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1. An Adapted Coherent Flow Power Doppler Beamforming Scheme for Improved Sensitivity Towards Blood Signal Energy

   https://doi.org/10.1109/ULTSYM.2018.8579870  

   Ozgun, Kathryn; Tierney, Jaime; Byram, Brett (October 2018, 2018 IEEE International Ultrasonics Symposium (IUS))

   Ultrasonic flow imaging remains susceptible to cluttered imaging environments, which often results in degraded image quality. Coherent Flow Power Doppler (CFPD) is a beamforming technique that has demonstrated efficacy in mitigating the presence of diffuse clutter in flow images. CFPD depicts the normalized aperture domain coherence of the backscattered echo, which is described by the van Cittert-Zernike theorem. However, the use of a normalized coherence metric uncouples the image intensity from the underlying blood signal energy. As a result, CFPD is not a robust approach to study gradation in blood signal energy, which depicts the fractional moving blood volume. We have developed a modified beamforming scheme, termed power-preserving Coherent Flow Power Doppler (ppCFPD), which depicts a measure of mutual intensity, rather than normalized coherence. This approach retains the clutter suppression capability of CFPD, while preserving sensitivity toward the underlying signal energy, similar to conventional power Doppler. Efficacy of this approach was shown via Field II simulations, and in vivo feasibility was demonstrated in a human liver. Overall, this adapted approach shows promise as an alternative technique to depict flow gradation in cluttered imaging environments. 

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2. A Spatial Coherence Beamformer Design for Power Doppler Imaging

   https://doi.org/10.1109/TMI.2019.2953657  

   Ozgun, Kathryn; Tierney, Jaime; Byram, Brett (January 2020, IEEE Transactions on Medical Imaging)

   (Early Access) Acoustic clutter is a primary source of image degradation in ultrasound imaging. In the context of flow imaging, tissue and acoustic clutter signals are often much larger in magnitude than the blood signal, which limits the sensitivity of conventional power Doppler in SNR-limited environments. This has motivated the development of coherence-based beamformers, including Coherent Flow Power Doppler (CFPD), which have demonstrated efficacy in mitigating sources of diffuse clutter. However, CFPD uses a measure of normalized coherence, which incurs a non-linear relationship between image intensity and the magnitude of the blood echo. As a result, CFPD is not a robust approach to study gradation of blood signal energy, which depicts the fractional moving blood volume. We propose the application of mutual intensity, rather than normalized coherence, to retain the clutter suppression capability inherent in coherence beamforming, while preserving the underlying signal energy. Feasibility of this approach was shown via Field II simulations, phantoms, and in vivo human liver data. In addition, we derive an adaptive statistical threshold for the suppression of residual noise signals. Overall, this beamformer design shows promise as an alternative technique to depict flow volume gradation in cluttered imaging environments. 

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3. Independent Component-Based Spatiotemporal Clutter Filtering for Slow Flow Ultrasound

   https://doi.org/10.1109/TMI.2019.2951465  

   Tierney, Jaime; Baker, Jennifer; Brown, Daniel; Wilkes, Don; Byram, Brett (January 2019, IEEE Transactions on Medical Imaging)

   (Early Access) Effective tissue clutter filtering is critical for non-contrast ultrasound imaging of slow blood flow in small vessels. Independent component analysis (ICA) has been considered by other groups for ultrasound clutter filtering in the past and was shown to be superior to principal component analysis (PCA)-based methods. However, it has not been considered specifically for slow flow applications or revisited since the onset of other slow flow-focused advancements in beamforming and tissue filtering, namely angled plane wave beamforming and full spatiotemporal singular value decomposition (SVD) (i.e., PCA-based) tissue filtering. In this work, we aim to develop a full spatiotemporal ICA-based tissue filtering technique facilitated by plane wave applications and compare it to SVD filtering. We compare ICA and SVD filtering in terms of optimal image quality in simulations and phantoms as well as in terms of optimal correlation to ground truth blood signal in simulations. Additionally, we propose an adaptive blood independent component sorting and selection method. We show that optimal and adaptive ICA can consistently separate blood from tissue better than principal component analysis (PCA)-based methods using simulations and phantoms. Additionally we demonstrate initial in vivo feasibility in ultrasound data of a liver tumor. 

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4. Single breath‐hold volumetric lung imaging at 0. 55T using stack‐of‐spiral (SoS) out‐in balanced SSFP

   https://doi.org/10.1002/mrm.30386  

   Tian, Ye; Lee, Nam_G; Zhao, Ziwei; Wilcox, Alison_G; Nieva, Jorge_J; Nayak, Krishna_S (November 2024, Magnetic Resonance in Medicine)

   Abstract PurposeTo develop a robust single breath‐hold approach for volumetric lung imaging at 0.55T. MethodA balanced‐SSFP (bSSFP) pulse sequence with 3D stack‐of‐spiral (SoS) out‐in trajectory for volumetric lung imaging at 0.55T was implemented. With 2.7× undersampling, the pulse sequence enables imaging during a 17‐s breath‐hold. Image reconstruction is performed using 3D SPIRiT and 3D l1‐Wavelet regularizations. In two healthy volunteers, single breath‐hold SoS out‐in bSSFP was compared against stack‐of‐spiral UTE (spiral UTE) and half‐radial dual‐echo bSSFP (bSTAR), based on signal intensity (SI), blood‐lung parenchyma contrast, and image quality. In six patients with pathologies including lung nodules, fibrosis, emphysema, and air trapping, single breath‐hold SoS out‐in and bSTAR were compared against low‐dose computed tomography (LDCT). ResultsSoS out‐in bSSFP achieved 2‐mm isotropic resolution lung imaging with a single breath‐hold duration of 17 s. SoS out‐in (2‐mm isotropic) provided higher lung parenchyma and blood SI and blood‐lung parenchyma contrast compared to spiral UTE (2.4 × 2.4 × 2.5 mm³) and bSTAR (1.6‐mm isotropic). When comparing SI normalized by voxel size, SoS out‐in has lower lung parenchyma signal, higher blood signal, and a higher blood‐lung parenchyma contrast compared to bSTAR. In patients, SoS out‐in bSSFP was able to identify lung fibrosis and lung nodules of size 4 and 8 mm, and breath‐hold bSTAR was able to identify lung fibrosis and 8 mm nodules. ConclusionSingle breath‐hold volumetric lung imaging at 0.55T with 2‐mm isotropic spatial resolution is feasible using SoS out‐in bSSFP. This approach could be useful for rapid lung disease screening, and in cases where free‐breathing respiratory navigated approaches fail. 

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5. An improved training scheme for deep neural network ultrasound beamforming

   https://doi.org/10.1109/ULTSYM.2019.8925953  

   Vienneau, Emelina; Luchies, Adam; Byram, Brett (October 2019, 2019 IEEE International Ultrasonics Symposium (IUS))

   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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