Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images 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. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) 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 for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

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Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound

Due to parallel advances in signal processing and computer hardware in the last 15 years, quantitative ultrasound techniques have reached maturity, allowing for the construction of quantitative maps or images of soft tissues. This book will focus on 5 modern research topics related to quantitative ultrasound of soft tissues: - Spectral-based methods for tissue characterization, tissue typing, cancer detection, etc.; - Envelope statistics analysis as a means of quantifying and imaging tissue properties; - Ultrasound elastography for quantifying elastic properties of tissues (several clinical ultrasound scanners now display elastography images); - Scanning acoustic microscopy for forming images of mechanical properties of soft tissues with micron resolution (desktop size scanners are now available); and - Ultrasound computer tomography for breast cancer imaging (new ultrasound tomography systems have been developed and are currently under evaluation clinically).

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Quantitative Ultrasound in Soft Tissues

Ultrasound (US) spectral analysis methods are used to analyze the radiofrequency (RF) data collected from cell pellets exposed to chemotherapeutics that induce apoptosis and other chemicals that induce nuclear transformations. Calibrated backscatter spectra from regions-of-interest (ROI) were analyzed using linear regression techniques to calculate the spectral slope and midband fit. Two f/2 transducers, with operating frequencies of 30 and 34 MHz (relative bandwidths of 93% and 78%, respectively) were used with a custom-made imaging system that enabled the collection of the raw RF data. For apoptotic cells, the spectral slope increased from 0.37 dB/MHz before drug exposure to 0.57 dB/MHz 24 h after, corresponding to a change in effective scatterer radius from 8.7 to 3.2 m. The midband fit increased in a time-dependent fashion, peaking at 13dB 24 h after exposure. The statistical deviation of the spectral parameters was in close agreement with theoretical predictions. The results provide a framework for using spectral parameter methods to monitor apoptosis in in vitro and in in vivo systems and are being used to guide the design of system and signal analysis parameters. (E-mail: mkolios@ryerson.ca) © 2002 World Federation for Ultrasound in Medicine & Biology.

Ultrasonic spectral parameter characterization of apoptosis.

Ultrasonic speckle formation, analysis and processing applied to tissue characterization

Ultrasonic spectrum analysis for tissue evaluation

A study was performed to find and test quantitative methods of analysing echographic signals for the differentiation of diffuse liver diseases. An on-line data acquisition system was used to acquire radiofrequency (RF) echo signals from volunteers and patients. Several methods to estimate the frequency-dependent attenuation coefficient were evaluated, in which a correction for the frequency and depth-dependent diffraction and focusing effects caused by the sound beam was applied. Using the estimated value of the attenuation coefficient the RF signals themselves were corrected to remove the depth dependencies caused by the sound beam and by the frequency-dependent attenuation. After this preprocessing the envelope of the corrected RF signals was calculated and B-mode images were reconstructed. The texture was analysed in the axial direction by first- and second-order statistical methods. The accuracy and precision of the attenuation methods were assessed by using computer simulated RF signals and RF data obtained from a tissue-mimicking phantom. The phantom measurements were also used to test the performance of the methods to correct for the depth dependencies.

http://iopscience.iop.org/article/10.1088/0031-9155/36/8/002/pdf

R L Romijn

Papers

Ultrasound attenuation and texture analysis of diffuse liver disease methods and preliminary results