Contrast-enhanced ultrasound imaging using capacitive micromachined ultrasonic transducers.

Capacitive micromachined ultrasonic transducers (CMUTs) have a nonlinear relationship between the applied voltage and the emitted signal, which is detrimental to conventional contrast enhanced ultrasound (CEUS) techniques. Instead, a three-pulse amplitude modulation (AM) sequence has been proposed, which is not adversely affected by the nonlinearly emitted harmonics. In this paper, this is shown theoretically, and the performance of the sequence is verified using a 4.8 MHz linear capacitive micromachined ultrasonic transducer (CMUT) array, and a comparable lead zirconate titanate (PZT) array, across 6-60 V applied alternating current (AC) voltage. CEUS images of the contrast agent SonoVue flowing through a 3D printed hydrogel phantom showed an average enhancement in contrast-to-tissue ratio (CTR) between B-mode and CEUS images of 49.9 and 37.4 dB for the PZT array and CMUT, respectively. Furthermore, hydrophone recordings of the emitted signals showed that the nonlinear emissions from the CMUT did not significantly degrade the cancellation in the compounded AM signal, leaving an average of 2% of the emitted power between 26 and 60 V of AC. Thus, it is demonstrated that CMUTs are capable of CEUS imaging independent of the applied excitation voltage when using a three-pulse AM sequence.

Anatomic and Functional Imaging Using Row-Column Arrays.

Row-column (RC) arrays have the potential to yield full 3-D 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 article summarizes the current literature for RC imaging and demonstrates 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 sidelobe levels, and the field of view can be expanded by using convex or lensed RC probes. GPU beamforming allows for three 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 (SRI) with isotropic resolution below [Formula: see text]. 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.

3D printed calibration micro-phantoms for super-resolution ultrasound imaging validation.

This study evaluates the use of 3D printed phantoms for 3D super-resolution ultrasound imaging (SRI) algorithm calibration. The main benefit of the presented method is the ability to do absolute 3D micro-positioning of sub-wavelength sized ultrasound scatterers in a material having a speed of sound comparable to that of tissue. Stereolithography is used for 3D printing soft material calibration micro-phantoms containing eight randomly placed scatterers of nominal size 205 µm × 205 µm × 200 µm. The backscattered pressure spatial distribution is evaluated to show similar distributions from micro-bubbles as the 3D printed scatterers. The printed structures are found through optical validation to expand linearly in all three dimensions by 2.6% after printing. SRI algorithm calibration is demonstrated by imaging a phantom using a λ/2 pitch 3 MHz 62+62 row-column addressed (RCA) ultrasound probe. The printed scatterers will act as point targets, as their dimensions are below the diffraction limit of the ultrasound system used. Two sets of 640 volumes containing the phantom features are imaged, with an intervolume uni-axial movement of the phantom of 12.5 µm, to emulate a flow velocity of 2 mm/s at a frame rate of 160 Hz. The ultrasound signal is passed to a super-resolution pipeline to localise the positions of the scatterers and track them across the 640 volumes. After compensating for the phantom expansion, a scaling of 0.989 is found between the distance between the eight scatterers calculated from the ultrasound data and the designed distances. The standard deviation of the variation in the scatterer positions along each track is used as an estimate of the precision of the super-resolution algorithm, and is expected to be between the two limiting estimates of (σ̃x,σ̃y,σ̃z) = (22.7 µm, 27.6 µm, 9.7 µm) and (σ̃x,σ̃y,σ̃z) = (18.7 µm, 19.3 µm, 8.9 µm). In conclusion, this study demonstrates the use of 3D printed phantoms for determining the accuracy and precision of volumetric super-resolution algorithms.

Detection and Localization of Ultrasound Scatterers Using Convolutional Neural Networks.

Delay-and-sum (DAS) beamforming is unable to identify individual scatterers when their density is so high that their point spread functions overlap. 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 delay-and-sum (DAS) beamforming. A CNN was designed to take radio frequency channel data and return non-overlapping 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 [Formula: see text] (outlier ratio: 4%) laterally and ±26 [Formula: see text] (outlier ratio: 1%) axially. To evaluate the proposed method on measured data, two phantoms containing cavities were 3-D printed and imaged. For the phantom study, the 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 [Formula: see text] (outlier ratio: 1%) laterally and ±37 [Formula: see text] (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 [Formula: see text] (outlier ratio: 0%) laterally and ±44 [Formula: see text] with a bias of 22 [Formula: see text] (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.

Three-Dimensional Super-Resolution Imaging Using a Row-Column Array.

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 is 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 microphantoms are used for investigating the approach. The flow microphantom contains a 100- [Formula: see text] 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 microphantom, the standard deviation on the position is (20.7, 19.8, 9.1) [Formula: see text]. The precision estimated for the flow phantom is below [Formula: see text] 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 mm3 ( 1.17λ×2.12λ×0.63λ ), so the possible volume resolution is 28900 times smaller than for SA RC B-mode imaging.