Quantitative Ultrasound in Cancer Imaging

Abstract

Ultrasound is a relatively inexpensive, portable, and versatile imaging modality that has a broad range of clinical uses. It incorporates many imaging modes, such as conventional gray-scale "Bmode" imaging to display echo amplitude in a scanned plane; M-mode imaging to track motion at a given fixed location over time; duplex, color, and power Doppler imaging to display motion in a scanned plane; harmonic imaging to display non-linear responses to incident ultrasound; elastographic imaging to display relative tissue stiffness; and contrast-agent imaging with simple contrast agents to display blood-filled spaces or with targeted agents to display specific agentbinding tissue types. These imaging modes have been well described in the scientific, engineering, and clinical literature. A less well-known ultrasonic imaging technology is based on quantitative ultrasound or (QUS), which analyzes the distribution of power as a function of frequency in the original received echo signals from tissue and exploits the resulting spectral parameters to characterize and distinguish among tissues. This article discusses the attributes of QUS-based methods for imaging cancers and providing improved means of detecting and assessing tumors. The discussion will include applications to imaging primary prostate cancer and metastatic cancer in lymph nodes to illustrate the methods.Ultrasound has become a very commonly used clinical imaging modality because it is relatively inexpensive, portable, and versatile; it is an imaging modality that has found widespread acceptance in a broad range of applications ranging from screening through biopsy guidance to treatment targeting and therapy monitoring. Clinical applications of ultrasound incorporate many imaging modes, such as conventional gray-scale "B-mode" imaging to display echo amplitude in a scanned plane; M-mode imaging to track motion at a given fixed location over time; duplex, color, and power Doppler imaging to display motion in a scanned plane; harmonic imaging to display non-linear responses to incident ultrasound; elastographic imaging to display relative tissue stiffness; and contrast-agent imaging with simple contrast agents to display blood-filled spaces or with targeted agents to display specific agent-binding tissue types. These imaging modes have been well described in the scientific, engineering, and clinical literature over the past several decades.1 -9 A less wellknown ultrasonic imaging technology is based on quantitative ultrasound or (QUS), which analyzes the distribution of power as a function of frequency in the received echo signals backscattered from tissue; QUS exploits the resulting spectral parameters to characterize and distinguish among tissues.

Variation of ultrasound image lateral spectrum with assumed speed of sound and true scatterer density

“…However, the increasing contrast in sharpness for higher mean scatterer distances suggests a novel approach of estimating scatterer concentration. Compared to existing methods that take the axial spectrum of the image using one assumed speed of sound [28][29][30], the proposed approach would compare the contrast in lateral spectrum obtained between several candidate speeds of sound, in effect implying that intentionally blurring the image may help to uncover more information about the underlying structure of the investigated medium.…”

Multiparametric Monitoring of Chemotherapy Treatment Response in Locally Advanced Breast Cancer Using Quantitative Ultrasound and Diffuse Optical Spectroscopy

“…QUS uses the spectral information of radiofrequency (RF) signals that are typically discarded in conventional grey-scale sonography [ Figure 1]. The spectral information of the RF signal is retained and processed by applying a Fourier transform to the signal to compute a frequency-dependent power spectrum [24]. QUS parameters, such as the mid-band fit (MBF), 0-MHz intercept (SI) and spectral slope (SS) are determined by applying a linear regression function within a discrete frequency bandwidth of the computed power spectrum [24][25][26][27].…”

“…The spectral information of the RF signal is retained and processed by applying a Fourier transform to the signal to compute a frequency-dependent power spectrum [24]. QUS parameters, such as the mid-band fit (MBF), 0-MHz intercept (SI) and spectral slope (SS) are determined by applying a linear regression function within a discrete frequency bandwidth of the computed power spectrum [24][25][26][27]. In early studies by Lizzi et al, www.impactjournals.com/oncotarget QUS parameters were studied for therapy response monitoring in hyperthermia-treated ocular tumors [27].…”

Feasibility and Reproducibility of Fetal Lung Texture Analysis by Automatic Quantitative Ultrasound Analysis and Correlation with Gestational Age

“…This approach is based on an image processing method to the already acquired ultrasound picture, which estimates tissue characteristics by means of a quantitative analysis. The use of quantitative texture analysis in ultrasound has previously been investigated for medical diagnostic applications, including breast cancer [13,14,15,16] and liver disease [17,18,19]. There are few references in the literature on the assessment of fetal lung texture by quantitative ultrasound tissue characterization.…”