• The Proposed multi-microjet impact model suitable for high hydrostatic pressures. • Cavitation erosion experiments can be conducted in an ultrasonic cavitation system composed of a spherical cavity focused transducer with high acoustic energy in a sub-wavelength focal region. • Quantitative analysis of cavitation pit morphology can be achieved through the proposed microjet impact model. • A microjet velocity inversion model was proposed to quantitative evaluate the cavitation erosion intensity.

Objectives: To develop and apply magnetic resonance imaging (MRI) parameter-based machine learning (ML) models to predict non-perfused volume (NPV) reduction and residual regrowth of uterine fibroids after high-intensity focused ultrasound (HIFU) ablation. Methods: MRI data of 573 uterine fibroids in 410 women who underwent HIFU ablation from the Chongqing Haifu Hospital (training set, N = 405) and the First Affiliated Hospital of Chongqing Medical University (testing set, N = 168) were retrospectively analyzed. Fourteen MRI parameters were screened for important predictors using the Boruta algorithm. Multiple ML models were constructed to predict NPV reduction and residual fibroid regrowth in a median of 203.0 (interquartile range: 122.5–367.5) days. Furthermore, optimal models were used to plot prognostic prediction curves. Results: Fourteen features, including postoperative NPV, indicated predictive ability for NPV reduction. Based on the 10-fold cross-validation, the best average performance of multilayer perceptron achieved with R² was 0.907. In the testing set, the best model was linear regression (R² =0.851). Ten features, including the maximum thickness of residual fibroids, revealed predictive power for residual fibroid regrowth. Random forest model achieved the best performance with an average area under the curve (AUC) of 0.904 (95% confidence interval (CI), 0.869–0.939), which was maintained in the testing set with an AUC of 0.891 (95% CI, 0.850–0.929). Conclusions: ML models based on MRI parameters can be used for prognostic prediction of uterine fibroids after HIFU ablation. They can potentially serve as a new method for learning more about ablated fibroids.

Objectives: To develop and evaluate magnetic resonance imaging (MRI)‐based radiomics models for predicting residual myoma regrowth within 1 year after high‐intensity focused ultrasound (HIFU) ablation of uterine myomas.Methods: A retrospective analysis of residual myoma regrowth within 1 year was performed on 428 myomas in 339 patients who were diagnosed with uterine myoma and treated with HIFU ablation in two hospital centers. In total, 851 radiomics features were extracted from T2‐weighted images (T2WI) obtained 1 day after HIFU ablation, and the least absolute shrinkage and selection operator in the training cohort (n = 243) was employed to select radiomics features. Support vector machines were adopted to develop radiomics, clinicoradiological and combined radiomics–clinical models to predict residual myoma regrowth, defined as an increase in residual myoma volume of > 10% between that at day 1 post HIFU and that at follow‐up MRI within 1 year. These models were validated in both internal (n = 81) and external (n = 104) test cohorts. The predictive performance and clinical application of these models were assessed using receiver‐operating‐characteristics‐curve analysis, the area under the curve (AUC) and decision‐curve analysis.Results: The AUCs of the T2WI‐based radiomics prediction model in the internal and external test cohorts were 0.834 (95% CI, 0.747–0.920) and 0.801 (95% CI, 0.712–0.889), respectively, and those of the clinicoradiological model were 0.888 (95% CI, 0.816–0.960) and 0.912 (95% CI, 0.851–0.973), respectively. The combined model had better predictive performance than either the radiomics or the clinicoradiological model, with AUC values of 0.922 (95% CI, 0.857–0.987) and 0.930 (95% CI, 0.880–0.980) in the internal and external test cohorts, respectively. Decision‐curve analysis also indicated that application of the combined model has clinical value, this model achieving more net benefits than the other two models.Conclusion: T2WI‐based radiomics features can predict effectively the occurrence of residual myoma regrowth within 1 year after HIFU ablation of uterine myomas, which serves as an accurate and convenient reference for clinical decision‐making. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Cavitation erosion is a general phenomenon in the fields of aviation, navigation, hydraulic machinery, and so on, causing great damage to fluid machinery. With the vast requirements in deep ocean applications, it is urgent to study the mechanism of cavitation erosion and the cavitation erosion resistance of different materials under high hydrostatic pressure to predict and avoid the effect of cavitation erosion. In this work, the spatially confined cavitation bubble cloud associated with Gaussian-like intensity distribution sonoluminescence (SL) was produced by a spherically focused ultrasound transducer with two opening ends near metallic plates under different hydrostatic pressures (0.1, 3, 6, and 10 MPa). The cavitation erosion effects on copper, 17-4PH stainless steel and tungsten plates were studied. Through coupling analysis towards the SL intensity distribution, the macro/micro morphology of cavitation erosion, and the physical parameters of different metallic materials (hardness, yield strength, and melting point), it is found that with increasing hydrostatic pressure, the erosion effect is intensified, the depth of cavitation pits increases, the phenomenon of melting can be observed on materials with relatively low melting points, and the cavitation erosion experienced an evolution process from high-temperature creep to fracture. This work has also established a method for the evaluation of materials' cavitation erosion resistance with measurable SL intensity distribution, which is promising to promote the designing and selection of anti-cavitation materials in deep-sea applications.

Background: Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning. Materials and methods: This retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics–clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively. Results: A total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics–clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693–0.954) and external (AUC = 0.773, 95% CI 0.647–0.902) test cohorts, and the DCA also suggested the radiomics–clinical model had the highest net benefit. Conclusions: The radiomics–clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.

The near-solid wall multi-bubble cavitation is an extremely complex phenomenon, and cavitation has strong erosiveness. The melting point (about 3410 °C) of tungsten is highest among all pure metals, and its hardness is also very high (its yield strength is greater than 1 GPa). What would happen to pure tungsten wire under extreme conditions caused by collapsing cavitation bubbles at high hydrostatic pressure? In this paper, we have studied the fracture process of pure tungsten wire with diameter of 0.2 mm mounted at the focus of a standing acoustic wave produced by a spherical cavity transducer with two open ends placed in a near spherical pressure container, and also studied the macro and micro morphological characteristics of the fracture and the surface damage at different fracture stages of tungsten wire under various hydrostatic pressures and driving electric powers. The results have shown that the fracture time of tungsten wire is inversely proportional to avitation intensity with hydrostatic pressure and driving electric power, the higher the acoustic pressure caused by higher electric power, the shorter the fracture time. The possible fracture mechanisms of tungsten wire in this situation we found mainly contributed to asymmetrically bubbles collapse near the surface of tungsten wire, leading to tearing the surface apart; consequently cracks along the radial and axial directions of a tungsten wire extend simultaneously, classified as trans-granular fracture and inter-granular fracture, respectively. With the increase of cavitation intensity, the cracks tend to extend more radially and the axial crack propagation path becomes shorter, that is, mainly for trans-granular fracture; with the decrease of cavitation intensity, intergranular fracture becomes more obvious. When the hydrostatic pressure was 10 MPa and the driving electric power was 2 kW, the fibers became softener due to the fracture of the tungsten wire. The fracture caused by acoustic cavitation was different from conventional mechanical fracture, such as tensile, shear, fatigue fracture, on macro and micro morphology.