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.
Super-resolution imaging (SRI) can achieve subwavelength resolution by detecting and tracking intravenously injected microbubbles (MBs) over time. However, current SRI is limited by long data acquisition times since the MB detection still relies on diffraction-limited conventional ultrasound images. This limits the number of detectable MBs in a fixed time duration. In this work, we propose a deep learning-based method for detecting and localizing high-density multiple point targets from radio frequency (RF) channel data. A Convolutional Neural Network (CNN) was trained to return confidence maps given RF channel data, and the positions of point targets were estimated from the confidence maps. RF channel data for training and evaluation were simulated in Field II by placing point targets randomly in the region of interest and transmitting three steered plane waves. The trained CNN achieved a precision and recall of 0.999 and 0.960 on a simulated test dataset. The localization errors after excluding outliers were within ± 46 µm and ± 27 µm in the lateral and axial directions. A scatterer phantom was 3-D printed and imaged by the Synthetic Aperture Real-time Ultrasound System (SARUS). On measured data, a precision and recall of 0.976 and 0.998 were achieved, and the localization errors after excluding outliers were within ± 101 µm and ± 75 µm in the lateral and axial directions. We expect that this method can be extended to highly concentrated microbubble (MB) detection in order to accelerate SRI.
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