Deep Convolutional Robust PCA with Application to Ultrasound Imaging

Cohen, Regev; Zhang, Yi; Solomon, Oren; Toberman, Daniel; Taieb, Liran; Sloun, Ruud Jg van; Eldar, Yonina C.

doi:10.1109/icassp.2019.8683030

Cited by 19 publications

(38 citation statements)

References 23 publications

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“…For instance, spatiotemporal filtering with singular value decomposition (SVD) has, importantly, increased Doppler sensitivity and has been exploited to separate microbubbles from tissue (Errico et al 2015;Desailly et al 2017;Song et al 2017). In addition, deep learning techniques can be used to solve the microbubble tissue separation problem through an unfolded robust principal component scheme (Cohen et al 2019). More simply, DI can also highlight the motion or disruption of microbubbles (Desailly et al 2013).…”

Section: Microbubble Differentiation Versus Tissuementioning

confidence: 99%

See 1 more Smart Citation

Super-resolution Ultrasound Imaging

Christensen-Jeffries¹,

Couture²,

Dayton³

et al. 2020

Ultrasound in Medicine & Biology

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312

173

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The majority of exchanges of oxygen and nutrients are performed around vessels smaller than 100 mm, allowing cells to thrive everywhere in the body. Pathologies such as cancer, diabetes and arteriosclerosis can profoundly alter the microvasculature. Unfortunately, medical imaging modalities only provide indirect observation at this scale. Inspired by optical microscopy, ultrasound localization microscopy has bypassed the classic compromise between penetration and resolution in ultrasonic imaging. By localization of individual injected microbubbles and tracking of their displacement with a subwavelength resolution, vascular and velocity maps can be produced at the scale of the micrometer. Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents. These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.

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Section: Microbubble Differentiation Versus Tissuementioning

confidence: 99%

“…We first assume that the tissue clutter has been removed. This can be achieved by using high-pass filtering, singular value decomposition or deep learning methods (Cohen et al 2019). The image obtained can then be written as a convolution between the PSF of the system and the microbubbles.…”

Section: Exploiting Signal Structurementioning

confidence: 99%

Super-resolution Ultrasound Imaging

Christensen-Jeffries¹,

Couture²,

Dayton³

et al. 2020

Ultrasound in Medicine & Biology

Self Cite

312

173

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show abstract

“…In this way, RPCA has been used as a powerful tool in MRI, CT, and ultrasound imaging [72][73][74]. Many optimization algorithms have been proposed for cluster suppression in ultrasound imaging using RPCA, RMC [21,55,75].…”

Section: Explicit Decompositionmentioning

confidence: 99%

Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods

2020

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Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the dataset. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression.

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“…Such Neural Networks (NNs) are also typically over-parameterised [16]. By training light weight models, we tend to limit or avoid the under-specification problem, as there are fewer degrees of freedom in the NN architecture [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Deep Proximal Learning for High-Resolution Plane Wave Compounding

Chennakeshava¹,

Luijten²,

Mischi³

et al. 2021

Preprint

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Plane Wave imaging enables many applications that require high frame rates, including localisation microscopy, shear wave elastography, and ultra-sensitive Doppler. To alleviate the degradation of image quality with respect to conventional focused acquisition, typically, multiple acquisitions from distinctly steered plane waves are coherently (i.e. after time-of-flight correction) compounded into a single image. This poses a trade-off between image quality and achievable frame-rate. To that end, we propose a new deep learning approach, derived by formulating plane wave compounding as a linear inverse problem, that attains high resolution, high-contrast images from just 3 plane wave transmissions. Our solution unfolds the iterations of a proximal gradient descent algorithm as a deep network, thereby directly exploiting the physics-based generative acquisition model into the neural network design. We train our network in a greedy manner, i.e. layer-by-layer, using a combination of pixel, temporal, and distribution (adversarial) losses to achieve both perceptual fidelity and data consistency. Through the strong model-based inductive bias, the proposed architecture outperforms several standard benchmark architectures in terms of image quality, with a low computational and memory footprint.

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Deep Convolutional Robust PCA with Application to Ultrasound Imaging

Cited by 19 publications

References 23 publications

Super-resolution Ultrasound Imaging

Super-resolution Ultrasound Imaging

Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods

Deep Proximal Learning for High-Resolution Plane Wave Compounding

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