In this paper we evaluate tissue elasticity as a longstanding but qualitative biomarker for prostate cancer and sonoelastography as an emerging imaging tool for providing qualitative and quantitative measurements of prostate tissue stiffness. A Kelvi

A novel quantitative sonoelastography technique for assessing the viscoelastic properties of skeletal muscle tissue was developed. Slowly propagating shear wave interference patterns (termed crawling waves) were generated using a two-source configuration vibrating normal to the surface. Theoretical models predict crawling wave displacement fields, which were validated through phantom studies. In experiments, a viscoelastic model was fit to dispersive shear wave speed sonoelastographic data using nonlinear least-squares techniques to determine frequency-independent shear modulus and viscosity estimates. Shear modulus estimates derived using the viscoelastic model were in agreement with that obtained by mechanical testing on phantom samples. Preliminary sonoelastographic data acquired in healthy human skeletal muscles confirm that high-quality quantitative elasticity data can be acquired in vivo. Studies on relaxed muscle indicate discernible differences in both shear modulus and viscosity estimates between different skeletal muscle groups. Investigations into the dynamic viscoelastic properties of (healthy) human skeletal muscles revealed that voluntarily contracted muscles exhibit considerable increases in both shear modulus and viscosity estimates as compared to the relaxed state. Overall, preliminary results are encouraging and quantitative sonoelastography may prove clinically feasible for in vivo characterization of the dynamic viscoelastic properties of human skeletal muscle.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called “characteristic vectors“) from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the “characteristic vectors”were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.

Elastography is a technique that maps relative tissue stiffness. Ultrasonographic (US) elastography (sonoelastography) is a novel modality that is the subject of active research for clinical applications, primarily breast and prostate lesion imaging. Breast and prostate tumors generally have biomechanical properties different from those of normal tissues: Tumors are usually stiffer. This phenomenon is responsible for tissue contrast on elastograms. For the prostate gland and breast, the main image acquisition techniques are vibration sonoelastography and compression sonoelastography. The sonoelastographic appearances of several common breast lesions, including fibroadenomas, simple and complex cysts, ductal carcinomas, malignant lymph nodes, and hematomas, are reviewed. In addition, the US elastographic appearances of the normal prostate gland, prostate carcinomas, and benign prostate hyperplasia are illustrated. Potential pitfalls in the interpretation of elastograms, including false-positive and false-negative images, are illustrated. These imaging findings are derived from ongoing research because sonoelastography is not yet accepted for routine clinical use.