Cooperation of fuzzy segmentation operators for correction aliasing phenomenon in 3D color Doppler imaging

A Pipeline for the Generation of Synthetic Cardiac Color Doppler

Color Doppler imaging (CDI) is the modality of choice for simultaneous visualization of myocardium and intracavitary flow over a wide scan area. This visualization modality is subject to several sources of error, the main ones being aliasing and clutter. Mitigation of these artifacts is a major concern for better analysis of intracardiac flow. One option to address these issues is through simulations. In this article, we present a numerical framework for generating clinical-like CDI. Synthetic blood vector fields were obtained from a patient-specific computational fluid dynamics CFD model. Realistic texture and clutter artifacts were simulated from real clinical ultrasound cineloops. We simulated several scenarios highlighting the effects of 1) flow acceleration; 2) wall clutter; and 3) transmit wavefronts, on Doppler velocities. As a comparison, an "ideal" color Doppler was also simulated, without these harmful effects. This synthetic dataset is made publicly available and can be used to evaluate the quality of Doppler estimation techniques. Besides, this approach can be seen as a first step toward the generation of comprehensive datasets for training neural networks to improve the quality of Doppler imaging.

A Deep Learning Approach to Resolve Aliasing Artifacts in Ultrasound Color Flow Imaging

Despite being used clinically as a noninvasive flow visualization tool, color flow imaging (CFI) is known to be prone to aliasing artifacts that arise due to fast blood flow beyond the detectable limit. From a visualization standpoint, these aliasing artifacts obscure proper interpretation of flow patterns in the image view. Current solutions for resolving aliasing artifacts are typically not robust against issues such as double aliasing. In this article, we present a new dealiasing technique based on deep learning principles to resolve CFI aliasing artifacts that arise from single- and double-aliasing scenarios. It works by first using two convolutional neural networks (CNNs) to identify and segment CFI pixel positions with aliasing artifacts, and then it performs phase unwrapping at these aliased pixel positions. The CNN for aliasing identification was devised as a U-net architecture, and it was trained with in vivo CFI frames acquired from the femoral bifurcation that had known presence of single- and double-aliasing artifacts. Results show that the segmentation of aliased CFI pixels was achieved successfully with intersection over union approaching 90%. After resolving these artifacts, the dealiased CFI frames consistently rendered the femoral bifurcation's triphasic flow dynamics over a cardiac cycle. For dealiased CFI pixels, their root-mean-squared difference was 2.51% or less compared with manual dealiasing. Overall, the proposed dealiasing framework can extend the maximum flow detection limit by fivefold, thereby improving CFI's flow visualization performance.

An Extended Least Squares Method for Aliasing-Resistant Vector Velocity Estimation

An extended least squares method for robust, angle-independent 2-D vector velocity estimation using plane-wave ultrasound imaging is presented. The method utilizes a combination of least squares regression of Doppler autocorrelation estimates and block matching to obtain aliasing-resistant vector velocity estimates. It is shown that the aliasing resistance of the technique may be predicted using a single parameter, which is dependent on the selected transmit and receive steering angles. This parameter can therefore be used to design the aliasing-resistant transmit-receive setups. Furthermore, it is demonstrated that careful design of the transmit-receive steering pattern is more effective than increasing the number of Doppler measurements to obtain robust vector velocity estimates, especially in the presence of higher order aliasing. The accuracy and robustness of the method are investigated using the realistic simulations of blood flow in the carotid artery bifurcation, with velocities up to five times the Nyquist limit. Normalized root-mean-square (rms) errors are used to assess the performance of the technique. At -5 dB channel data blood SNR, rms errors in the vertical and horizontal velocity components were approximately 5% and 15% of the maximum absolute velocity, respectively. Finally, the in vivo feasibility of the technique is shown by imaging the carotid arteries of healthy volunteers.

Flow and pressure measurements in aneurysms and arteriovenous malformations with phase contrast MR imaging

PURPOSE

To explore phase contrast (PC) magnetic resonance imaging of aneurysms and arteriovenous malformations (AVM). PC imaging obtains a vector field of the velocity and can yield additional hemodynamic information, including: volume flow rate (VFR) and intravascular pressure. We expect to find lower VFR distal to aneurysms and higher VFR in vessels of the AVM.

MATERIALS AND METHODS

Five cerebral aneurysm and three AVM patients were imaged with PC techniques and compared to VFR of a healthy cohort. VFR was calculated in vessel segments in each patient and compared statistically to the healthy cohort by computing the z-score. Intravascular pressure was calculated in the aneurysms and in the nidus of each AVM.

RESULTS

We found that patients with aneurysm had z<-0.48 in vessels distal to the aneurysm (reduced flow), while AVM patients had z>6 in some vessels supplying and draining the nidus (increased flow). Pressures in aneurysms were highly variable between subjects and location, while in the nidus of the AVM patients; pressure trended higher in larger AVMs.

CONCLUSION

The study findings confirm the expectation of lower distal flow in aneurysm and higher arterial and venous flow in AVM patients.