Ernest J. Feleppa

Theoretical framework for spectrum analysis in ultrasonic tissue characterization.

An analytic model is described for application in ultrasonic tissue characterization. The model is applicable to clinical broadband pulse echo systems. It treats spectra derived from received echo signals and relates them to physical tissue properties. The model can be applied to deterministic tissue structures (e.g., retinal detachments, larger blood vessels, and surface layers of the kidney) and to stochastic tissue structures (e.g., various tumors). The beam patterns included in the model are those generated by focused transducers typically used in high-resolution clinical ultrasound. Appropriate calibration procedures are also treated; these are needed for interpretation of absolute spectral parameters. The results obtained with the analytic model have been used to design a digital processing system and the associated techniques which are now being applied during examinations of the eye and abdominal organs. The results have proven useful in interpreting data from various types of tissues. To illustrate the application of these results, representative clinical data, obtained from the digital system, are presented for two types of tissue architectures. The first case is a detached retina representing a deterministic structure characterized by well-defined thickness and reflection coefficients. The second case is asteroid hyalosis and represents a stochastic entity in which the positions of small scattering particles are best described in statistical terms, and characterization is accompanied by means of normalized power spectra.

Relationship of Ultrasonic Spectral Parameters to Features of Tissue Microstructure

Absfract-&mlytical studies have been conducted to investigate the importance of specific tissue features in determining ultrasonic spectral “signatures” that have proven to be diagnostically useful. Three models of tissue microstructure were considered, and calibrated power spectra were computed for a series of scatterer sizes, concentrations, and acoustic impedances. General results were obtained, then specific parameters were employed to simulate studies of the eye and liver. These results were compared to clinical data-base information, and excellent correspondence was found. Studies using acoustic microscopy, test objects, and clinical data are continuing to refine the analytical model. I. INTRODUCTION U

Statistical framework for ultrasonic spectral parameter imaging

This study examines the statistics of ultrasonic spectral parameter images that are being used to evaluate tissue microstructure in several organs. The parameters are derived from sliding-window spectrum analysis of radiofrequency echo signals. Calibrated spectra are expressed in dB and analyzed with linear regression procedures to compute spectral slope, intercept and midband fit, which is directly related to integrated backscatter. Local values of each parameter are quantitatively depicted in gray-scale cross-sectional images to determine tissue type, response to therapy and physical scatterer properties. In this report, we treat the statistics of each type of parameter image for statistically homogeneous scatterers. Probability density functions are derived for each parameter, and theoretical results are compared with corresponding histograms clinically measured in homogeneous tissue segments in the liver and prostate. Excellent agreement was found between theoretical density functions and data histograms for homogeneous tissue segments. Departures from theory are observed in heterogeneous tissue segments. The results demonstrate how the statistics of each spectral parameter and integrated backscatter are related to system and analysis parameters. These results are now being used to guide the design of system and analysis parameters, to improve assays of tissue heterogeneity and to evaluate the precision of estimating features associated with effective scatterer sizes and concentrations.

Comparison of theoretical scattering results and ultrasonic data from clinical liver examinations

A theoretical analysis of soft-tissue ultrasonic scattering has been used to formulate specific results describing spectral parameters for tissue characterization. Results are applicable to clinical liver examinations. Three spectral parameters are mathematically expressed in terms of acoustic attenuation and the effective sizes, concentrations, and relative acoustic impedances of tissue scatters. Results from a clinical data base are shown to agree well with analytical results for each spectral parameter. Agreement is found for: spectral shapes; effects of attenuation; and correlations between parameters. Images of three spectral parameters are presented and their gray-scale features are evaluated with reference to analytical results.

Ultrasonic spectrum analysis for tissue evaluation

Spectrum analysis procedures have been developed to improve upon the diagnostic capabilities afforded by conventional ultrasonic images. These procedures analyze the frequency content of broadband, coherent echo signals returned from the body. They include calibration procedures to remove system artifacts and thereby provide quantitative measurements of tissue backscatter. Several independent spectral parameters have been used to establish databases for various organs; several investigations have shown that these parameters can be used with statistical classifiers to identify tissue type. Locally computed spectra have been used to generate sets of images displaying independent spectral parameters. Stained images have been derived by analyzing these parameter images with statistical classifiers and using color to denote tissue type (e.g., cancer). This report describes spectrum analysis procedures, discusses how measured parameters are related to physical tissue properties, and summarizes results describing estimator precision. It also presents illustrative clinical results showing how such procedures are being adapted to address specific clinical problems for a number of organs. This report indicates where further developments are needed and suggests how these techniques may improve image segmentation for three-dimensional displays and volumetric assays.

