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The generalized contrast-to-noise ratio (gCNR) is a relatively new image quality metric designed to assess the probability of lesion detectability in ultrasound images. Although gCNR was initially demonstrated with ultrasound images, the metric is theoretically applicable to multiple types of medical images. In this paper, the applicability of gCNR to photoacoustic images is investigated. The gCNR was computed for both simulated and experimental photoacoustic images generated by amplitude-based (i.e., delay-and-sum) and coherence-based (i.e., short-lag spatial coherence) beamformers. These gCNR measurements were compared to three more traditional image quality metrics (i.e., contrast, contrast-to-noise ratio, and signal-to-noise ratio) applied to the same datasets. An increase in qualitative target visibility generally corresponded with increased gCNR. In addition, gCNR magnitude was more directly related to the separability of photoacoustic signals from their background, which degraded with the presence of limited bandwidth artifacts and increased levels of channel noise. At high gCNR values (i.e., 0.95-1), contrast, contrast-to-noise ratio, and signal-to-noise ratio varied by up to 23.7-56.2 dB, 2.0-3.4, and 26.5-7.6×1020, respectively, for simulated, experimental phantom, andin vivodata. Therefore, these traditional metrics can experience large variations when a target is fully detectable, and additional increases in these values would have no impact on photoacoustic target detectability. In addition, gCNR is robust to changes in traditional metrics introduced by applying a minimum threshold to image amplitudes. In tandem with other photoacoustic image quality metrics and with a defined range of 0 to 1, gCNR has promising potential to provide additional insight, particularly when designing new beamformers and image formation techniques and when reporting quantitative performance without an opportunity to qualitatively assess corresponding images (e.g., in text-only abstracts).
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An improved training scheme for deep neural network ultrasound beamforming
Deep neural networks have been shown to be effective adaptive beamformers for ultrasound imaging. However, when training with traditional L p norm loss functions, model selection is difficult because lower loss values are not always associated with higher image quality. This ultimately limits the maximum achievable image quality with this approach and raises concerns about the optimization objective. In an effort to align the optimization objective with the image quality metrics of interest, we implemented a novel ultrasound-specific loss function based on the spatial lag-one coherence and signal-to-noise ratio of the delayed channel data in the short-time Fourier domain. We employed the R-Adam optimizer with look ahead and cyclical learning rate to make the training more robust to initialization and local minima, leading to better model performance and more reliable convergence. With our custom loss function and optimization scheme, we achieved higher contrast-to-noise-ratio, higher speckle signal-to-noise-ratio, and more accurate contrast ratio reconstruction than with previous deep learning and delay-and-sum beamforming approaches.
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- Award ID(s):
- 1750994
- PAR ID:
- 10138381
- Date Published:
- Journal Name:
- 2019 IEEE International Ultrasonics Symposium (IUS)
- Page Range / eLocation ID:
- 568 to 570
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ULTSYM.2019.8925953
