Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and linear discriminant analysis, each pixel of the image is classified as belonging to one of the three regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression.

Transcranial Doppler ultrasound (TCD) is a method that uses a hand held low frequency (2-2.5 MHz), pulsed Doppler phased array probe to measure blood velocity within the arteries located inside the brain. The problem with TCD lies in the low ultrasonic energy penetrating inside the brain through the skull which leads to low signal to noise ratio. This is due to several effects including phase aberration, variations in the speed of sound in the skull, scattering, the acoustic impedance mismatch and absorption of the three layer medium constituted by soft tissues, the skull and the brain. The goal of this paper is to study the effect of transmission losses due to the acoustic impedance mismatch on the transmitted energies as a function of frequency. To do so, wave propagation was modelled from the ultrasonic transducer into the brain. This model calculates transmission coefficients inside the brain, leading to a frequency-dependent transmission coefficient for a given skin and bone thickness. This approach was validated experimentally by comparing the analytical results with measurements obtained from a bone phantom plate mimicking the skull. The average position error of the occurrence of the maximum amplitude between the experiment and analytical result was equivalent to a 0.06 mm error on the skin thickness given a fixed bone thickness. Similarity between the experimental and analytical result was also demonstrated by calculating correlation coefficients. The average correlation between the experimental and analytical result came out to be 0.50 for a high frequency probe and 0.78 for a lower frequency probe. Further analysis of the simulation showed that an optimized excitation frequency can be chosen based on skin and bone thicknesses, thereby offering an opportunity to improve the image quality of TCD.

The use of mechanically representative phantoms is important for experimental validation in ultrasound (US) imaging, elastography, and image registration. This article proposes a model to predict the elastic modulus of a soft tissue-mimicking phantom based on two very easily controllable parameters: gelatin concentration and refrigeration duration. The model has been validated on small- and large-scale phantoms; it provides a good prediction of the elastic modulus in both cases (error < 16.2%). The tissue-mimicking phantom is made following a low-cost and simple fabrication procedure using commercial household gelatin with psyllium hydrophilic mucilloid fiber to obtain echogenicity. A large range of elastic properties was obtained (15-100kPa) by adjusting the gelatin concentration between 5% and 20% (g/mL) and the refrigeration time of the sample between 2 and 168 h, allowing to mimic normal and pathological human soft tissues. The phantom's acoustic properties (velocity, attenuation, and acoustic impedance) are also assessed using the American Institute of Ultrasound in Medicine (AIUM) standard.