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

Abstract

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.

Fig. 1

(Left) Illustration of the planar reflector technique and (right) illustration of the reference phantom technique.

Fig. 2

Ultrasound grey-scale B-mode images superimposed with estimates of ESD for (left) rat fibroadenomas, (center) mouse carcinomas, and (right) mouse sarcomas. Figure reproduced from [5].

Fig. 3

Feature analysis plot of the ESD versus μ versus k parameter. Figure from [5].

Fig. 4

QUS images of thyroids enhanced by ESD (left column) and EAC (right column). The top row is normal thyroid (no tumor observed), the second row is a C-cell adenoma, the third row is PTC, and the last row is a FTC.

Fig. 5

QUS images utilizing a level of cancer suspicion metric overlaid on B-mode images of the prostate. (Reproduced from [79])

Fig. 6

Illustrative results obtained with a non-metastatic lymph node (a and b) and a nearly entirely metastatic lymph node (c and d). a) and c): parametric cross-sectional images displaying effective scatterer-size estimates. b) and d) histologic thin section approximately corresponding to a) and c), respectively. Metastatic region is highlighted in green in d) and segmentation results are shown by the green and red highlights in a) and c). (Adapted from [102]).

Fig. 7

Illustrative Lymph Explorer screen capture showing cancer probabilities. Results were obtained from a partially-metastatic lymph node of a colorectal-cancer patient. In the three QUS images, regions highlighted in red indicate cancer probability greater than 75%; those in green indicate a cancer probability smaller than 25% and those in orange indicate a cancer probability between 25% and 75%. In the co-registered histology image, the green outline indicates the metastatic region. (Adapted from [102,103])

Fig. 8

Backscatter coefficients from liver samples extracted from animals on different diets with (solid line) zero days on fatty diet, (dashed line) three weeks on fatty diet, and (dot-dashed line) six weeks on fatty diet.

Fig. 9

B-mode and parametric images at different times during the HIFU treatment on rat R1.

Fig. 10

Examples of Structure Factor Size and Attenuation Estimator parametric images of red blood cell aggregation (W, longitudinal views) in case of low (left) and high inflammation (right). Data were acquired within the pump circuit deviation (top) and the femoral vein (bottom). The color maps vary from 0 (blue) to 40 (red). Variable W has no unit. LPS = lipopolysaccharide; CPB = cardiopulmonary bypass. (Adapted from [143]).

Fig 11

Schematic representation of aggregates treated as individual scatterers. The aggregates of red blood cells in blood (left side) are assumed to be homogeneous particles (right side) with effective properties that depend on the internal hematocrit and on the density and compressibility of the red blood cells within them. (Adapted from [146]).