Sparse deconvolution method for ultrasound images based on automatic estimation of reference signals

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

doi:10.1016/j.ultras.2015.12.011

Cited by 19 publications

(12 citation statements)

References 21 publications

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“…These spurious reflections could be limited by positioning the receiver in the plane of the sound-generating membrane, at the expense of a reduced acoustical aperture due to optical shadowing. Alternatively, the ringing could be removed through source deconvolution [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Video-rate all-optical ultrasound imaging

Alles

Noimark

Maneas

et al. 2018

Biomed. Opt. Express

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All-optical ultrasound imaging, where ultrasound is generated and detected using light, has recently been demonstrated as a viable modality that is inherently insensitive to electromagnetic interference and exhibits wide bandwidths. High-quality 2D and 3D all-optical ultrasound images of tissues have previously been presented; however, to date, long acquisition times (ranging from minutes to hours) have hindered clinical application. Here, we present the first all-optical ultrasound imaging system capable of video-rate, real-time two-dimensional imaging of biological tissue. This was achieved using a spatially extended nano-composite optical ultrasound generator, a highly sensitive fibre-optic acoustic receiver, and eccentric illumination resulting in an acoustic source exhibiting optimal directivity. This source was scanned across a one-dimensional source aperture using a fast galvo mirror, thus enabling the dynamic synthesis of source arrays comprising spatially overlapping sources at non-uniform source separation distances. The resulting system achieved a sustained frame rate of 15 Hz, a dynamic range of 30 dB, a penetration depth of at least 6 mm, a resolution of 75 µm (axial) by 100 µm (lateral), and enabled the dynamics of a pulsating ex vivo carotid artery to be captured.

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Section: Discussionmentioning

confidence: 99%

Video-rate all-optical ultrasound imaging

Alles

Noimark

Maneas

et al. 2018

Biomed. Opt. Express

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“…Starting from the early 2000s, along with the increase in computational power, the used machine learning models have become more powerful. Many authors have reported favorable automatic ultrasonic results with advanced statistical pattern analysis approaches, such as sparse coding [19] and support vector machines [20]. Those research results confirmed that novel machine learning/pattern recognition techniques could substantially contribute to ultrasonic data interpretation.…”

Section: Related Workmentioning

confidence: 69%

Benchmarking Deep Learning Models for Automatic Ultrasonic Imaging Inspection

Toyama

2021

IEEE Access

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The success of deep neural networks in carrying out a wide variety of cognitive tasks also raised expectations regarding the advent of AI for the ultrasonic testing (UT) data interpretation in the Non-destructive evaluation (NDE) field. Though it is a growing area of research, we identify two main barriers that hinder research in the field: the lack of real-world, annotated datasets accessible to the public and the scarceness of benchmarked performance of the state-of-the-art deep learning models. To address these issues, we first introduce a new dataset called "USimgAIST" which contains more than 7000 real ultrasonic inspection images with both normal cases and defective ones from 17 types of flaws [1]. Using the dataset, we performed a comprehensive evaluation of representative deep learning models. Through the study, we expect to validate whether existing AI models can achieve human-level ultrasonic image understanding for defect characterization. Besides, all detailed benchmarking comparisons, including defect detection accuracy, model complexity, memory usage, and inference time, are shown. We hope this study exhibits an overview of performances of advanced learning models working for ultrasonic image analysis and lays the groundwork for prospective practitioners to compare their methods and results fairly.

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“…When we apply the OMP algorithm to the deconvolution model of phased array ultrasound, we can use the incident ultrasound reference signal to construct a matrix as the dictionary , which is more convenient to apply to this model. 6 OMP algorithm performs Gram-Schmidt orthogonalization on the atoms obtained through the index, thereby reducing the redundancy of atoms set and avoiding the problem of selecting the same atoms repeatedly. The OMP algorithm has fewer iterations and faster convergence speed.…”

Section: Figure 1 Flow Chart Of Omp Algorithmmentioning

confidence: 99%

“…Therefore sparse deconvolution methods can be applied. 6,7,8 In order to solve the sparse deconvolution problem, many algorithms have been reported these years. Commonly used strategies are often based on iterative shrinkage, relaxations, and greedy approximations.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic phased array total focusing method based on sparse deconvolution

Mei

Jin

et al. 2018

Proceedings of Meetings on Acoustics

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Ultrasonic phased array total focusing method (TFM) has been widely used in the field of nondestructive testing (NDT) as an advances post-processing imaging algorithm. It shows great potential in defect detection and characterization. However, temporal resolution of TFM is limited by transducer frequency, and the large amount of collecting data leads to low imaging efficiency. To overcome these disadvantages, an ultrasonic phased array total focusing method based on sparse deconvolution has been proposed in this paper. Based on the actual situation of ultrasonic testing, a regularized sparse deconvolution model has been established. The Bayesian theory is used to add low-probability prior information into the deconvolution model. Meanwhile, the sparse deconvolution optimization algorithm proposed in this paper is used to solve the sparse deconvolution model. Compared with ordinary phased array total focusing method, this algorithm reduces the calculation amount and improves the temporal resolution. The performance of the proposed method is proved by simulation comparisons. The results verify that the proposed method has higher temporal resolution and imaging efficiency.

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Sparse deconvolution method for ultrasound images based on automatic estimation of reference signals

Cited by 19 publications

References 21 publications

Video-rate all-optical ultrasound imaging

Video-rate all-optical ultrasound imaging

Benchmarking Deep Learning Models for Automatic Ultrasonic Imaging Inspection

Ultrasonic phased array total focusing method based on sparse deconvolution

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