Andrew Kalisz

Radiation-force technique to monitor lesions during ultrasonic therapy.

This report describes a monitoring technique for high-intensity focused ultrasound (US), or HIFU, lesions, including protein-denaturing lesions (PDLs) and those made for noninvasive cardiac therapy and tumor treatment in the eye, liver and other organs. Designed to sense the increased stiffness of a HIFU lesion, this technique uniquely utilizes the radiation force of the therapeutic US beam as an elastographic push to detect relative stiffness changes. Feasibility was demonstrated with computer simulations (treating acoustically induced displacements, concomitant heating, and US displacement-estimation algorithms) and pilot in vitro experimental studies, which agree qualitatively in differentiating HIFU lesions from normal tissue. Detectable motion can be induced by a single 5 ms push with temperatures well below those needed to form a lesion. Conversely, because the characteristic heat diffusion time is much longer than the characteristic relaxation time following a push, properly timed multiple therapy pulses will form lesions while providing precise control during therapy.

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.

Statistics of ultrasonic spectral parameters for prostate and liver examinations

A theoretical analysis was performed to describe statistical characteristics of calibrated spectral parameters used for ultrasonic tissue evaluation in the prostate and liver. The analysis assumes that radiofrequency (rf) echo signals exhibit Gaussian statistics. It derives the probability density function (pdf) of spectral parameters that are computed using sliding-window analysis techniques. The analysis relates the standard deviations of linear-regression spectral-parameter estimates to system and analysis parameters including bandwidth, center frequency, and the length of the sliding analysis window. The analysis also derives the pdf for mid-band fit parameter images. Theoretical results are found to agree well with clinical data from homogeneous segments in liver and prostate. The results offer a basis for evaluating spectral-estimator precision and for conducting future studies of lesion detectability based on spectral features.

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.

Spectrum-Analysis and Neural Networks for Imaging to Detect and Treat Prostate Cancer

Conventional B-mode ultrasound currently is the standard means of imaging the prostate for guiding prostate biopsies and planning brachytherapy to treat prostate cancer. Yet B-mode images do not adequately display cancerous lesions of the prostate. Ultrasonic tissue-type imaging based on spectrum analysis of radiofrequency (rf) echo signals has shown promise for overcoming the limitations of B-mode imaging for visualizing prostate tumors. This method of tissue-type imaging utilizes nonlinear classifiers, such as neural networks, to classify tissue based on values of spectral parameter and clinical variables. Two- and three-dimensional images based on these methods demonstrate potential for guiding prostate biopsies and targeting radiotherapy of prostate cancer. Two-dimensional images are being generated in real time in ultrasound scanners used for real-time biopsy guidance and have been incorporated into commercial dosimetry software used for brachytherapy planning. Three-dimensional renderings show promise for depicting locations and volumes of cancer foci for disease evaluation to assist staging and treatment planning, and potentially for registration or fusion with CT images for targeting external-beam radiotherapy.

Study of ultrasonic contrast agents using a dual-frequency band technique

We have developed a dual-frequency band technique to study frequency-dependent phenomena associated with ultrasonic contrast agents. Our technique uses a superimposed high-frequency (10 MHz) broad-band ultrasound (US) pulse to investigate contrast agent interaction with a low-frequency (e.g., 0.5 MHz) ultrasonic field. Our digitally controlled system has the ability to produce two colinear, confocal US pulses at different center frequencies, to adjust the relative phasing and pulse repetition frequency of each pulse, and to acquire digital backscatter data. A series of experimental studies demonstrated that the high-frequency backscatter signal responded to several phenomena induced in contrast agent particles by the low-frequency beam. These phenomena included radial pulsations, nonlinear oscillations and depletion. Initial results also demonstrated a relative phase shift between the high- and low-frequency signals; this shift is due to a difference in sound velocity at these frequencies, and it may convey information about the contrast agent concentration.

