In vivo ultrasonic imaging with transducer arrays suffers from image degradation resulting from beamforming limitations, including diffraction-limited beamforming and beamforming degradation caused by tissue inhomogeneity. Additionally, based on recent studies, multipath scattering also causes significant image degradation. To reduce degradation from both sources, we propose a model-based signal decomposition scheme. The proposed algorithm identifies spatial frequency signatures to decompose received wavefronts into their most significant scattering sources. Scattering sources originating from a region of interest are used to reconstruct decluttered wavefronts, which are beamformed into decluttered RF scan lines or A-lines. To test the algorithm, ultrasound system channel data were acquired during liver scans from 8 patients. Multiple data sets were acquired from each patient, with 55 total data sets, 43 of which had identifiable hypoechoic regions on normal B-mode images. The data sets with identifiable hypoechoic regions were analyzed. The results show the decluttered B-mode images have an average improvement in contrast over normal images of 7.3 ± 4.6 dB. The contrast-to-noise ratio (CNR) changed little on average between normal and decluttered Bmode, -0.4 ± 5.9 dB. The in vivo speckle SNR decreased; the change was -0.65 ± 0.28. Phantom speckle SNR also decreased, but only by -0.40 ± 0.03.

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Ultrasonic multipath and beamforming clutter reduction: a chirp model approach

We investigate and extend the ideal observer methodology developed by Smith and Wagner to detection and discrimination tasks related to breast sonography. We provide a numerical approach for evaluating the ideal observer acting on radio frequency (RF) frame data, which involves inversion of large nonstationary covariance matrices, and we describe a power-series approach to computing this inverse. Considering a truncated power series suggests that the RF data be Wiener-filtered before forming the final envelope image. We have compared human performance for Wiener-filtered and conventional B-mode envelope images using psychophysical studies for 5 tasks related to breast cancer classification. We find significant improvements in visual detection and discrimination efficiency in four of these five tasks. We also use the Smith-Wagner approach to distinguish between human and processing inefficiencies, and find that generally the principle limitation comes from the information lost in computing the final envelope image.

Observer efficiency in discrimination tasks Simulating Malignant and benign breast lesions imaged with ultrasound

For the 2-class detection problem (signal absent/present), the likelihood ratio is an ideal observer in that it minimizes Bayes risk for arbitrary costs and it maximizes the area under the receiver operating characteristic (ROC) curve [AUC]. The AUC-optimizing property makes it a valuable tool in imaging system optimization. If one considered a different task, namely, joint detection and localization of the signal, then it would be similarly valuable to have a decision strategy that optimized a relevant scalar figure of merit. We are interested in quantifying performance on decision tasks involving location uncertainty using the localization ROC (LROC) methodology. Therefore, we derive decision strategies that maximize the area under the LROC curve, A/sub LROC/. We show that these decision strategies minimize Bayes risk under certain reasonable cost constraints. The detection-localization task is modeled as a decision problem in three increasingly realistic ways. In the first two models, we treat location as a discrete parameter having finitely many values resulting in an (L+1) class classification problem. In our first simple model, we do not include search tolerance effects and in the second, more general, model, we do. In the third and most general model, we treat location as a continuous parameter and also include search tolerance effects. In all cases, the essential proof that the observer maximizes A/sub LROC/ is obtained with a modified version of the Neyman-Pearson lemma. A separate form of proof is used to show that in all three cases, the decision strategy minimizes the Bayes risk under certain reasonable cost constraints.

Decision strategies that maximize the area under the LROC curve

This paper proposes an efficient array beam-forming method using spatial matched filtering (SMF) for ultrasonic imaging. In the proposed method, ultrasonic waves are transmitted from an array subaperture with fixed transmit focus as in conventional array imaging. At receive, radio frequency echo signals from each receive channel are passed through a spatial matched filter that is constructed based on the system transmit-receive spatial impulse response. The filtered echo signals are then summed without time delays. The filter concentrates and spatially registers the echo energy from each element so that the pulse-echo impulse response of the summed output is focused with acceptably low side lobes. Analytical beam pattern analysis and simulation results using a linear array show that this spatial filtering method can improve lateral resolution and contrast-to-noise ratio as compared with conventional dynamic receive focusing (DRF) methods. Experimental results with a linear array are consistent but point out the need to address additional practical issues. Spatial filtering is equivalent to synthetic aperture methods that dynamically focus on both transmit and receive throughout the field of view. In one common example of phase aberrations, the SMF method was degraded to a degree comparable to conventional DRF methods.

/pdf/efficient-array-beam-forming-by-spatial-filtering-for-ct4fmvouts.pdf

Efficient array beam forming by spatial filtering for ultrasound B-mode imaging

The widespread development of new ultrasound image formation techniques has created a need for a standardized methodology for comparing the resulting images. Traditional methods of evaluation use quantitative metrics to assess the imaging performance in specific tasks, such as point resolution or lesion detection. Quantitative evaluation is complicated by unconventional new methods and nonlinear transformations of the dynamic range of data and images. Transformation-independent image metrics have been proposed for quantifying task performance. However, clinical ultrasound still relies heavily on visualization and qualitative assessment by expert observers. We propose the use of histogram matching to better assess differences across image formation methods. We briefly demonstrate the technique using a set of sample beamforming methods and discuss the implications of such image processing. We present variations of histogram matching and provide code to encourage the application of this method within the imaging community.

Histogram Matching for Visual Ultrasound Image Comparison

A rigorous statistical theory for characterizing the performance of medical ultrasound systems for lesion detection tasks is developed. A design strategy for optimizing ultrasound systems should be to adjust parameters for maximum information content, which is obtained by maximizing the ideal observer performance. Then, given the radio-frequency data, image and signal processing algorithms are designed to extract as much diagnostically relevant information as possible. In this paper, closed-form and low-contrast approximations of ideal observer performance are derived for signal known statistically detection tasks. The accuracy of the approximations are tested by comparing with Monte Carlo techniques. A metric borrowed and modified from photon imaging, Generalized Noise Equivalent Quanta, is shown to be a useful and measurable target-independent figure of merit when adapted for ultrasound systems. This theory provides the potential to optimize design tradeoffs for detection tasks. For example it may help us understand how to push the limits of specific features, such as spatial resolution, without significantly compromising overall detection performance.

/pdf/detection-performance-theory-for-ultrasound-imaging-systems-2vaq3ixhyf.pdf

M.D. Parry

Papers

Detection performance theory for ultrasound imaging systems