The viscoelastic properties of lung tissue are of interest in medicine as they have been shown to be affected by various pathologies. Identifying the mechanical properties of lung tissue first requires a means of quantitatively measuring phenomena, such as mechanical wave motion, that are affected by these properties. In the present study, lung surface motion is measured on excised pig lungs to determine suitable viscoelastic models. The relation between the surface wave speed and the frequency is analyzed and different viscoelastic models are used to fit this relation. Also a more comprehensive method to evaluate the frequency-dependent shear modulus of the pig lung measuring the propagation of surface waves on the surface of the lung is presented and viscoelastic models (both of integer and fractional order) are compared to experimental results over the frequency range of 100–500 Hz.
Moyamoya disease (MMD) is a rare cerebrovascular occlusive disease with progressive stenosis of the terminal portion of internal cerebral artery (ICA) and its main branches, which can cause complications, such as high risks of disability and increased mortality. Accurate and timely diagnosis may be difficult for physicians who are unfamiliar to MMD. Therefore, this study aims to achieve a preoperative deep-learning-based evaluation of MMD by detecting steno-occlusive changes in the middle cerebral artery or distal ICA areas.A fine-tuned deep learning model was developed using a three-dimensional (3D) coordinate attention residual network (3D CA-ResNet). This study enrolled 50 preoperative patients with MMD and 50 controls, and the corresponding time of flight magnetic resonance angiography (TOF-MRA) imaging data were acquired. The 3D CA-ResNet was trained based on sub-volumes and tested using patch-based and subject-based methods. The performance of the 3D CA-ResNet, as evaluated by the area under the curve (AUC) of receiving-operator characteristic, was compared with that of three other conventional 3D networks.With the resulting network, the patch-based test achieved an AUC value of 0.94 for the 3D CA-ResNet in 480 patches from 10 test patients and 10 test controls, which is significantly higher than the results of the others. The 3D CA-ResNet correctly classified the MMD patients and normal healthy controls, and the vascular lesion distribution in subjects with the disease was investigated by generating a stenosis probability map and 3D vascular structure segmentation.The results demonstrated the reliability of the proposed 3D CA-ResNet in detecting stenotic areas on TOF-MRA imaging, and it outperformed three other models in identifying vascular steno-occlusive changes in patients with MMD.
Previous studies of the first author and others have focused on low audible frequency (<1 kHz) shear and surface wave motion in and on a viscoelastic material comprised of or representative of soft biological tissue. A specific case considered has been surface (Rayleigh) wave motion caused by a circular disk located on the surface and oscillating normal to it. Different approaches to identifying the type and coefficients of a viscoelastic model of the material based on these measurements have been proposed. One approach has been to optimize coefficients in an assumed viscoelastic model type to match measurements of the frequency-dependent Rayleigh wave speed. Another approach has been to optimize coefficients in an assumed viscoelastic model type to match the complex-valued frequency response function (FRF) between the excitation location and points at known radial distances from it. In the present article, the relative merits of these approaches are explored theoretically, computationally, and experimentally. It is concluded that matching the complex-valued FRF may provide a better estimate of the viscoelastic model type and parameter values; though, as the studies herein show, there are inherent limitations to identifying viscoelastic properties based on surface wave measurements.
Abstract Background To analyze the differences in clinical features and computed tomography characteristics in the two types of mixed epithelial and stromal tumor of the kidney (MESTK) and to establish a treatment plan for the MESTK types. Methods 17 patients underwent multidetector computed tomography (MDCT) before surgery and had a pathological diagnosis of MESTK were enrolled. Their clinical information (R.E.N.A.L.Nephrometry Score (R.E.N.A.L.－NS), radical nephrectomy (RN), partial nephrectomy (PN), etc.) were collected. The radiological features included renal sinus fat invagination (SFI), maximal diameter (MD), capsule and septa of the tumor, etc. were also analyzed. They were divided into two types according to the MD solid /MD tumor ratio (type A with > 63%; type B with ≤ 63%). An independent-sample t-test and Fisher exact test were used to assess the differences between the two groups. Results MESTKs demonstrated a variable multi-septate cystic and solid components with a delayed enhancement. There were 9 patients for type A and 8 subjects for type B. Compared with type A, the lesions in type B have larger MD (79.13±39.06 vs. 41.22±24.19, p = 0.028), higher R.E.N.A.L.－NS (10.03±0.50 vs. 8.95±1.26, P＜0.001), higher RN (75.00% vs.22.22%, p =0.015), larger SFI (87.5% vs.33.3%, p=0.05), more septa (100% vs. 0%, p <0.001) and more capsule (100% vs. 11.1%, p < 0.001). Conclusion Type B MESTK has more hazardous features compared with type A, suggesting that RN is more suitable for type B and PN for type A.
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