In this article, we demonstrate a piezoelectric micromachined diaphragm device based on PMN-PT single crystal thin layer. The electrical properties of the device are characterized. A finite element analysis is carried out for the fabricated model. Harmonic analysis is performed to investigate the vibrational modalities of the diaphragm. The FEA results show very good agreement with the experimental data. As an application example, a diaphragm device is coated with thiol carboxylic PEG (MW 5,000DA) as a resonator for bio mass sensing. The devices are also useful for many other applications including acoustic sensing, ultrasonic transducing, and vibration sensing, etc.
Restriction by hardware caused the very low projection number at a single phase for 4-dimensional cone beam (4D-CBCT) CT imaging, and reconstruction using conventional reconstruction algorithms is thus constrained by serious streak artifacts and noises. To address this problem, we propose an approach to reconstructing 4D-CBCT images with multi-phase projections based on the assumption that the image at one phase can be viewed as the motion-compensated image at another phase. Specifically, we formulated a cost function using multi-phase projections to construct the fidelity term and the TV regularization method. For fidelity term construction, the projection data of the current phase and those at other phases were jointly used by reformulating the imaging model. The Gradient-Projection-Barzilai-Line search (GPBL) method was used to optimize the complex cost function. Physical phantom and patient data results showed that the proposed approach could effectively reduce the noise and artifacts, and the introduction of additional temporal correlation did not introduce new artifacts or motion blur.
Being low-level radiation exposure and less harmful to health, low-dose computed tomography (LDCT) has been widely adopted in the early screening of lung cancer and COVID-19. LDCT images inevitably suffer from the degradation problem caused by complex noises. It was reported that, compared with commercial iterative reconstruction methods, deep learning (DL)-based LDCT denoising methods using convolutional neural network (CNN) achieved competitive performance. Most existing DL-based methods focus on the local information extracted by CNN, while ignoring both explicit non-local and context information (which are leveraged by radiologists). To address this issue, we propose a novel deep learning model named radiologist-inspired deep denoising network (RIDnet) to imitate the workflow of a radiologist reading LDCT images. Concretely, the proposed model explicitly integrates all the local, non-local and context information rather than local information only. Our radiologist-inspired model is potentially favoured by radiologists as a familiar workflow. A double-blind reader study on a public clinical dataset shows that, compared with state-of-the-art methods, our proposed model achieves the most impressive performance in terms of the structural fidelity, the noise suppression and the overall score. As a physicians-inspired model, RIDnet gives a new research roadmap that takes into account the behavior of physicians when designing decision support tools for assisting clinical diagnosis. Models and code are available at this https URL.
Abstract BackgroundNonalcoholic fatty liver disease (NAFLD) is rapidly becoming one of the most common liver diseases. Ultrasound elastography has been used for the diagnosis of NAFLD. However, clinical research on steatosis by elastography technology has mainly focused on steatosis with fibrosis or non-alcoholic steatohepatitis (NASH), while steatosis without fibrosis has been poorly studied. Moreover, the relationship between liver viscoelasticity and steatosis grade is not clear. In this study, we evaluated the degree of liver steatosis in a simple steatosis rat model using shear wave elastography (SWE). ResultsThe viscoelasticity values of 69 rats with hepatic steatosis were measured quantitatively by SWE in vivo and validated by a dynamic mechanical analysis (DMA) test. Pathological sections were used to determine the steatosis grading for each rat. The results showed that the elasticity values obtained by the two methods followed the same trend, and the elasticity is significantly correlated with liver steatosis. The Pearson’s correlation coefficients indicate that elasticity obtained by SWE is positively linear correlated with DMA (r = 0.628, p = 7.85×10 -9 ). The combined Voigt elasticity measurements have high validity in the prediction of steatosis (S0 vs S1-S4), with an AUROC of 0.755 (95% CI = 0.6175-0.8925, p < 0.01) and the optimal cutoff value was 2.08 kPa with a sensitivity of 78% and specificity of 63%.ConclusionSWE might have the feasibility to be introduced as an auxiliary technique for NAFLD patients in clinical settings. However, the viscosity results measured by the two methods are significantly different because the two methods work in different frequency bands.
Prostate cancer has a poor prognosis and high mortality rate due to metastases. Extracellular matrix (ECM) re-modelling and stroma composition have been linked to cancer progression, including key components of cell migration, tumour metastasis, and tissue modulus. Moreover, collagens are one of the most significant components of the extracellular matrix and have been ascribed to many aspects of neoplastic transformation. This study characterises collagen re-modelling around localised prostate cancer using the second harmonic generation of collagen (SHG), genotyping and ultrasound shear wave elastography (USWE) measured modulus in men with clinically localised prostate cancer. Tempo-sequence assay for gene expression of COL1A1 and COL3A1 was used to confirm the expression of collagen. Second-harmonic generation imaging and genotyping of ECM around prostate cancer showed changes in content, orientation, and type of collagen according to Gleason grades (cancer aggressivity), and this correlated with the tissue modulus measured by USWE in kilopascals. Furthermore, there were clear differences between collagen orientation and type around normal and cancer tissues.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)