Elastic nonlinearity imaging.

Previous work has demonstrated improved diagnostic performance of highly trained breast radiologists when provided with B-mode plus elastography images over B-mode images alone. In those studies we have observed that elasticity imaging can be difficult to perform if there is substantial motion of tissue out of the image plane. So we are extending our methods to 3D/4D elasticity imaging with 2D arrays. Further, we have also documented the fact that some breast tumors change contrast with increasing deformation and those observations are consistent with in vitro tissue measurements. Hence, we are investigating imaging tissue stress-strain nonlinearity. These studies will require relatively large tissue deformations (e.g., > 20%) which will induce out of plane motion further justifying 3D/4D motion tracking. To further enhance our efforts, we have begun testing the ability to perform modulus reconstructions (absolute elastic parameter) imaging of in vivo breast tissues. The reconstructions are based on high quality 2D displacement estimates from strain imaging. Piecewise linear (secant) modulus reconstructions demonstrate the changes in elasticity image contrast seen in strain images but, unlike the strain images, the contrast in the modulus images approximates the absolute modulus contrast. Nonlinear reconstructions assume a reasonable approximation to the underlying constitutive relations for the tissue and provide images of the (near) zero-strain shear modulus and a nonlinearity parameter that describes the rate of tissue stiffening with increased deformation. Limited data from clinical trials are consistent with in vitro measurements of elastic properties of tissue samples and suggest that the nonlinearity of invasive ductal carcinoma exceeds that of fibroadenoma and might be useful for improving diagnostic specificity. This work is being extended to 3D.

Linear and nonlinear elasticity imaging of soft tissue in vivo: demonstration of feasibility.

We establish the feasibility of imaging the linear and nonlinear elastic properties of soft tissue using ultrasound. We report results for breast tissue where it is conjectured that these properties may be used to discern malignant tumors from benign tumors. We consider and compare three different quantities that describe nonlinear behavior, including the variation of strain distribution with overall strain, the variation of the secant modulus with overall applied strain and finally the distribution of the nonlinear parameter in a fully nonlinear hyperelastic model of the breast tissue.

Differentiating benign from malignant solid breast masses with US strain imaging.

PURPOSE: To prospectively evaluate the sensitivity and specificity of ultrasonographic (US) strain imaging for distinguishing between benign and malignant solid breast masses, with biopsy results as the reference standard. MATERIALS AND METHODS: The study was institutional review board approved and HIPAA compliant. Informed consent was obtained from all participating patients. US strain imaging of 403 breast masses was performed. The 50 malignant and 48 benign lesions (in patients aged 19-83 years; mean age, 49 years +/- 17 [standard deviation]) with the highest quality were selected for the reader study. Three observers blinded to the pathologic outcomes first described the B-mode image findings by using US Breast Imaging Reporting and Data System descriptors and derived a probability of malignancy. They then updated the probability by assessing strain images. Receiver operating characteristic (ROC) curves were constructed by using these probabilities. Areas under the ROC curve, sensitivities, and specificities were calculated and compared. Interobserver variability and the correlation between automated and subjective image quality assessment were analyzed. RESULTS: The average area under the ROC curve for all three readers after US strain imaging (0.903) was greater than that after B-mode US alone (0.876, P = .014). With use of a 2% probability of malignancy threshold, strain imaging-as compared with B-mode US alone-had improved average specificity (0.257 vs 0.132, P < .001) and high sensitivity (0.993 vs 0.987, P > .99). Significant interobserver variability was observed (P < .001). The ability to assess strain image quality appeared to correlate with the highest observer performance. CONCLUSION: US strain imaging can facilitate improved classification of benign and malignant breast masses. However, interobserver variability and image quality influence observer performance.

In vitro uterine strain imaging: preliminary results.

OBJECTIVE: Uterine abnormalities, such as leiomyomas, endometrial polyps, and adenomyosis, are often clinically associated with irregular uterine bleeding. These abnormalities can have similar B-mode characteristics but require different treatment. The objective of this study was to develop diagnostic techniques based on ultrasound strain imaging that would allow in vivo visualization and characterization of endometrial and myometrial uterine abnormalities, enabling physicians to improve diagnosis and treatment. METHODS: Ultrasound strain imaging was performed on 29 uteri removed via elective hysterectomy. An ultrasound system with a linear array transducer was used to obtain radio frequency echo data during manual freehand compressions of the tissue. Radio frequency data were post-processed with a 2-dimensional block-matching algorithm to generate strain images. RESULTS: In the uteri involved in this study, there were 19 leiomyomas, 1 case of adenomyosis, and 3 endometrial polyps observed on strain imaging. Leiomyomas appeared stiffer than the surrounding normal myometrium in strain images and were characterized by a slipping artifact at their boundary. Endometrial polyps appeared softer than the normal surrounding myometrium. The average strain contrast in small leiomyomas (<1.5 cm) compared to the myometrium was 1.75 +/- 1.14; the strain contrast was 2.50 +/- 1.15 in large leiomyomas and 0.40 +/- 0.05 in endometrial polyps. Leiomyoma strain contrast results were consistent with modulus contrast values from mechanical testing results. CONCLUSIONS: Ultrasound strain imaging can differentiate between endometrial polyps and leiomyomas. More data are necessary to validate these results and to ascertain whether other uterine abnormalities can also be differentiated.

A novel image formation method for ultrasonic strain imaging.

This paper presents a new method for forming high-quality ultrasonic strain images. To achieve this goal, three radiofrequency echo frames are selected by an automated performance assessment method and used to generate two parent strain images located in the same physical grid from which a high quality composite strain image may be calculated by averaging. The automatic performance evaluation method combines the consistency among the two parent strain images and the accuracy of motion tracking into a single summary "displacement quality measure." The proposed algorithm is evaluated with datasets acquired from in vivo breast tissue data. Our results show that that the proposed strain formation method shows substantial potential to outperform other methods available in the literature.

A novel performance descriptor for ultrasonic strain imaging: a preliminary study.

Ultrasonic strain imaging that uses signals from conventional diagnostic ultrasound systems is capable of showing the contrast of tissue elasticity, which provides new diagnostically valuable information. To assess and improve the diagnostic performance of ultrasonic strain imaging, it is essential to have a quantitative measure of image quality. Moreover, it is useful if the image quality measure is simple to interpret and can be used for visual feedback while scanning and as a training tool for operator performance evaluation. This report describes the development of a novel quantitative method for systematic performance assessment that is based on the combination of measures of the accuracy of motion tracking and consistency among consecutive strain fields. The accuracy of motion tracking assesses the reliability of strain images. The consistency among consecutive strain images assesses the signal quality in strain images. The clinical implications of the proposed method to differentiate good or poor strain images are discussed. Results of experiments with tissue-mimicking phantoms and in vivo breast-tissue data demonstrate that the performance measure is a useful method for automatically rating elasticity image quality.