Removing Artifacts in Transcranial Photoacoustic Imaging With Polarized Self-Attention Dense-UNet.

OBJECTIVE: Photoacoustic imaging (PAI) is a promising transcranial imaging technique. However, the distortion of photoacoustic signals induced by the skull significantly influences its imaging quality. We aimed to use deep learning for removing artifacts in PAI. METHODS: In this study, we propose a polarized self-attention dense U-Net, termed PSAD-UNet, to correct the distortion and accurately recover imaged objects beneath bone plates. To evaluate the performance of the proposed method, a series of experiments was performed using a custom-built PAI system. RESULTS: The experimental results showed that the proposed PSAD-UNet method could effectively implement transcranial PAI through a one- or two-layer bone plate. Compared with the conventional delay-and-sum and classical U-Net methods, PSAD-UNet can diminish the influence of bone plates and provide high-quality PAI results in terms of structural similarity and peak signal-to-noise ratio. The 3-D experimental results further confirm the feasibility of PSAD-UNet in 3-D transcranial imaging. CONCLUSION: PSAD-UNet paves the way for implementing transcranial PAI with high imaging accuracy, which reveals broad application prospects in preclinical and clinical fields.

Impact of seasonal changes in urban green spaces with diverse vegetation structures on college students' physical and mental health.

Based on the perceptions of college student participants in winter and summer, the effects of different vegetation structures within landscapes (single-layer woodland, tree-shrub-grass composite woodlands, tree-grass composite woodland, and single-layer grassland) and concrete squares without plants were investigated, and the skin conductivity level (SCL) and environmental perception recovery score (PRS) associated with landscape types were calculated. The results indicated that seasonal differences in landscape perception significantly affected college student participants' PRS but not their SCL scores, both in winter and summer. Viewing single-layer and tree-shrub-grass composite woodlands in summer, as well as single-layer woodland in winter, enhanced the environmental perception of the college student participants. The restorative effects of the four vegetation types in green spaces were ranked as follows: single-layer woodland, tree-shrub-grass composite woodlands, single-layer grassland, and tree-grass composite woodlands and concrete squares without plants. These findings underscore the importance of considering seasonal variations when choosing plant species for landscaping purposes, with evergreen single-layer woodland being a suitable choice for winter urban landscapes. This provides a scientific basis for assessing landscape perception and preferences in the future.

BDIS-SLAM: a lightweight CPU-based dense stereo SLAM for surgery.

PURPOSE: Common dense stereo simultaneous localization and mapping (SLAM) approaches in minimally invasive surgery (MIS) require high-end parallel computational resources for real-time implementation. Yet, it is not always feasible since the computational resources should be allocated to other tasks like segmentation, detection, and tracking. To solve the problem of limited parallel computational power, this research aims at a lightweight dense stereo SLAM system that works on a single-core CPU and achieves real-time performance (more than 30 Hz in typical scenarios). METHODS: A new dense stereo mapping module is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our new dense stereo mapping module includes stereo matching and 3D dense depth mosaic methods. Stereo matching is achieved with the recently proposed CPU-level real-time matching algorithm Bayesian Dense Inverse Searching (BDIS). A BDIS-based shape recovery and a depth mosaic strategy are integrated as a new thread and coupled with the backbone ORB-SLAM2 system for real-time stereo shape recovery. RESULTS: Experiments on in vivo data sets show that BDIS-SLAM runs at over 30 Hz speed on modern single-core CPU in typical endoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional 12 % time compared with the backbone ORB-SLAM2. Although our lightweight BDIS-SLAM simplifies the process by ignoring deformation and fusion procedures, it can provide a usable dense mapping for modern MIS on computationally constrained devices. CONCLUSION: The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS. It achieves 30 Hz on a modern single-core CPU in typical endoscopy/colonoscopy scenarios (image size around 640 × 480 ). BDIS-SLAM provides a low-cost solution for dense mapping in MIS and has the potential to be applied in surgical robots and AR systems. Code is available at https://github.com/JingweiSong/BDIS-SLAM .