Synthetic microswimmers are a class of artificial nano- or microscale particle capable of converting external energy into motion. They are similar to natural microswimmers such as bacteria in behavior and are, therefore, of great interest to the study of active matter. Additionally, microswimmers show promise in applications ranging from bioanalytics and environmental monitoring to particle separation and drug delivery. However, since their sizes are on the nano-/microscale and their speeds are in the μm s(-1) range, they fall into a low Reynolds number regime where viscosity dominates. Therefore, new propulsion schemes are needed for these microswimmers to be able to efficiently move. Furthermore, many of the hotly pursued applications call for innovations in the next phase of development of biocompatible microswimmers. In this review, the latest developments of microswimmers powered by ultrasound are presented. Ultrasound, especially at MHz frequencies, does little harm to biological samples and provides an advantageous and well-controlled means to efficiently power microswimmers. By critically reviewing the recent progress in this research field, an introduction of how ultrasound propels colloidal particles into autonomous motion is presented, as well as how this propulsion can be used to achieve preliminary but promising applications.

No abstract

This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, DeepcomplexMRI takes advantage of the availability of a large number of existing multi-channel groudtruth images and uses them as target data to train the deep residual convolutional neural network offline. In particular, a complex convolutional network is proposed to take into account the correlation between the real and imaginary parts of MR images. In addition, the kspace data consistency is further enforced repeatedly in between layers of the network. The evaluations on in vivo datasets show that the proposed method has the capability to recover the desired multi-channel images. Its comparison with state-of-theart method also demonstrates that the proposed method can reconstruct the desired MR images more accurately.

No abstract