Abstract

Conventional ultrasound B-mode imaging is mainly qualitative in nature. While conventional imaging techniques, including ultrasound, may be sensitive to the detection of anomalous tissue features, the ability to classify these tissues often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. Quantitative ultrasound (QUS) imaging techniques can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging techniques can encompass a wide variety of techniques including spectral-based parameterization, elastography, flow estimation and envelope statistics. Furthermore, a goal of QUS imaging techniques is to provide system- and operator-independent parameters related to tissue properties. Different applications of QUS imaging techniques in diagnostic ultrasound will be discussed in this paper. Specifically, spectral-based techniques and envelope statistics at clinical frequencies and at high ultrasonic frequencies (> 15 MHz) will be examined for their abilities to improve diagnostic ultrasound. Spectral-based techniques include the estimation of the backscatter coefficient, estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer size and the concentration of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges related to the implementation of QUS imaging techniques and recent successes of QUS implementation for medical diagnostics will be discussed. Challenges for clinical application include correctly accounting for attenuation effects and implementation of QUS on clinical devices. Successful applications demonstrating the ability of QUS to improve medical diagnostics will include cancer detection and classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of thermal therapy on solid tumors.

Full Text
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