holes.

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Sparsity driven ultrasound imaging

Cited by 30 publications

References 45 publications

Super-resolution photoacoustic and ultrasound imaging with sparse arrays

Super-resolution photoacoustic and ultrasound imaging with sparse arrays

Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging

Fast total focusing method for ultrasonic imaging

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