Tensor Velocity Imaging With Motion Correction

Ultrasound-based Velocity Vector Imaging in the Carotid Bifurcation: Repeatability and an In Vivo Comparison With 4-D Flow MRI

OBJECTIVE

Ultrasound-based velocity vector imaging (US-VVI) is a promising technique to gain insight into complex blood flow patterns that play an important role in atherosclerosis. However, in vivo validation of the 2-D velocity vector fields in the carotid bifurcation, using an adaptive velocity compounding method, is lacking. Its performance was validated in vivo against 4-D flow magnetic resonance imaging (MRI). Furthermore, the repeatability of US-VVI was determined.

METHODS

High frame rate US-VVI, which was repeated three times, and 4-D flow MRI data were acquired of the carotid bifurcation of 20 healthy volunteers. A semiautomatic registration of all US-VVI (n = 60) and 4-D flow MRI data was performed. The 2-D velocity vector fields were compared using cosine similarity and the root-mean-square error of the velocity magnitude. Temporal velocity profiles from the common carotid artery and internal carotid artery were compared. The interobserver and intraobserver agreement of US-VVI was determined for peak systolic velocities and end-diastolic velocities.

RESULTS

The registration was successful in 83% of cases. The 2-D velocity vector fields matched well between modalities, which is supported by high cosine similarities and low root-mean-square error of the velocity magnitudes. Temporal profiles showed high resemblance, with similarity indices of 0.87 and 0.80, and mean peak systolic velocity differences of 0.91 and 7.9 cm/s in the common carotid artery and internal carotid artery, respectively. Good repeatability of US-VVI was shown with a highest bias and standard deviation of 1.7 and 11.7 cm/s, respectively.

CONCLUSION

Good agreements were found of both vector angles and velocity magnitudes between US-VVI and 4-D flow MRI. Given the high spatiotemporal resolution, US-VVI enables the capture of small recirculating regions of short duration that are missed by 4-D flow MRI.

Motion Object Detection Model for Electronic Referee Scoring in Table Tennis Events

As a sport widely played around the world, the fairness and enjoyment of table tennis competitions have received increasing attention. Traditional table tennis referees rely on manual judgment, which has problems such as strong subjectivity and high misjudgment rate. Therefore, this study combines the background subtraction method and the Kalman filtering algorithm. It processes missing images in videos to propose a motion object detection and motion estimation model for table tennis events. The test results showed that the average loss value of the model was only 0.33, the average detection accuracy in the 20-category data set was 0.94, and the average detection time was 103.9 ms. In the simulation test, the model achieved the best trajectory prediction accuracy in both complete video images and partially missing information video images. The maximum difference in horizontal and vertical directions was 10.7 and 4.3 pixels, respectively, and the maximum error in three-dimensional coordinates was (3.3, 2.8, 2.1). The table tennis target detection and motion estimation model has high detection accuracy and stability, providing new ideas and methods for the development of electronic referee systems in table tennis competitions.

Real-Time Full-Volume Row-Column Imaging

An implementation of volumetric beamforming for row-column addressed arrays (RCAs) is proposed, with optimizations for Graphics Processing Units (GPUs). It is hypothesized that entire volumes can imaged in real time by a consumer-class GPU at an emission rate ≥12 kHz. A separable beamforming algorithm was used to reduce the number of calculations with a negligible impact on the image quality. Here, a single image was beamformed for each emission and then extrapolated to reproduce the volume, which resulted in 65 times fewer calculations per volume. Reusing computations and samples among adjacent pixels and frames reduced the amount of overhead and load instructions, increasing performance. A GPU beamformer, written in CUDA C++, was modified to implement the dual-stage imaging with optimizations. In-vivo rat kidney data was acquired using a 6 MHz Vermon 128+128 RCA probe and a Verasonics Vantage 256 scanner. The acquisition used 96 defocused emissions at a 12 kHz rate for a volume acquisition rate of 125 Hz. Processing time, including all pre-processing, was measured for an NVIDIA GeForce RTX 4090 GPU, and the resulting beamforming rate was 1440 volumes per second, greatly exceeding the real-time rate. Based on the GPU's floating-point throughput, this corresponds to 22% of the theoretically achievable rate. High efficiency was also shown for an RTX 2080 Ti and RTX 3090, both achieving real-time imaging. This shows that 3D imaging can be performed in real time with a setup similar to 2D imaging: Using a single graphics card, one scanner, and 128 transmit/receive channels.

