A novel transcranial ultrasound imaging method with diverging wave transmission and deep learning approach

Du, Bin; Wang, Jinyan; Zheng, Haoteng; Xiao, Chenhui; Fang, Siyuan; Lu, Minhua; Mao, Rui

doi:10.1016/j.cmpb.2019.105308

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…Nerve segmentation [151], [222] Base U-net [50] Inception block [223] Residual block [224] Modified parallel U-net Breast lesion [225] Base U-net [39] Attention gate Arterial wall [226] Base U-net Cardiovascular structures [227] Base U-net Fetal head [219] Base U-net Gastrointestinal tumor [228] Base U-net Knee cartilage [96] Modified U-net with dual parallel encoders Preterm birth prediction [220] Base U-net Thyroid [229] Residual block Transcranial detection [221] Base U-net Ovary detection [230] Base U-net…”

Section: Reference Model/methods Usedmentioning

confidence: 99%

“…Additionally, unlike many other image modalities, ultrasound devices are more maneuverable and can capture images from multiple angles. Ultrasound is also safe since it does not use radiation, hence it is the primary imaging modality for pregnancy-related diagnosis [219]- [221]. Medical ultrasound use cases also include analysis of soft tissue such as nerve bundles [39], [50], [151], [222], [223].…”

Section: Ultrasoundmentioning

confidence: 99%

See 1 more Smart Citation

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: Reference Model/methods Usedmentioning

confidence: 99%

Section: Ultrasoundmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…1 B. The experimental system was mainly composed of 5 parts: ultrasonic imaging system, transcranial AMM, skull, water tank, and resolution phantom [ 29 ]. The ultrasonic imaging system was used to collect the transcranial images.…”

Section: Resultsmentioning

confidence: 99%

Transcranial Acoustic Metamaterial Parameters Inverse Designed by Neural Networks

Yang,

Jiang,

Zhang

et al. 2023

BME Front

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Objective: The objective of this work is to investigate the mapping relationship between transcranial ultrasound image quality and transcranial acoustic metamaterial parameters using inverse design methods. Impact Statement: Our study provides insights into inverse design methods and opens the route to guide the preparation of transcranial acoustic metamaterials. Introduction: The development of acoustic metamaterials has enabled the exploration of cranial ultrasound, and it has been found that the influence of the skull distortion layer on acoustic waves can be effectively eliminated by adjusting the parameters of the acoustic metamaterial. However, the interaction mechanism between transcranial ultrasound images and transcranial acoustic metamaterial parameters is unknown. Methods: In this study, 1,456 transcranial ultrasound image datasets were used to explore the mapping relationship between the quality of transcranial ultrasound images and the parameters of transcranial acoustic metamaterials. Results: The multioutput parameter prediction model of transcranial metamaterials based on deep back-propagation neural network was built, and metamaterial parameters under transcranial image evaluation indices are predicted using the prediction model. Conclusion: This inverse big data design approach paves the way for guiding the preparation of transcranial metamaterials.

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“…The field of deep learning in medical ultrasound imaging is rapidly expanding with many applications such as clutter suppression in Doppler [1], super-resolution imaging [2,3], anomaly detection methods for breast ultrasound images [4], beamforming pre-steered, subsampled data [5], transcranial ultrasound imaging [6,7], minimum variance beamforming [8,9], and image reconstruction from raw channel data [10][11][12][13]. In the context of image reconstruction from raw channel data, CNNs have proven their capability to learn the reconstruction process without requiring explicit input information regarding receiver array geometry, a medium speed of sound, or the spatial discretization of the imaged region of interest (parameters which are typically essential for the standard delay and sum algorithm).…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging

Sharahi,

Acconcia,

et al. 2023

Sensors

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Convolutional neural networks (CNNs), initially developed for image processing applications, have recently received significant attention within the field of medical ultrasound imaging. In this study, passive cavitation imaging/mapping (PCI/PAM), which is used to map cavitation sources based on the correlation of signals across an array of receivers, is evaluated. Traditional reconstruction techniques in PCI, such as delay-and-sum, yield high spatial resolution at the cost of a substantial computational time. This results from the resource-intensive process of determining sensor weights for individual pixels in these methodologies. Consequently, the use of conventional algorithms for image reconstruction does not meet the speed requirements that are essential for real-time monitoring. Here, we show that a three-dimensional (3D) convolutional network can learn the image reconstruction algorithm for a 16×16 element matrix probe with a receive frequency ranging from 256 kHz up to 1.0 MHz. The network was trained and evaluated using simulated data representing point sources, resulting in the successful reconstruction of volumetric images with high sensitivity, especially for single isolated sources (100% in the test set). As the number of simultaneous sources increased, the network’s ability to detect weaker intensity sources diminished, although it always correctly identified the main lobe. Notably, however, network inference was remarkably fast, completing the task in approximately 178 s for a dataset comprising 650 frames of 413 volume images with signal duration of 20μs. This processing speed is roughly thirty times faster than a parallelized implementation of the traditional time exposure acoustics algorithm on the same GPU device. This would open a new door for PCI application in the real-time monitoring of ultrasound ablation.

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A novel transcranial ultrasound imaging method with diverging wave transmission and deep learning approach

Cited by 6 publications

References 28 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Transcranial Acoustic Metamaterial Parameters Inverse Designed by Neural Networks

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging

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