Super resolution (SR) imaging is currently conducted using fragile ultrasound contrast agents. This precludes using the full acoustic pressure range, and the distribution of bubbles has to be sparse for them to be isolated for SR imaging. Images have to be acquired over minutes to accumulate enough positions for visualizing the vasculature. A new method for SUper Resolution imaging using the Erythrocytes (SURE) as targets is introduced, which makes it possible to maximize the emitted pressure for good signal-to-noise ratios. The abundant number of erythrocyte targets make acquisition fast, and the SURE images can be acquired in seconds. A Verasonics Vantage 256 scanner was used in combination with a GE L8-18iD linear array probe operated at 10 MHz for a wavelength of 150 μm. A 12 emissions synthetic aperture ultrasound sequence was employed to scan the kidney of a Sprague-Dawley rat for 24 seconds to visualize its vasculature. An ex vivo micro-CT image using the contrast agent Microfil was also acquired at a voxel size of 22.6 μm for validating the SURE images. The SURE image revealed vessels with a size down to 29 μm, five times smaller than the ultrasound wavelength, and the dense grid of vessels in the full kidney was reliably shown for scan times between 1 to 24 seconds. Visually the SURE images revealed the same vasculature as the micro-CT images. SURE images are acquired in seconds rather than minutes without contrast injection for easy clinical use, and they can be measured at full regulatory levels for pressure, intensity, and probe temperature.
KEYWORDS: Ultrasonography, Kidney, Filtering (signal processing), In vivo imaging, Visualization, Super resolution, Super resolution microscopy, Detection and tracking algorithms
Microbubble (MB) tracking is an integral part of super-resolution ultrasound imaging by providing sharper images and enabling velocity estimation. Tracking the MBs from the last to the first frame can generate different trajectories than tracking from the first to the last frame, when the next positions of a track depends on its previous positions, e.g., in Kalman-based methods. Our hypothesis is that tracking in a forward-backward manner can increase the overall tracking performance. In simulations, MB positions with a parabolic flow profile were generated inside two tubes. Three different tracking methods, including nearest-neighbor, Kalman, and hierarchical Kalman, were investigated. Using the proposed forward-backward strategy, all estimated velocity profiles for all trackers were improved and were closer to the actual velocity profiles with an improvement between 28% to 40% in the relative standard deviation (RSD) of the velocity values over 10 cross-sections of the tubes. A Sprague Dawley rat kidney was scanned for 10 minutes using a BK5000 scanner and X18L5s transducer, which is a linear array probe with 150 elements. The tracking results from the in vivo experiments showed that the combined image of the forward and backward tracks had 35% additional unique track positions. It showed a clear visual enhancement in the super-resolved velocity map. Overall, the improvement in visual aspects and velocity estimates suggest forward-backward strategy as an upgrade for Kalman-based trackers.
A delay-and-sum beamformer implementation for 3D imaging with row-column arrays is presented. It is written entirely in the MATLAB programming language for flexible use and fast modifications for research use, and all parts can run on either the CPU or GPU. Dynamic apodization with row-column arrays is presented and is supported in both transmit and receive. Delay calculations are simplified compared to previous beamformers, and 3D delay and apodization calculations are reduced to 2D problems for faster calculations. The performance is evaluated on an Intel Xeon E5-2630 v4 CPU with 64 GB RAM and a NVIDIA GeForce GTX 1080 Ti GPU with 11 GB RAM. A 192+192 array is simulated to image a volume of 96-by-96-by-45 wavelengths sampled at 0.3 wavelength in the axial direction and 0.5 wavelength in the lateral and elevation directions giving 5.53 million sample points. A single-element synthetic aperture sequence with 192 emissions is used. The 192 volumes are beamformed in approximately 1 hour on the CPU and 5 minutes on the GPU corresponding to a speed-up of up to 12.2 times. For a smaller beamforming problem consisting of the three center planes in the volume, a speed-up of 4.6 times is found from 109 to 24 seconds. The GPU utilization is around 5.0% of the possible floating point calculations indicating a trade-off between the easy programming approach and high performance.
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