KEYWORDS: Signal to noise ratio, Phased arrays, Image quality, In vivo imaging, Chromium, Speckle, Histograms, Point spread functions, Autocorrelation, Analytics
PurposeEarly 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.ApproachIn 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.’s Cψ (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.ResultsgSNR 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, the DAS2 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.ConclusionsUsing 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.
Early image quality metrics were often designed with clinicians in mind, and ideally better metrics would correlate with the subjective opinion of clinically better images. 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, making the meaning of these metrics inaccurate at best. The result is the possibility of beamformers that can “manipulate” metrics to be better, while not producing clinically better images. In this work, Smith et al.’s SNR metric for lesion detectability1 is considered, and a more robust version, here called generalized SNR (gSNR), is proposed that uses gCNR2, 3 as a core, and therefore is more robust to transformations and manipulations. SNR differs from gCNR in that it uses lesion size and spatial resolution as components of it’s calculation, which gCNR does not. It is analytically shown that for Rayleigh distributed data, gCNR can be written in terms of Smith et al.’s Cψ (and therefore can be used as a substitution), and more robust methods for estimating the resolution cell size are considered. This allows for a robust estimate of lesion detectability based on estimated gCNR that may correlate with clinical assessments of image quality.
We previously showed that domain adaptive deep neural networks (DNNs) can outperform delay-and-sum (DAS) beamforming in the context of abdominal imaging. We hypothesize the ability of our domain adaptive DNN framework to be applied to transthoracic echocardiography (TTE). We also propose architectural improvements, such as leveraging an encoder-decoder structure and skip connections, to further improve ultrasound image quality for echocardiography tasks such as the detection of thrombi in the left atrial appendage (LAA). DNN training data utilized simulated and in vivo cardiac data. Simulated anechoic and hypoechoic cysts with various amounts of clutter were generated through Field II and in vivo data was collected by scanning patients at Vanderbilt University Medical Center. Fundamental frequency TTE data from five separate cases were processed with DAS, ADMIRE, the baseline model, and multiple models with modified architectures. We found that even when varying the amount of training data, the DNNs consistently achieved higher generalized contrast-to-noise (gCNR) and contrast ratio (CR) but lower contrast-to-noise ratio when compared to DAS. The best-performing beamformer was one DNN with our architectural improvements, achieving higher average gCNR and CR values of .907 and 48.30 dB compared to the baseline DNN values of .788 and 39.45dB, and DAS values of .717 and 14.08dB. Our results demonstrate that our domain adaptive DNN can effectively be applied in the context of transthoracic cardiology, and an encoder-decoder architecture with skip connections can result in even more improvements. Further advancements may improve image quality even more.
Reverberation clutter is a difficult source of image degradation in patients, and minimum variance (MV) in particular is poorly equipped to handle such sources, as we demonstrate here. We propose that a pre-processing step such as ADMIRE should be implemented in cases with high reverberation clutter when we still want to be able to implement MV to realize improvements in lateral resolution. The ADMIRE, or aperture domain model image reconstruction, method is specifically designed to suppress or eliminate reverberant and off-axis sources of clutter while returning the decluttered channel data with its original dimensionality, allowing us to sequentially process the data with MV. We show that in simulated data this combined method results in clear improvements to image quality, contrast ratio, and target boundary thickness compared to DAS and MV alone. In in vivo cases, contrast ratio and general image quality are improved, and boundary thickness is generally on par with DAS and MV.
Kidney stones are often poorly visualized with ultrasound despite the fact that they have a large impedance mismatch. In previous kidney stone studies conducted by our group, we demonstrated that the Mid-Lag Spatial Coherence (MLSC) beamforming method suppresses the incoherent background speckle while enhancing coherent scatterers. This allowed kidney stones to be highlighted. To study this approach in more detail Field-II simulations and in-house phantoms containing kidney stones were used to test the effectiveness of MLSC with different parameters. The number of lags used during beamforming and the brightness of the point target were altered. Then, the CNR, SNR, CR, and PSNR of the phantoms and simulations were compared. The CNR experienced little change between lag ranges, but the SNR and PSNR increased with the start lag. SNR increased by 12.9% ± 2.9% between the lowest and highest lag range while PSNR increased by 27.9% ± 4.6% between the lowest and highest lag range. CR did not change in a regular pattern but remained consistently higher than delay and sum beamforming. We also compare MLSC against short-lag spatial coherence (SLSC) and show that we also see improvements over this method including an increase of MLSC over SLSC ranging between 250% and 401% for PSNR and between 414% and 879% for CR.
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