Comparison of template-matching and singular-spectrum-analysis methods for imaging implanted brachytherapy seeds.

Brachytherapy using small implanted radioactive seeds is becoming an increasingly popular method for treating prostate cancer, in which a radiation oncologist implants seeds in the prostate transperineally under ultrasound guidance. Dosimetry software determines the optimal placement of seeds for achieving the prescribed dose based on ultrasonic determination of the gland boundaries. However, because of prostate movement and distortion during the implantation procedure, some seeds may not be placed in the desired locations; this causes the delivered dose to differ from the prescribed dose. Current ultrasonic imaging methods generally cannot depict the implanted seeds accurately. We are investigating new ultrasonic imaging methods that show promise for enhancing the visibility of seeds and thereby enabling real-time detection and correction of seed-placement errors during the implantation procedure. Real-time correction of seed-placement errors will improve the therapeutic radiation dose delivered to target tissues. In this work, we compare the potential performance of a template-matching method and a previously published method based on singular spectrum analysis for imaging seeds. In particular, we evaluated how changes in seed angle and position relative to the ultrasound beam affect seed detection. The conclusion of the present study is that singular spectrum analysis has better sensitivity but template matching is more resistant to false positives; both perform well enough to make seed detection clinically feasible over a relevant range of angles and positions. Combining the information provided by the two methods may further reduce ambiguities in determining where seeds are located.

Ultrasonic multi-feature analysis procedure for computer-aided diagnosis of solid breast lesions.

We have developed quantitative descriptors to provide an objective means of noninvasive identification of cancerous breast lesions. These descriptors include quantitative acoustic features assessed using spectrum analysis of ultrasonic radiofrequency (rf) echo signals and morphometric properties related to lesion shape. Acoustic features include measures of echogenicity, heterogeneity and shadowing, computed by generating spectral-parameter images of the lesion and surrounding tissue. Spectral-parameter values are derived from rf echo signals at each pixel using a sliding-window Fourier analysis. We derive quantitative acoustic features from spectral-parameter maps of the lesion and adjacent areas. We quantify morphometric features by geometric and fractal analysis of traced lesion boundaries. Initial results on biopsy-proven cases show that although a single parameter cannot reliably discriminate cancerous from noncancerous breast lesions, multi-feature analysis provides excellent discrimination for this data set. We have processed data for 130 biopsy-proven patients, acquired during routine ultrasonic examinations at three clinical sites and produced an area under the receiver-operating-characteristics (ROC) curve of 0.947 +/- 0.045. Among the quantitative descriptors, lesion-margin definition, spiculation and border irregularity are the most useful; some additional morphometric features (such as border irregularity) also are particularly effective in lesion classification. Our findings are consistent with many of the BI-RADS (Breast Imaging Reporting and Data System) breast-lesion-classification criteria in use today.

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.

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.

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.

Ultrasonic spectrum-analysis and neural-network classification as a basis for ultrasonic imaging to target brachytherapy of prostate cancer.

Conventional B-mode ultrasound is the standard means of imaging the prostate for guiding prostate biopsies and planning brachytherapy of prostate cancer. Yet B-mode images do not allow adequate visualization of cancerous lesions of the prostate. Ultrasonic tissue-typing imaging based on spectrum analysis of radiofrequency echo signals has shown promise for overcoming the limitations of B-mode imaging for visualizing prostate tumors. Tissue typing based on radiofrequency spectrum analysis uses nonlinear methods, such as neural networks, to classify tissue by using spectral-parameter and clinical-variable values. Two- and three-dimensional images based on these methods show potential for improving the guidance of prostate biopsies and the targeting of radiotherapy of prostate cancer. Two-dimensional images have been imported into instrumentation for real-time biopsy guidance and into commercial dose-planning software for brachytherapy planning. Three-dimensional renderings seem to be capable of depicting locations and volumes of cancer foci.