High-precision motion estimation has become essential in ultrasound-based techniques such as time-domain Doppler and elastography. Normalized cross-correlation (NCC) has been shown as one of the best motion estimators. However, a significant drawback is its associated computational cost, especially when RF signals are used. In this paper, a method based on sum tables developed elsewhere is adapted for fast NCC calculation in ultrasound-based motion estimation, and is tested with respect to the speed enhancement of the specific application of ultrasound-based motion estimation. Both the numerator and denominator in the NCC definition are obtained through pre-calculated sum tables to eliminate redundancy of repeated NCC calculations. Unlike a previously reported method, a search region following the principle of motion estimation is applied in the construction of sum tables. Because an exhaustive search and high window overlap are typically used for highest quality imaging, the computational cost of the proposed method is significantly lower than that of the direct method using the NCC definition, without increasing bias and variance characteristics of the motion estimation or sacrificing the spatial resolution. Therefore, high quality, high spatial resolution, and high calculation speed can be all simultaneously obtained using the proposed methodology. The high efficiency of this method was verified using RF signals from a human abdominal aorta in vivo. For the parameters typically used, a real-time, very high frame rate of 310 frames/s was achieved for the motion estimation. The proposed method was also extended to 2-D NCC motion estimation and motion estimation with other algorithms. The technique could thus prove very useful and flexible for real-time motion estimation as well as in other fields such as optical flow and image registration.

Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and with the fast iterative shrinkage algorithm. We show that our architecture exhibits better image quality and contrast.

One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images and transcend the trade-off between spatial resolution and imaging speed. We compared various convolutional neural network (CNN) architectures, and selected a Fully Dense U-net (FD U-net) model that produced the best results. To mimic various undersampling conditions in practice, we artificially downsampled fully-sampled PAM images of mouse brain vasculature at different ratios. This allowed us to not only definitively establish the ground truth, but also train and test our deep learning model at various imaging conditions. Our results and numerical analysis have collectively demonstrated the robust performance of our model to reconstruct PAM images with as few as 2% of the original pixels, which can effectively shorten the imaging time without substantially sacrificing the image quality.

By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy (ULM) has recently been proposed, which greatly improves the spatial resolution of ultrasound (US) imaging and will be helpful for clinical diagnosis. Nevertheless, several challenges remain in fast ULM imaging. The main problems are that current localization methods used to implement fast ULM imaging, e.g., a previously reported localization method based on sparse recovery (CS-ULM), suffer from long data-processing time and exhaustive parameter tuning (optimization). To address these problems, in this paper, we propose a ULM method based on deep learning, which is achieved by using a modified sub-pixel convolutional neural network (CNN), termed as mSPCN-ULM. Simulations and in vivo experiments are performed to evaluate the performance of mSPCN-ULM. Simulation results show that even if under high-density condition (6.4 MBs/mm2), a high localization precision ( 28 lm in the lateral direction and 24 lm in the axial direction) and a high localization reliability (Jaccard index of 0.66) can be obtained by mSPCN-ULM, compared to CS-ULM. The in vivo experimental results indicate that with plane wave scan at a transmit center frequency of 15.625 MHz, microvessels with diameters of 17 lm can be detected and adjacent microvessels with a distance of 42 lm can be separated. Furthermore, when using GPU acceleration, the data-processing time of mSPCN-ULM can be shortened to 6 sec/frame in the simulations and 23 sec/frame in the in vivo experiments, which is 3-4 orders of magnitude faster than CS-ULM. Finally, once the network is trained,mSPCN-ULM does not need parameter tuning to implement ULM. As a result, mSPCN-ULM opens the door to implement ULM with fast data-processing speed, high imaging accuracy, short data-acquisition time, and high flexibility (robustness to parameters) characteristics.