Doppler and Pair-Wise Optical Flow Constrained 3D Motion Compensation for 3D Ultrasound Imaging

Volumetric (3D) ultrasound imaging using a 2D matrix array probe is increasingly developed for various clinical procedures. However, 3D ultrasound imaging suffers from motion artifacts due to tissue motions and a relatively low frame rate. Current Doppler-based motion compensation (MoCo) methods only allow 1D compensation in the in-range dimension. In this work, we propose a new 3D-MoCo framework that combines 3D velocity field estimation and a two-step compensation strategy for 3D diverging wave compounding imaging. Specifically, our framework explores two constraints of a round-trip scan sequence of 3D diverging waves, i.e., Doppler and pair-wise optical flow, to formulate the estimation of the 3D velocity fields as a global optimization problem, which is further regularized by the divergence-free and first-order smoothness. The two-step compensation strategy is to first compensate for the 1D displacements in the in-range dimension and then the 2D displacements in the two mutually orthogonal cross-range dimensions. Systematical in-silico experiments were conducted to validate the effectiveness of our proposed 3D-MoCo method. The results demonstrate that our 3D-MoCo method achieves higher image contrast, higher structural similarity, and better speckle patterns than the corresponding 1D-MoCo method. Particularly, the 2D cross-range compensation is effective for fully recovering image quality.

Transverse oscillation tensor velocity imaging using a row-column addressed array: Experimental validation

Tensor velocity imaging (TVI) performance with a row-column probe was assessed for constant flow in a straight vessel phantom and pulsatile flow in a carotid artery phantom. TVI, i.e., estimating the 3-D velocity vector as a function of time and spatial position, was performed using the transverse oscillation cross-correlation estimator, and the flow was acquired with a Vermon 128+128 row-column array probe connected to a Verasonics 256 research scanner. The emission sequence used 16 emissions per image, and a TVI volume rate of 234 Hz was obtained for a pulse repetition frequency (f_prf) of 15 kHz. The TVI was validated by comparing estimates of the flow rate through several cross-sections with the flow rate set by the pump. For the constant 8 mL/s flow in the straight vessel phantom with relative estimator bias (RB) and standards deviation (RSD) was found in the range of -2.18% to 0.55% and 4.58% to 2.48% in measurements performed with an f_prf of 15, 10, 8, and 5 kHz. The pulsatile flow in the carotid artery phantom the was set to an average flow rate of 2.44 mL/s, and the flow was acquired with an f_prf of 15, 10, and 8 kHz. The pulsatile flow was estimated from two measurement sites: one at a straight section of the artery and one at the bifurcation. In the straight section, the estimator predicted the average flow rate with an RB value ranging from -7.99% to 0.10% and an RSD value ranging from 10.76% to 6.97%. At the bifurcation, RB and RSD values were between -7.47% to 2.02% and 14.46% to 8.89%. This demonstrates that an RCA with 128 receive elements can accurately capture the flow rate through any cross-section at a high sampling rate.

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.

Performance Assessment of Row-Column Transverse Oscillation Tensor Velocity Imaging Using Computational Fluid Dynamics Simulation of Carotid Bifurcation Flow

In this work, the accuracy of row-column tensor velocity imaging (TVI), i.e., 3-D vector flow imaging (VFI) in 3-D space over time, is quantified on a complex, clinically relevant flow. The quantification is achieved by transferring the flow simulated using computational fluid dynamics (CFD) to a Field II simulation environment, and this allows for a direct comparison between the actual and estimated velocities. The carotid bifurcation flow simulations were performed with a peak inlet velocity of 80 cm/s, nonrigid vessel walls, and a flow cycle duration of 1.2 s. The flow was simulated from two observation angles, and it was acquired using a 3-MHz 62+62 row-column addressed array (RCA) at a pulse repetition frequency ( f_prf ) of 10 and 20 kHz. The tensor velocities were obtained at a frame rate of 208.3 Hz, at f_prf = 10 kHz , and the results from two velocity estimators were compared. The two estimators were the directional transverse oscillation (TO) cross correlation estimator and the proposed autocorrelation estimator. Linear regression between the actual and estimated velocity components yielded, for the cross correlation estimator, an R ² value in the range of 0.89-0.91, 0.46-0.77, and 0.91-0.97 for the x -, y -, and z -components, and 0.87-0.89, 0.40-0.83, and 0.91-0.96 when using the autocorrelation estimator. The results demonstrate that an RCA can, with just 62 receive channels, measure complex 3-D flow fields at a high volume rate.