Three-Dimensional High-Frequency Backscatter and Envelope Quantification of Cancerous Human Lymph Nodes

Quantitative imaging methods using high-frequency ultrasound (HFU) offer a means of characterizing biological tissue at the microscopic level. Previously, high-frequency, 3-D quantitative ultrasound (QUS) methods were developed to characterize 46 freshly-dissected lymph nodes of colorectal-cancer patients. 3-D ultrasound radiofrequency data were acquired using a 25.6 MHz center-frequency transducer and each node was inked before tissue fixation to recover orientation after sectioning for 3-D histological evaluation. Backscattered echo signals were processed using 3-D cylindrical regions-of-interest (ROIs) to yield four QUS estimates associated with tissue microstructure (i.e., effective scatterer size, acoustic concentration, intercept and slope). These QUS estimates, obtained by parameterizing the backscatter spectrum, showed great potential for cancer detection. In the present study, these QUS methods were applied to 112 lymph nodes from 77 colorectal and gastric cancer patients. Novel QUS methods parameterizing the envelope statistics of the ROIs using Nakagami and homodyned-K distributions were also developed; they yielded four additional QUS estimates. The ability of these eight QUS estimates to classify lymph nodes and detect cancer was evaluated using receiver operating characteristics (ROC) curves. An area under the ROC curve of 0.996 with specificity and sensitivity of 95% were obtained by combining effective scatterer size and one envelope parameter based on the homodyned-K distribution. Therefore, these advanced 3-D QUS methods potentially can be valuable for detecting small metastatic foci in dissected lymph nodes.

Ultrasonic spectral-parameter imaging of the prostate

Spectrum analysis of the radiofrequency echo signals obtained from ultrasonically scanning the prostate may provide information capable of distinguishing cancerous from noncancerous tissue. In American men, prostate cancer is the highest‐incidence cancer and the second‐highest cancer killer. It is diagnosed using ultrasonically guided biopsies, which are limited by the low sensitivity and specificity of the guidance method. Spectrum analysis of the echo signals uses information that is discarded by conventional ultrasound imaging technology. The inclusion of this information shows differences between the ultrasound‐scattering properties of cancerous and noncancerous prostate tissues. Spectrum analysis of ultrasonic echoes provides parameter values that can be related to scattering properties of tissue and can be compared to database parameter value ranges associated with cancerous and noncancerous tissues. Images can be generated to display parameter values, scatterer properties, or most likely tissue type. Results to date suggest that these differences may be sufficient to improve biopsy guidance significantly and therefore to improve the efficacy of biopsy‐based diagnosis of prostate cancer.

Recent developments in tissue-type imaging (TTI) for planning and monitoring treatment of prostate cancer.

Because current methods of imaging prostate cancer are inadequate, biopsies cannot be effectively guided and treatment cannot be effectively planned and targeted. Therefore, our research is aimed at ultrasonically characterizing cancerous prostate tissue so that we can image it more effectively and thereby provide improved means of detecting, treating and monitoring prostate cancer. We base our characterization methods on spectrum analysis of radiofrequency (rf) echo signals combined with clinical variables such as prostate-specific antigen (PSA). Tissue typing using these parameters is performed by artificial neural networks. We employed and evaluated different approaches to data partitioning into training, validation, and test sets and different neural network configuration options. In this manner, we sought to determine what neural network configuration is optimal for these data and also to assess possible bias that might exist due to correlations among different data entries among the data for a given patient. The classification efficacy of each neural network configuration and data-partitioning method was measured using relative-operating-characteristic (ROC) methods. Neural network classification based on spectral parameters combined with clinical data generally produced ROC-curve areas of 0.80 compared to curve areas of 0.64 for conventional transrectal ultrasound imaging combined with clinical data. We then used the optimal neural network configuration to generate lookup tables that translate local spectral parameter values and global clinical-variable values into pixel values in tissue-type images (TTIs). TTIs continue to show cancerous regions successfully, and may prove to be particularly useful clinically in combination with other ultrasonic and nonultrasonic methods, e.g., magnetic-resonance spectroscopy.

Three-dimensional High-frequency Characterization of Cancerous Lymph Nodes

High-frequency ultrasound (HFU) offers a means of investigating biologic tissue at the microscopic level. High-frequency, three-dimensional (3-D) quantitative-ultrasound (QUS) methods were developed to characterize freshly-dissected lymph nodes of cancer patients. Three-dimensional ultrasound data were acquired from lymph nodes using a 25.6-MHz center-frequency transducer. Each node was inked prior to tissue fixation to recover orientation after sectioning for 3-D histologic evaluation. Backscattered echo signals were processed using 3-D cylindrical regions-of-interest to yield four QUS estimates associated with tissue microstructure (i.e., effective scatterer size, acoustic concentration, intercept and slope). QUS estimates were computed following established methods using two scattering models. In this study, 46 lymph nodes acquired from 27 patients diagnosed with colon cancer were processed. Results revealed that fully-metastatic nodes could be perfectly differentiated from cancer-free nodes using slope or scatterer-size estimates. Specifically, results indicated that metastatic nodes had an average effective scatterer size (i.e., 37.1 +/- 1.7 microm) significantly larger (p < 0.05) than that in cancer-free nodes (i.e., 26 +/- 3.3 microm). Therefore, the 3-D QUS methods could provide a useful means of identifying small metastatic foci in dissected lymph nodes that might not be detectable using current standard pathology procedures.

Differentiation of breast tumors by ultrasonic tissue characterization.

The ability of ultrasonic tissue characterization to differentiate and classify benign and malignant breast tissues in vivo in patients with palpable breast masses and in vitro in excised breast tissue was evaluated. One‐hundred and twenty‐four in vivo and 89 in vitro studies were performed using a technique of UTC based on parameters from the power spectrum of backscattered echoes. Sensitivities and specificities for diagnosing carcinoma were 86 and 84% for in vivo studies and 94 and 92% for in vitro studies. These UTC parameters provided threshold values for color‐coding breast lesion images. The results of this preliminary investigation suggest that UTC provides a basis for assessing more accurately lesions suspected of being malignant prior to biopsy and possibly for evaluating breast lesions noninvasively.