Iterative Model-Based Beamforming for High Dynamic Range Applications

Expanding generalized contrast-to-noise ratio into a clinically relevant measure of lesion detectability by considering size and spatial resolution

Purpose

Early image quality metrics were often designed with clinicians in mind, and ideal metrics would correlate with the subjective opinion of practitioners. Over time, adaptive beamformers and other post-processing methods have become more common, and these newer methods often violate assumptions of earlier image quality metrics, invalidating the meaning of those metrics. The result is that beamformers may "manipulate" metrics without producing more clinical information.

Approach

In this work, Smith et al.'s signal-to-noise ratio (SNR) metric for lesion detectability is considered, and a more robust version, here called generalized SNR (gSNR), is proposed that uses generalized contrast-to-noise ratio (gCNR) as a core. It is analytically shown that for Rayleigh distributed data, gCNR is a function of Smith et al.'sC ψ (and therefore can be used as a substitution). More robust methods for estimating the resolution cell size are considered. Simulated lesions are included to verify the equations and demonstrate behavior, and it is shown to apply equally well to in vivo data.

Results

gSNR is shown to be equivalent to SNR for delay-and-sum (DAS) beamformed data, as intended. However, it is shown to be more robust against transformations and report lesion detectability more accurately for non-Rayleigh distributed data. In the simulation included, the SNR of DAS was 4.4 ± 0.8 , and minimum variance (MV) was 6.4 ± 1.9 , but the gSNR of DAS was 4.5 ± 0.9 , and MV was 3.0 ± 0.9 , which agrees with the subjective assessment of the image. Likewise, theDAS 2 transformation (which is clinically identical to DAS) had an incorrect SNR of 9.4 ± 1.0 and a correct gSNR of 4.4 ± 0.9 . Similar results are shown in vivo.

Conclusions

Using gCNR as a component to estimate gSNR creates a robust measure of lesion detectability. Like SNR, gSNR can be compared with the Rose criterion and may better correlate with clinical assessments of image quality for modern beamformers.

Methods for Enhancing the Robustness of the Generalized Contrast-to-Noise Ratio

The generalized contrast-to-noise ratio (gCNR) is a new but increasingly popular metric for measuring lesion detectability due to its use of probability distribution functions that increase robustness against transformations and dynamic range alterations. The value of these kinds of metrics has become increasingly important as it becomes clear that traditional metrics can be arbitrarily boosted with advanced beamforming or the right kinds of postprocessing. The gCNR works well for most cases; however, we will demonstrate that for some specific cases the implementation of gCNR using histograms requires careful consideration, as histograms can be poor estimates of probability density functions (PDFs) when designed improperly. This is demonstrated with simulated lesions by altering the amount of data and the number of bins used in the calculation, as well as by introducing some extreme transformations that are represented poorly by uniformly spaced histograms. In this work, the viability of a parametric gCNR implementation is tested, more robust methods for implementing histograms are considered, and a new method for estimating gCNR using empirical cumulative distribution functions (eCDFs) is shown. The most consistent methods found were to use histograms on rank-ordered data or histograms with variable bin widths, or to use eCDFs to estimate the gCNR.

Enhancing sizing accuracy in ultrasound images with an alternative ADMIRE model and dynamic range considerations

Ultrasound imaging can struggle with sizing accuracy, especially when the targets have a significantly different amplitude compared to the surrounding background. In this work, we consider the challenging task of accurately sizing hyperechoic structures, and specifically kidney stones, where accurate sizing is critical for determining medical intervention. AD-Ex, an extended alternative model of our aperture domain model image reconstruction (ADMIRE) pre-processing method, is introduced and is designed to improve clutter removal and improve sizing accuracy. We compare this method against other resolution enhancing methods such as minimum variance (MV) and generalized coherence factor (GCF), and against those methods using AD-Ex as a pre-processing tool. These methods are evaluated among patients with kidney stone disease, with the task of accurately sizing the stones against the gold standard, computed tomography (CT). Stone ROI's were selected using contour maps as reference from which the lateral stone size was estimated. Among the in vivo kidney stone cases we processed, AD-Ex+MV had the overall lowest sizing error among the methods, with an average error of 10.8% compared to the next best method AD-Ex which had an average error of 23.4%. For reference, DAS had an average error of 82.4%. Though dynamic range was evaluated to determine optimal thresholding for sizing applications, variability between stone cases was too high for any conclusions to be drawn at this time.

Combining ADMIRE and MV to Improve Image Quality

Aperture domain model image reconstruction (ADMIRE) is a frequency-domain, model-based beamformer, in part designed for removing reverberation and off-axis clutter. Minimum variance (MV) is alternatively designed to reduce off-axis interference and improve lateral resolution. MV is known to be less effective in high incoherent noise scenarios, and its performance in the presence of reverberation has not been evaluated. By implementing ADMIRE before MV, the benefits of both these beamformers can be achieved. In this article, the assumptions of MV are discussed, specifically their relationship to reverberation clutter. The use of ADMIRE as a preprocessing step to suppress noise from simulations with linear scanning and in vivo curvilinear kidney data is demonstrated, and both narrowband and broadband implementations of MV are applied. With optimal parameters, ADMIRE + MV demonstrated sizing improvements over MV alone by an average of 52.1% in 0-dB signal-to-clutter ratio reverberation cyst simulations and 14.5% in vivo while improving the contrast ratio compared to ADMIRE alone by an average of 15.1% in simulations and 14.0% in vivo. ADMIRE + MV demonstrated a consistent improvement compared to DAS, MV, and ADMIRE both in terms of sizing and contrast ratio.

Spatiotemporal Coherence to Quantify Sources of Image Degradation in Ultrasonic Imaging

Thermal noise and acoustic clutter signals degrade ultrasonic image quality and contribute to unreliable clinical assessment. When both noise and clutter are prevalent, it is difficult to determine which one is a more significant contributor to image degradation because there is no way to separately measure their contributions in vivo. Efforts to improve image quality often rely on an understanding of the type of image degradation at play. To address this, we derived and validated a method to quantify the individual contributions of thermal noise and acoustic clutter to image degradation by leveraging spatial and temporal coherence characteristics. Using Field II simulations, we validated the assumptions of our method, explored strategies for robust implementation, and investigated its accuracy and dynamic range. We further proposed a novel robust approach for estimating spatial lag-one coherence. Using this robust approach, we determined that our method can estimate the signal-to-thermal noise ratio (SNR) and signal-to-clutter ratio (SCR) with high accuracy between SNR levels of -30 to 40 dB and SCR levels of -20 to 15 dB. We further explored imaging parameter requirements with our Field II simulations and determined that SNR and SCR can be estimated accurately with as few as two frames and sixteen channels. Finally, we demonstrate in vivo feasibility in brain imaging and liver imaging, showing that it is possible to overcome the constraints of in vivo motion using high-frame rate M-Mode imaging.