S. Kaisar Alam

Elastography : Imaging the Elastic Properties of Soft Tissues with Ultrasound

Elastography is a method that can ultimately generate several new kinds of images, called elastograms. As such, all the properties of elastograms are different from the familiar properties of sonograms. While sonograms convey information related to the local acoustic backscatter energy from tissue components, elastograms relate to its local strains, Youngs moduli or Poissons ratios. In general, these elasticity parameters are not directly correlated with sonographic parameters, i.e. elastography conveys new information about internal tissue structure and behavior under load that is not otherwise obtainable. In this paper we summarize our work in the field of elastography over the past decade. We present some relevant background material from the field of biomechanics. We then discuss the basic principles and limitations that are involved in the production of elastograms of biological tissues. Results from biological tissues in vitro and in vivo are shown to demonstrate this point. We conclude with some observations regarding the potential of elastography for medical diagnosis.

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.

Reduction of signal decorrelation from mechanical compression of tissues by temporal stretching: Applications to elastography

Elastography is based on the estimation of strain due to tissue compression. Strain is computed from the estimates of time delays between gated precompression and postcompression echo signals. Time delay estimates are obtained from the location of the peak of the crosscorrelation function between gated precompression and postcompression signals. It is of paramount importance to accurately estimate the time delays for good quality elastograms. A main source of time delay estimation (TDE) error in elasticity imaging is the decorrelation of the echo signal as a result of tissue compression (decorrelation noise). The effect of decorrelation on the mean of the crosscorrelation function and the correlation coefficient has been investigated. The expected value of the cross-correlation function between the precompression and postcompression signals was shown to be a filtered version of the autocorrelation function of the precompression signal. In this article, the effect of temporal stretching of the postcompression echo signal on the cross-correlation function will be investigated along the same line. The applied compression is assumed to be uniform; the decorrelations introduced by the lateral and elevational tissue movements are ignored. The theory predicts that if the postcompression echo signals are stretched before the TDE step, then for small strains, the cross-correlation function very closely resembles the autocorrelation function. For larger strains, correlation is improved if temporal stretching is applied. The theory is corroborated by results from simulation and homogeneous phantom experiments. Thus, the decorrelation noise in elastograms can be reduced by temporal stretching of the postcompression signal.

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.

A Robust Heart Rate Monitoring Scheme Using Photoplethysmographic Signals Corrupted by Intense Motion Artifacts

Goal: Although photoplethysmographic (PPG) signals can monitor heart rate (HR) quite conveniently in hospital environments, trying to incorporate them during fitness programs poses a great challenge, since in these cases, the signals are heavily corrupted by motion artifacts. Methods: In this paper, we present a novel signal processing framework which utilizes two channel PPG signals and estimates HR in two stages. The first stage eliminates any chances of a runaway error by resorting to an absolute criterion condition based on ensemble empirical mode decomposition. This stage enables the algorithm to depend very little on the previously estimated HR values and to discard the need of an initial resting phase. The second stage, on the other hand, increases the algorithms robustness against offtrack errors by using recursive least squares filters complemented with an additional novel technique, namely time-domain extraction. Results: Using this framework, an average absolute error of 1.02 beat per minute (BPM) and standard deviation of 1.79 BPM are recorded for 12 subjects performing a run with peak velocities reaching as high as 15 km/h. Conclusion: The performance of this algorithm is found to be better than the other recently reported algorithms in this field such as TROIKA and JOSS. Significance: This method is expected to greatly facilitate the presently available wearable gadgets in HR computation during various physical activities.

On the use of envelope and RF signal decorrelation as tissue strain estimators

Bamber and Bush (1995) used the correlation coefficient for freehand elasticity imaging. Varghese and Ophir (1996) found it to be a biased estimator of strain with a large variability. In this study, we systematically investigate the effect of changes in various system and processing parameters on the performance of the correlation coefficient strain estimator, and demonstrate, using simulated data, that noise and frequency-dependent attenuation can introduce variable bias in this estimator.

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.

Adaptive spectral strain estimators for elastography

In conventional elastography, internal tissue deformations, induced by external compression applied to the tissue surface, are estimated by cross-correlation analysis of echo signals obtained before and after compression. Conventionally, strains are estimated by computing the gradient of estimated displacement. However, gradient-based algorithms are highly susceptible to noise and decorrelation, which could limit their utility. We previously developed strain estimators based on a frequency-domain (spectral) formulation that were shown to be more robust but less precise compared to conventional strain estimators. In this paper, we introduce a novel spectral strain estimator that estimates local strain by maximizing the correlation between the spectra of pre- and postcompression echo signals using iterative frequency-scaling of the latter; we also discuss a variation of this algorithm that may be computationally more efficient but less precise. The adaptive spectral strain estimator combines the advantages of time- and frequency-domain methods and has outperformed conventional estimators in experiments and 2-D finite-element simulations.

Direct and gradient-based average strain estimation by using weighted nearest neighbor cross-correlation peaks

In this paper, two novel approaches, gradientbased and direct strain estimation techniques, are proposed for high-quality average strain imaging incorporating a cost function maximization. Stiffness typically is a continuous function. Consequently, stiffness of proximal tissues is very close to that of the tissue corresponding to a given data window. Hence, a cost function is defined from exponentially weighted neighboring pre- and post-compression RF echo normalized cross-correlation peaks in the lateral (for displacement estimation) or in both the axial and the lateral (for direct strain estimation) directions. This enforces a controlled continuity in displacement/strain and average displacement/strain is calculated from the corresponding maximized cost function. Axial stress causes lateral shift in the tissue. Therefore, a 1-D post-compression echo segment is selected by incorporating Poissons ratio. Two stretching factors are considered simultaneously in gradient-based strain estimation that allow imaging the lesions properly. The proposed time-domain gradient-based and direct-strain-estimation-based algorithms demonstrate significantly better performance in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), peak signal-to-noise ratio (PSNR), and mean structural similarity (MSSIM) than the other reported time-domain gradientbased and direct-strain-estimation techniques in finite element modeling (FEM) simulation and phantom experiments. For example, in FEM simulation, it has been found that the proposed direct strain estimation method can improve up to approximately 2.49 to 8.71, 2.2 to 6.63, 1.5 to 5, and 1.59 to 2.45 dB in the SNRe, CNRe, PSNR, and MSSIM compared with the traditional direct strain estimation method, respectively, and the proposed gradient-based algorithm demonstrates 2.99 to 16.26, 18.74 to 23.88, 3 to 9.5, and 0.6 to 5.36 dB improvement in the SNRe, CNRe, PSNR, and MSSIM, respectively, compared with a recently reported time-domain gradient-based technique. The range of improvement as noted above is for low to high applied strains. In addition, the comparative results using the in vivo breast data (including malignant or benign masses) also show that the lesion size is better defined by the proposed gradient-based average strain estimation technique.

IMPLEMENTATION ISSUES IN ULTRASONIC FLOW IMAGING

This article addresses several implementation issues in ultrasonic flow imaging. We discuss frequency-dependent scattering and attenuation, use of interpolation for computation intensive methods and implications of the use of chirps to increase bandwidth. We also discuss wall filtering issues; our observations show that the butterfly search estimator may be capable of detecting flow in the vicinity of strong stationary scatterers (clutter) without additional processing such as wall-filtering. Illustrative examples are given for simulated and experimental data.