Objective . Ultrasound localization microscopy (ULM) enables microvascular reconstruction by localizing microbubbles (MBs). Although ULM can obtain microvascular images that are beyond the ultimate resolution of the ultrasound (US) diffraction limit, it requires long data processing time, and the imaging accuracy is susceptible to the density of MBs. Deep learning (DL)-based ULM is proposed to alleviate these limitations, which simulated MBs at low-resolution and mapped them to coordinates at high-resolution by centroid localization. However, traditional DL-based ULMs are imprecise and computationally complex. Also, the performance of DL is highly dependent on the training datasets, which are difficult to realistically simulate. Approach . A novel architecture called adaptive matching network (AM-Net) and a dataset generation method named multi-mapping (MMP) was proposed to overcome the above challenges. The imaging performance and processing time of the AM-Net have been assessed by simulation and in vivo experiments. Main results . Simulation results show that at high density (20 MBs/frame), when compared to other DL-based ULM, AM-Net achieves higher localization accuracy in the lateral/axial direction. In vivo experiment results show that the AM-Net can reconstruct ∼24.3 μ m diameter micro-vessels and separate two ∼28.3 μ m diameter micro-vessels. Furthermore, when processing a 128 × 128 pixels image in simulation experiments and an 896 × 1280 pixels image in vivo experiment, the processing time of AM-Net is ∼13 s and ∼33 s, respectively, which are 0.3–0.4 orders of magnitude faster than other DL-based ULM. Significance . We proposes a promising solution for ULM with low computing costs and high imaging performance.

Lead halide perovskites have exhibited excellent performance in solar cells, LEDs and detectors. Thermal properties of perovskites, such as heat capacity and thermal conductivity, have rarely been studied and corresponding devices have barely been explored. Considering the high absorption coefficient (10⁴~10⁵ cm⁻¹), low specific heat capacity (296–326 J kg⁻¹ K⁻¹) and small thermal diffusion coefficient (0.145 mm² s⁻¹), herein we showcase the successful use of perovskite in optoacoustic transducers. The theoretically calculated phonon spectrum shows that the overlap of optical phonons and acoustic phonons leads to the up-conversion of acoustic phonons, and thus results in experimentally measured low thermal diffusion coefficient. The assembled device of PDMS/MAPbI₃/PDMS simultaneously achieves broad bandwidths (−6 dB bandwidth: 40.8 MHz; central frequency: 29.2 MHz), and high conversion efficiency (2.97 × 10⁻²), while all these parameters are the record values for optoacoustic transducers. We also fabricate miniatured devices by assembling perovskite film onto fibers, and clearly resolve the fine structure of fisheyes, which demonstrates the strong competitiveness of perovskite based optoacoustic transducers for ultrasound imaging.

Supplying wireless power is a challenging technical problem of great importance for implantable biomedical devices. Here, we introduce a novel implantable piezoelectric ultrasound energy–harvesting device based on Sm-doped Pb(Mg _1/3 Nb _2/3 )O ₃ -PbTiO ₃ (Sm-PMN-PT) single crystal. The output power density of this device can reach up to 1.1 W/cm ² in vitro, which is 18 times higher than the previous record (60 mW/cm ² ). After being implanted in the rat brain, under 1-MHz ultrasound with a safe intensity of 212 mW/cm ² , the as-developed device can produce an instantaneous effective output power of 280 μW, which can immediately activate the periaqueductal gray brain area. The rat electrophysiological experiments under anesthesia and behavioral experiments demonstrate that our wireless-powered device is well qualified for deep brain stimulation and analgesia applications. These encouraging results provide new insights into the development of implantable devices in the future.

Objective. Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microbubbles (MBs) and achieve resolution improvement for super-resolution ultrasound (SR-US) imaging. However, previous image-denoising methods (e.g. block-matching 3D, BM3D) requires long data processing times, making ULM only able to be processed offline. This work introduces a new way to reduce data processing time through deep learning. Approach. In this study, we propose deep learning (DL) denoising based on contrastive semi-supervised network (CS-Net). The neural network is mainly trained with simulated MBs data to extract MB signals from noise. And the performances of CS-Net denoising are evaluated in both in vitro flow phantom experiment and in vivo experiment of New Zealand rabbit tumor. Main results. For in vitro flow phantom experiment, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of single microbubble image are 26.91 dB and 4.01 dB, repectively. For in vivo animal experiment , the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24 μ m and two microvessels separated by 46 μ m could be clearly displayed. Most importantly,, the CS-Net denoising speeds for in vitro and in vivo experiments were 0.041 s frame ⁻¹ and 0.062 s frame ⁻¹ , respectively. Significance. DL denoising based on CS-Net can improve the resolution of SR-US as well as reducing denoising time, thereby making further contributions to the clinical real-time imaging of ULM.

Ultrasound is extensively studied for biomedical engineering applications. As the core part of the ultrasonic system, the ultrasound transducer plays a significant role. For the purpose of meeting the requirement of precision medicine, the main challenge for the development of ultrasound transducer is to further enhance its performance. In this article, an overview of recent developments in ultrasound transducer technologies that use a variety of material strategies and device designs based on both the piezoelectric and photoacoustic mechanisms is provided. Practical applications are also presented, including ultrasound imaging, ultrasound therapy, particle/cell manipulation, drug delivery, and nerve stimulation. Finally, perspectives and opportunities are also highlighted.

High-performance lead-free K_0.5Na_0.5NbO₃ piezoelectric ceramics present a practical alternative to lead-containing counterparts by effectively reducing potential environmental hazards. This advancement is particularly relevant to the development of ferroelectric heterojunction devices for biomedical applications. Here, we design and fabricate a frequency-adjustable ferroelectric heterojunction based on the developed K_0.5Na_0.5NbO₃ piezoelectric ceramics with a high piezoelectric coefficient (d ₃₃ = 680 pC/N). By leveraging flexible encapsulation, the heterojunction achieves miniaturization (φ = 13.3 mm, h = 2.28 mm) and suitability for implantation. After penetrating the rat skull, the ultrasound generated by the heterojunction at a frequency of 3 MHz reaches a focal depth of about 7.9 mm, a focal width of approximately 480 μm at −6 dB, and millimeter-scale continuous focal tuning (1.5 mm) within a narrow frequency range (2.7–3.3 MHz). Additionally, the implanted heterojunction enables long-term and high-precision transcranial neuromodulation, and consequently yields therapeutic effects in a myocardial infarction animal model. Collectively, this study highlights a viable strategy for developing and applying lead-free ferroelectric heterojunctions, expanding their potential in brain modulation, and providing new insights into clinical treatments of myocardial infarction.