see also Low-intensity pulsed ultrasound.

In biological tissues, it is known that the creation of gas bubbles (cavitation) during ultrasound exposure is more likely to occur at lower rather than higher frequencies.

Oscillating sound pressure wave with a frequency greater than the upper limit of the human hearing range. Ultrasound is thus not separated from 'normal' (audible) sound by differences in physical properties, only by the fact that humans cannot hear it. Although this limit varies from person to person, it is approximately 20 kilohertz (20,000 hertz) in healthy, young adults. Ultrasound devices operate with frequencies from 20 kHz up to several gigahertz.

Ultrasound images (sonograms) are made by sending a pulse of ultrasound into tissue using an ultrasound transducer (probe). The sound reflects and echoes off parts of the tissue; this echo is recorded and displayed as an image to the operator.

Ultrasound is a popular imaging modality for providing the neurosurgeon with real-time updated images of brain tissue. Interpretation of post-resection ultrasound images is difficult due to large brain shift and tissue resection.

Furthermore, several factors degrade the quality of post-resection ultrasound images such as the strong reflection of waves at the interface of saline water and brain tissue in resection cavities, air bubbles and the application of blood-clotting agents around the edges of resection.

Image registration allows the comparison of post-resection ultrasound images with higher quality preresection images, assists in interpretation of post-resection images and may help identify residual tumor, and as such, is of significant clinical importance. In a paper, Zhou et al propose a nonrigid symmetric registration (NSR) framework for accurate alignment of pre- and post-resection volumetric ultrasound images in near real-time. They first formulate registration as minimization of a regularized cost function, and analytically derive its derivative to efficiently optimize the cost function. An outlier detection algorithm is proposed and utilized in this framework to identify non-corresponding regions (outliers) and therefore improve the robustness and accuracy of registration. They use an Efficient Second-order Minimization (ESM) method for fast and robust optimization. Furthermore, we exploit a symmetric and inverseconsistent method to generate realistic deformation fields. The results show that NSR significantly improves the quality of the alignment between pre- and post-resection ultrasound images 1).

Compared to other prominent methods of medical imaging, ultrasonography has several advantages. It provides images in real-time (rather than after an acquisition or processing delay), it is portable and can be brought to a sick patient's bedside, it is substantially lower in cost, and it does not use harmful ionizing radiation. Drawbacks of ultrasonography include various limits on its field of view including difficulty imaging structures behind bone, and its relative dependence on a skilled operator.

Many different types of images can be formed using ultrasound. The most well-known type is a B-mode image, which displays a two-dimensional cross-section of the tissue being imaged. Other types of image can display blood flow, motion of tissue over time, the location of blood, the presence of specific molecules, the stiffness of tissue, or the anatomy of a three-dimensional region. Ultrasound can also be used therapeutically, to break up gallstones and kidney stones or to heat and destroy diseased or cancerous tissue.

see 3D Ultrasound.

see Cerebral ultrasound

see Contrast enhanced ultrasound

see Doppler ultrasound

see Point-of-care ultrasound (POCUS)

Ultrasound aspirator.

A neurosurgical phantom-based training system with ultrasound simulation represents a promising approach for the future training of neurosurgeons. It aims to improve surgical skill training by creating a more realistic simulation in a non-risk environment. Hence, it could help to bridge the gap between theoretical and practical training with the potential to benefit both physicians and patients 2).

Zhou H, Rivaz H. Registration of Pre- and Post-resection Ultrasound Volumes with Non-corresponding Regions in Neurosurgery. IEEE J Biomed Health Inform. 2016 Apr 14. [Epub ahead of print] PubMed PMID: 27101626.
Müns A, Mühl C, Haase R, Möckel H, Chalopin C, Meixensberger J, Lindner D. A neurosurgical phantom-based training system with ultrasound simulation. Acta Neurochir (Wien). 2014 Jun;156(6):1237-43. doi: 10.1007/s00701-013-1918-3. Epub 2013 Oct 23. PubMed PMID: 24150189.
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