A 3-D super resolution (SR) pipeline based on data from a Row-Column (RC) array is presented. The 3 MHz RC array contains 62 rows and 62 columns with a half wavelength pitch. A Synthetic Aperture (SA) pulse inversion sequence with 32 positive and 32 negative row emissions are used for acquiring volumetric data using the SARUS research ultrasound scanner. Data received on the 62 columns are beamformed on a GPU for a maximum volume rate of 156 Hz, when the pulse repetition frequency is 10 kHz. Simulated and 3-D printed point and flow micro-phantoms are used for investigating the approach. The flow micro-phantom contains a 100 µm radius tube injected with the contrast agent SonoVue. The 3-D processing pipeline uses the volumetric envelope data to find the bubble's positions from their interpolated maximum signal and yields a high resolution in all three coordinates. For the point micro-phantom the standard deviation on the position is (20.7, 19.8 , 9.1) µm (x, y, z). The precision estimated for the flow phantom is below 23 µm in all three coordinates, making it possible to locate structures on the order of a capillary in all three dimensions. The RC imaging sequence's point spread function has a size of 0.58 × 1.05 × 0.31 mm 3 (1.17λ ×2.12λ ×0.63λ), so the possible volume resolution is 28,900 times smaller than for SA RC B-mode imaging.
Delay-and-sum (DAS) beamforming is unable to identify individual scatterers when their density is so high that their point spread functions overlap each other. This paper proposes a convolutional neural network (CNN)-based method to detect and localize high-density scatterers, some of which are closer than the resolution limit of DAS beamforming. A CNN was designed to take radio frequency channel data and return nonoverlapping Gaussian confidence maps. The scatterer positions were estimated from the confidence maps by identifying local maxima. On simulated test sets, the CNN method with three plane waves achieved a precision of 1.00 and a recall of 0.91. Localization uncertainties after excluding outliers were ± 46 µm (outlier ratio: 4%) laterally and ± 26 µm (outlier ratio: 1%) axially. To evaluate the proposed method on measured data, two phantoms containing cavities were 3-D printed and imaged. For phantom study, training data were modified according to the physical properties of the phantoms and a new CNN was trained. On an uniformly spaced scatterer phantom, a precision of 0.98 and a recall of 1.00 were achieved with the localization uncertainties of ± 101 µm (outlier ratio: 1%) laterally and ± 37 µm (outlier ratio: 1%) axially. On a randomly spaced scatterer phantom, a precision of 0.59 and a recall of 0.63 were achieved. The localization uncertainties were ± 132 µm (outlier ratio: 0%) laterally and ± 44 µm with a bias of 22 µm (outlier ratio: 0%) axially. This method can potentially be extended to detect highly concentrated microbubbles in order to shorten data acquisition times of super-resolution ultrasound imaging.
The improved resolution provided by ultrasound super-resolution imaging (SRI) sets new demands on the fabrication of phantoms for the validation and verification of the technique. Phantoms should resemble tissue and replicate the 3D nature of tissue vasculature at the microvascular scale. This paper presents a potential method for creating complex 3D phantoms, via 3D printing of water-filled polymer networks. By using a custom-built stereolithographic printer, projected light of the desired patterns converts an aqueous poly(ethylene glycol) diacrylate (PEGDA) solution into a hydrogel, a material capable of containing 75 wt% of water. Due to the hydrogel mainly consisting of water, it will, from an acoustical point of view, respond very similar to tissue. A method for printing cavities as small as (100 µm) 3 is demonstrated, and a 3D printed flow phantom containing channels with cross sections of (200 µm) 2 is presented. The designed structures are geometrically manufactured with a 2% increase in dimensions. The potential for further reduction of the flow phantom channels size, makes 3D printing a promising method for obtaining microvascular-like structures.
We report angle resolved characterization of nanostructured and conventionally
Row-column (RC) arrays have the potential to yield full three-dimensional ultrasound imaging with a greatly reduced number of elements compared to fully populated arrays. They, however, have several challenges due to their special geometry. This review paper summarizes the current literature for RC imaging and demonstrate that full anatomic and functional imaging can attain a high quality using synthetic aperture (SA) sequences and modified delay-and-sum beamforming. Resolution can approach the diffraction limit with an isotropic resolution of half a wavelength with low side-lobe levels, and the field-ofview can be expanded by using convex or lensed RC probes. GPU beamforming allows for 3 orthogonal planes to be beamformed at 30 Hz, providing near real time imaging ideal for positioning the probe and improving the operator's workflow. Functional imaging is also attainable using transverse oscillation and dedicated SA sequence for tensor velocity imaging for revealing the full 3-D velocity vector as a function of spatial position and time for both blood velocity and tissue motion estimation. Using RC arrays with commercial contrast agents can reveal super resolution imaging with isotropic resolution below 20 µm. RC arrays can, thus, yield full 3-D imaging at high resolution, contrast, and volumetric rates for both anatomic and functional imaging with the same number of receive channels as current commercial 1-D arrays.
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