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1. An adaptive coherent flow power doppler beamforming scheme for improved sensitivity towards blood signal energy

   https://doi.org/10.1117/12.2514184  

   Ozgun, Kathryn A.; Byram, Brett C.; Ruiter, Nicole V.; Byram, Brett C. (March 2019, Medical Imaging 2019: Ultrasonic Imaging and Tomography)

   Ultrasonic flow imaging remains susceptible to cluttered imaging environments, which often results in degraded image quality. Coherent Flow Power Doppler (CFPD)–a beamforming technique–has demonstrated efficacy in addressing sources of diffuse clutter. CFPD depicts the normalized spatial coherence of the backscattered echo, which is described by the van Cittert-Zernike theorem. However, the use of a normalized coherence metric in CFPD uncouples the image intensity from the magnitude of the underlying blood echo. As a result, CFPD is not a robust approach to study gradation in blood echo energy, which depicts the fractional moving blood volume. We have developed a modified beamforming scheme, termed power-preserving Coherent Flow Power Doppler (ppCFPD), which employs a measure of signal covariance across the aperture, rather than normalized coherence. As shown via Field II simulations, this approach retains the clutter suppression capability of CFPD, while preserving the underlying signal energy, similar to standard power Doppler (PD). Furthermore, we describe ongoing work, in which we have proposed a thresholding scheme derived from a statistical analysis of additive noise, to further improve perception of flow. Overall, this adaptive 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. A Monte Carlo Rendering Framework for Simulating Optical Heterodyne Detection

   https://doi.org/10.1145/3731150  

   Kim, Juhyeon; Benko, Craig; Wrenninge, Magnus; Villemin, Ryusuke; Barber, Zeb; Jarosz, Wojciech; Pediredla, Adithya (August 2025, ACM Transactions on Graphics)

   Optical heterodyne detection (OHD) employs coherent light and optical interference techniques (Fig. 1-(A)) to extract physical parameters, such as velocity or distance, which are encoded in the frequency modulation of the light. With its superior signal-to-noise ratio compared to incoherent detection methods, such as time-of-flight lidar, OHD has become integral to applications requiring high sensitivity, including autonomous navigation, atmospheric sensing, and biomedical velocimetry. However, current simulation tools for OHD focus narrowly on specific applications, relying on domain-specific settings like restricted reflection functions, scene configurations, or single-bounce assumptions, which limit their applicability. In this work, we introduce a flexible and general framework for spectral-domain simulation of OHD. We demonstrate that classical radiometry-based path integral formulation can be adapted and extended to simulate the OHD measurements in the spectral domain. This enables us to leverage the rich modeling and sampling capabilities of existing Monte Carlo path tracing techniques. Our formulation shares structural similarities with transient rendering but operates in the spectral domain and accounts for the Doppler effect (Fig. 1-(B)). While simulators for the Doppler effect in incoherent (intensity) detection methods exist, they are largely not suitable to simulate OHD. We use a microsurface interpretation to show that these two Doppler imaging techniques capture different physical quantities and thus need different simulation frameworks. We validate the correctness and predictive power of our simulation framework by qualitatively comparing the simulations with real-world captured data for three different OHD applications—FMCW lidar, blood flow velocimetry, and wind Doppler lidar (Fig. 1-(C)). 

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5. 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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