Magnetically Manipulated Optoelectronic Hybrid Microrobots for Optically Targeted Non-Genetic Neuromodulation

Optically controlled neuromodulation is a promising approach for basic research of neural circuits and the clinical treatment of neurological diseases. However, developing a non-invasive and well-controllable system to deliver accurate and effective neural stimulation is challenging. Micro/nanorobots have shown great potential in various biomedical applications because of their precise controllability. Here, a magnetically-manipulated optoelectronic hybrid microrobot (MOHR) is presented for optically targeted non-genetic neuromodulation. By integrating the magnetic component into the metal-insulator-semiconductor junction design, the MOHR has excellent magnetic controllability and optoelectronic properties. The MOHR displays a variety of magnetic manipulation modes that enables precise and efficient navigation in different biofluids. Furthermore, the MOHR could achieve precision neuromodulation at the single-cell level because of its accurate targeting ability. This neuromodulation is achieved by the MOHR's photoelectric response to visible light irradiation, which enhances the excitability of the targeted cells. Finally, it is shown that the well-controllable MOHRs effectively restore neuronal activity in neurons damaged by β-amyloid, a pathogenic agent of Alzheimer's disease. By coupling precise controllability with efficient optoelectronic properties, the hybrid microrobot system is a promising strategy for targeted on-demand optical neuromodulation.

Gradient-based adaptive wavelet de-noising method for photoacoustic imaging in vivo

Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor SNR. Conventional solution of employing higher-power laser, or doing long-time signal averaging, may raise the system cost, time consumption, and tissue damage. Another strategy is de-noising algorithm design. In this paper, we propose a gradient-based adaptive wavelet de-noising method, which sets the energy gradient mutation point of low-frequency wavelet components as the threshold. We conducted simulation, ex-vivo and in-vivo experiments using acoustic-resolution PAM. The quality of de-noised PA image/signal by our proposed algorithm has improved by at least 30%, in comparison to the traditional signal denoising algorithms, which produces better contrast and clearer details. Moreover, it produces good results when dealing with multi-layer structures. The proposed de-noising method provides potential to improve the SNR of PA signal under single-shot low-power laser illumination for biomedical applications in vivo.

Adaptive denoising of photoacoustic signal and image based on modified Kalman filter

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal-to-noise ratio, and reconstructed PA images are of low quality. Despite that conventional Kalman filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real-time estimation due to its fixed model. Moreover, KF-based denoising algorithm has not been applied in PAI before. In this paper, we propose an adaptive modified KF (MKF) targeted at PAI denoising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by MKF, we cascade the backward part of Rauch-Tung-Striebel smoother, which utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in-band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove validity of the algorithm.