ROLE OF ADVANCED 2 AND 3-DIMENSIONAL ULTRASOUND FOR DETECTING PROSTATE CANCER

PURPOSE We explored the clinical usefulness of spectrum analysis and neural networks for classifying prostate tissue and identifying prostate cancer in patients undergoing transrectal ultrasound for diagnostic or therapeutic reasons. MATERIALS AND METHODS Data on a cohort of 215 patients who underwent transrectal ultrasound guided prostate biopsies at Memorial-Sloan Kettering Cancer Center, New York, New York were included in this study. Radio frequency data necessary for 2 and 3-dimensional (D) computer reconstruction of the prostate were digitally recorded at transrectal ultrasound and prostate biopsy. The data were spectrally processed and 2-D tissue typing images were generated based on a pre-trained neural network classification. We used manually masked 2-D tissue images as building blocks for generating 3-D tissue images and the images were tissue type color coded using custom software. Radio frequency data on the study cohort were analyzed for cancer probability using the data set pre-trained by neural network methods and compared with conventional B-mode imaging. ROC curves were generated for the 2 methods using biopsy results as the gold standard. RESULTS The mean area under the ROC curve plus or minus SEM for detecting prostate cancer for the conventional B-mode and neural network methods was 0.66 +/- 0.03 and 0.80 +/- 0.05, respectively. Sensitivity and specificity for detecting prostate cancer by the neural network method were significantly increased compared with conventional B-mode imaging. In addition, the 2 and 3-D prostate images provided excellent visual identification of areas with a higher likelihood of cancer. CONCLUSIONS Spectrum analysis could significantly improve the detection and evaluation of prostate cancer. Routine real-time application of spectrum analysis may significantly decrease the number of false-negative biopsies and improve the detection of prostate cancer at transrectal ultrasound guided prostate biopsy. It may also provide improved identification of prostate cancer foci during therapeutic intervention, such as brachytherapy, external beam radiotherapy or cryotherapy. In addition, 2 and 3-D images with prostate cancer foci specifically identified can help surgical planning and may in the distant future be an additional reliable noninvasive method of selecting patients for prostate biopsy.

Computer model of ultrasonic hyperthermia and ablation for ocular tumors using b-mode data

Computer simulations have been conducted to examine hyperthermia and ablation for treating ocular tumors. An interactive software package has been implemented that permits relevant tissue dimensions to be determined from B-mode data. This package also permits interactive beam positioning, and it provides image displays depicting computed absorbed doses and temperature rises. Results are presented showing how hyperthermia temperature patterns are influenced by beam position, beam geometry and frequency. Images showing ablative temperature rises at various time intervals are also presented. For hyperthermia, geometric models of beam profiles showed that a non-uniform beam pattern (with a central low-intensity region) can produce more uniform heating of small ocular tumors than a beam with a uniform intensity profile. For a given tumor, the uniformity of hyperthermia temperatures was found to be a function of frequency, with 4.75 MHz providing reasonably uniform results for typical tumor heights (near 7 mm). For ablation, diffraction computations were employed to calculate beam intensity profiles; results show an initially rapid rise in temperature levels with subsequent, slower heating beyond the -3-dB limits of the focal volume. The model is now being refined, and additional phenomena, including nonlinear propagation, will be incorporated.

Roles of hematocrit and fibrinogen in red cell aggregation determined by ultrasonic scattering properties

Parameters of the power spectrum of backscattered echoes were applied to quantitatively evaluate red cell aggregation in vitro. Human red cell suspensions were circulated in a closed loop of tubing, and ultrasonic, radiofrequency, echo-signal data were obtained using a 10-MHz transducer. Data acquisition was performed at 30-s to 1-min intervals for 5 min after flow stoppage. Two parameters of the normalized power spectrum of the echo signals, spectral slope and Y-intercept, were computed, and estimates of two scattering properties, the scatterer size and acoustic concentration were calculated from these parameters using equations based on scattering theory. Size and acoustic concentration were observed as they changed over time after the stoppage of flow. The key findings were that hematocrit affected the rate of cell aggregation while fibrinogen controlled aggregate size and acoustic concentration.