Deep learning in ultrasound elastography imaging: A review

Li, Hongliang; Bhatt, Manish; Qu, Zhen; Zhang, Shiming; Hartel, Martin C.; Khademhosseini, Ali; Cloutier, Guy

doi:10.1002/mp.15856

Cited by 11 publications

(6 citation statements)

References 204 publications

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“…[ 31 , 32 ] The underlying mechanism of elastography is the characterization of tissue stiffness using ultrasound imaging. [ 33 ] In this study, compared with the DL-grayscale model based on grayscale ultrasound images, the DL-elastography model based on elastography images achieved better diagnostic performance. This phenomenon demonstrates that elastography images could provide added value for improving the diagnostic performance of DL models.…”

Section: Discussionmentioning

confidence: 90%

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Wang,

Zhao,

Wan

et al. 2023

Medicine

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Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used to classify SLNM in breast cancer. Clinical and ultrasound data of 317 patients diagnosed with breast cancer at the Second Affiliated Hospital of Nanchang University were collected from January 2018 to December 2021 and randomly divided into training and internal validation cohorts at a ratio of 7:3. An external validation cohort comprising data from Nanchang Third Hospital with 42 patients collected. Three DL models, namely DL-grayscale, DL-CDFI, and DL-elastography, were proposed to predict SLNM by analyzing grayscale ultrasound, CDFI, and elastography images. Three DL models were compared and evaluated to assess diagnostic performance based on the area under the curve (AUC). The AUCs of the DL-grayscale were 0.855 and 0.788 in the internal and external validation cohorts, respectively. For the DL-CDFI model, the AUCs were 0.761 and 0.728, respectively. The diagnostic performance of DL-elastography was superior to that of the DL-grayscale and DL-CDFI. The AUC of the DL-elastography model was 0.879 in the internal validation cohort, with a classification accuracy of 86.13%, sensitivity of 91.60%, and specificity of 82.79%. The generalization capability of DL-elastography remained high in the external cohort, with an AUC of 0.876, and an accuracy of 85.00%. DL radiomics can be used to classify SLNM in breast cancer using ultrasound images. The proposed DL-elastography model based on elastography images achieved the best diagnostic performance and holds good potential for the management of patients with SLNM.

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Section: Discussionmentioning

confidence: 90%

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Wang,

Zhao,

Wan

et al. 2023

Medicine

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“…Even though the clinical significance of this study is limited in comparison with studies were Young's modulus, Poisson s ratio, and others parameters, are estimated [30] [32], the rotation elastogram is a direct estimation of the macro-mechanical behavior of benign tumors as fibroadenomas and may be used as a complementary parameter to improve the breast tumor diagnosis using ultrasound elastography. Furthermore, the use of machine and deep learning methods can improve tissue motion estimation, which can become relevant for enhancing the quality of RE image [33].…”

Section: Discussionmentioning

confidence: 99%

Rotation Elastogram Estimation Using Mechanical Assisted Spatial Compounding: An Experimental Validation Study

Zagaglioni,

Trejo,

Espíndola

et al. 2024

IEEE Access

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In quasi-static ultrasound elastography, breast tumor classification can be performed by using the rotation fill-in signature present in the rotation elastogram. This rotation fill-in signature is a benign tumors marker obtained from lesion rotation as a product of the lateral asymmetric stress field. However, the well-known low lateral resolution limitation of ultrasound imaging devices reduces the image quality of the rotation elastograms. Studies using beam-steering, synthetic transmit aperture (STA), diverging beam with STA, and sub-pitch translation of the ultrasonic beam methods have shown that the rotation elastogram's quality can be significantly improved. In this context, we aim to study the feasibility of improving the rotation elastogram quality by mechanical-assisted spatial compounding of displacement images from different ultrasound probe angles. Recently, through numerical simulations, we have shown the theoretical feasibility of this technique. Here, we present the corresponding experimental validation by using tissue-mimicking gelatin phantoms. Our experimental results show that the contrast-to-noise ratio of the rotation elastogram can be improved by approximately 5 dB by increasing the number of displacement images used in the spatial compounding for small scanning angles of about 1 • . This result confirms the prediction of previous numerical simulations. In addition, we show that the estimation of rotation can be used, in conjunction with the shear strain, to compute a new parameter to quantify the rotation possibility of the inclusion with respect to its shearing nature. In conclusion, our method is technically feasible but requires an exhaustive ultrasound probe position control in synchronism with the ultrasound image acquisition process.INDEX TERMS Quasi-static Ultrasound Elastography, Rotation elastogram, Shear strain elastogram, Spatial compounding.

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“…Cancer detection, liver fibrosis assessment, breast imaging, musculoskeletal disorders, cardiovascular health [75][76][77] Acoustic Radiation Force Imaging (ARFI)…”

Section: Description Working Applications Refs Ultrasound Imagingmentioning

confidence: 99%

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Singh,

Nayak,

Behl

et al. 2024

Diagnostics

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To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth’s subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

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Deep learning in ultrasound elastography imaging: A review

Cited by 11 publications

References 204 publications

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Rotation Elastogram Estimation Using Mechanical Assisted Spatial Compounding: An Experimental Validation Study

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

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