An Ultrasonic Elastography Method Based on Variable Length of Filter in Strain Computation

Zhu, Xun; Chen, Li; Liu, Shanna; Fang, Kai; Wu, Ruoyu; Tu, Guojin; Liu, Jianlin; Shen, Yuqiang; Zhou, Qingli

doi:10.1007/s11220-021-00335-5

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…In the UE system, the tissue with small elasticity will change greatly after being compressed and will turn red, otherwise the tissues will be blue. The modified UE 5-point method [ 13 ] was used to evaluate the benign and malignant lesions of suspected lesions: the overall or large area of the lesion was green = 1 point: the central area and surrounding area were blue and green, = 2 points: the inner area of the lesion was blue = 3 points: the area ratio of green and white was close to 1 : 1 = 4 points: the lesion is blue as a whole, or with a little green area scattered inside = 5 points: the lesion area and surrounding are blue, with or without green area inside =, respectively.…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Correlation Analysis of Ultrasound Elastography Score with Invasive Breast Cancer and Biological Prognostic Factors

Wan

Qin

et al. 2022

Contrast Media & Molecular Imaging

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Invasive breast cancer (IBC) is a kind of malignant tumor in which cancer cells have broken through the basement membrane of breast ducts or lobular acini and invaded the stroma. Although ultrasound elastography score (UES) has shown unique advantages in the diagnosis of IBC, its value in the prognosis is not clear. Here, we explored the correlation of UES with IBC and biological prognostic factors. The datum of 86 patients with suspected IBC from January 2018 to December 2021 was collected. UE was applied in the examination of all patients. The lesion tissue of the malignant group was punctured to detect and analyze the expression of biological prognostic factors, including estrogen receptor (E receptor), progesterone receptor (P receptor), and human epidermal growth factor 2 (HER factor 2) and Ki67. The differences in UES under different biological prognostic factors were compared. The receiver operating characteristic (ROC) curve was applied to analyze the diagnostic value of UES of IBC and the expression of biological prognostic factors. Based on the pathological diagnosis results, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of UES in the diagnosis of IBC were analyzed. The correlation of UES with IBC and biological prognostic factors was analyzed by multiple linear regression and Spearman method. ROC analysis showed that the area under the curve of UES for diagnosing IBC and evaluating the expression of P receptor, HER factor 2, and Ki67 were 0.877, 0.704, 0.763, and 0.820, respectively (P＜0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of UES when diagnosing IBC were 92.42%, 90.00%, 91.86%, 96.83%, and 78.26%, respectively. The UES of E receptor expression (positive and negative group) showed no obvious variance ( P > 0.05). The UES of P receptors (positive and negative), HER factor 2 (positive and negative), and Ki67 (high and low expression) showed obvious differences ( P < 0.05). Multiple linear regression and Spearman indicated UES was significantly correlated with the expression of P receptor, HER factor 2, and Ki67 (P＜0.05). UES has a certain diagnostic value for IBC and is significantly correlated to the expression of P receptor, HER factor 2, and Ki67, which is helpful for evaluating the prognosis of patients with IBC.

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Section: Methodsmentioning

confidence: 99%

[Retracted] Correlation Analysis of Ultrasound Elastography Score with Invasive Breast Cancer and Biological Prognostic Factors

Wan

Qin

et al. 2022

Contrast Media & Molecular Imaging

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“…In elastography, how to minimize the influence of noise on the reconstructed elastic property distribution is highly challenging. We usually filter the noisy data to reduce the influence of noise [36][37][38]. However, DL approach provides an alternative way to address this issue.…”

Section: Discussionmentioning

confidence: 99%

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Mei,

Deng,

Zhao

et al. 2024

CMES

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Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues. The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing. To address this issue, we propose a deep learning (DL) model based on conditional Generative Adversarial Networks (cGANs) to improve the quality of nonhomogeneous shear modulus reconstruction. To train this model, we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution. Both the simulated and experimental displacement fields are used to validate the proposed method. The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method. Moreover, we emphasize that our DL model is only trained on synthetic data. This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography. Overall, this work addresses several fatal issues in applying the DL technique into elastography, and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.

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An Ultrasonic Elastography Method Based on Variable Length of Filter in Strain Computation

Cited by 2 publications

References 28 publications

[Retracted] Correlation Analysis of Ultrasound Elastography Score with Invasive Breast Cancer and Biological Prognostic Factors

[Retracted] Correlation Analysis of Ultrasound Elastography Score with Invasive Breast Cancer and Biological Prognostic Factors

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

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