Advances in super-resolution photoacoustic imaging

Photoacoustic (PA) imaging (PAI), or optoacoustic imaging, is a hybrid imaging modality that combines optical absorption contrast and ultrasound image formation. In PAI, the target is excited by a short laser pulse and subsequently absorbs the photon energy, leading to a transient local temperature rise. The temperature rise induces a local pressure rise that propagates as acoustic waves. As acoustic waves generally undergo less scattering and attenuation in tissue compared with light, PAI can provide high-resolution images in both the optical (quasi)ballistic and (quasi)diffusive regimes (1,2). Based on the image formation methods, PAI can be classified into two categories: photoacoustic microscopy (PAM) and photoacoustic computed tomography (PACT). PAM uses a focused excitation light beam and/or a focused single-element ultrasonic transducer for direct image formation through position scanning (1,2). PAM has a maximum imaging depth ranging from a few hundred micrometers to a few millimeters with spatial resolution ranging from sub-micrometer to sub-millimeter (2,3). PAM can be further classified into optical-resolution PAM (OR-PAM) and acoustic-resolution PAM (AR-PAM). For both OR-PAM and AR-PAM, the axial resolution is determined by the bandwidth of the ultrasonic transducer (4). OR-PAM works in the optical (quasi)ballistic regime, whereas the light is tightly focused that it can penetrate about one optical transport mean free path (~1 mm in soft tissue). Therefore, the lateral resolution of OR-PAM is mainly determined by the optical focal spot size (4-6). The optical focusing is diffraction-limited as λ/2NA, where λ is the light wavelength, and NA is the numerical aperture of objective lens. On the contrary, in AR-PAM, the laser is loosely focused to fulfill the entire acoustic focal spot, thereby penetrating a few optical transport mean free paths, i.e., in the quasi-diffusive regime. The lateral resolution of AR-PAM is thus determined by the size of acoustic focus (4,7,8), limited by acoustic diffraction. In PACT, the object is illuminated with a wide-field laser beam in the diffusive regime, and the generated acoustic waves are detected at multiple locations or by using a multi-element transducer array. The image formed by PACT is reconstructed by an inverse algorithm. The spatial resolution of PACT is fundamentally limited by acoustic diffraction, and additionally affected by the directionality and spacing of the detector elements (9). Recently, several studies have shown that sub-diffraction imaging of biological samples can be achieved through PAI by breaking optical-diffraction limit in the (quasi)ballistic regime or acoustic-diffraction limit in the (quasi)diffusive regime, which have opened new possibilities for fundamental biological studies. Yao et al. developed a photoimprint PAM using the intensity-dependent photobleaching effect and acquired a melanoma cell PA image with a lateral resolution of 90 nm (10). Danielli et al. reported a label-free PA nanoscopy based on the optical-absorption saturation effect and acquired a mitochondria PA image with a lateral resolution of 88 nm (11). Chaigne et al. exploited the sample-dynamics-induced inherent temporal fluctuation in the PA signals and achieved a resolution enhancement of about 1.4 over conventional PACT (12). Murray et al. broke the acoustic diffraction limit by implementing a blind speckle illumination and block-FISTA reconstruction algorithm and achieved a resolution close to the acoustic speckle size (13). Dean-Ben et al. also overcame the acoustic diffraction limit by incorporating rapid sequential acquisition of 3D PA images of flowing absorbing particles and further enhanced the visibility of structures under limited-view tomographic conditions (14). Conkey et al. optimized wavefront shaping with photoacoustic feedback and achieved up to ten times improvement in signal-to-noise ratio and five to six times sub-acoustic-diffraction resolution (15). In this concise review, we summarize and analyze the recent development in super-resolution (SR) PAI (SR-PAI) in both the optical (quasi)ballistic and (quasi)diffusive regime, as well as their representative applications. We also discuss the current challenges in SR-PAI and envision the potential breakthroughs

Investigating the effects of two virtual reality types on individual self-perception and user experience in adults : realistic humanoid vs fantastical animated avatars

Avatars, in the virtual world, have transformed our ways of interaction and experience. This study investigated the impact of avatar types in VR technologies on adults’ perception, focusing on realistic humanoid and fantastical animated entities. We explored how embodying these two avatars in a basic virtual room affect participants’ emotions, self-perception, and behaviors while taking into account their tendency to anthropomorphize non-human entities. Participants engaged in movement activities and a tiny human-like game using head-mounted displays (HMD) in a simulated environment. From 14 interviews, we identified trends in avatar type and anthropomorphism’s effects on perception shifts, emotional responses, and behavior changes related to avatar embodiment. Our findings offer valuable insights for future avatar design and research, with potential enhancements in healthcare, education, and collaborative technologies, contributing to a deeper understanding of VR embodiment and user engagement.Informatio

Reason and Guarantee Measures for Construction Cracks of Pavement Concrete in High Temperature Period

The crack problem of concrete pavement is the key to ensure road safety and long-term service. The special environment of high temperature in summer is easy to lead to concrete cracking, this paper analyzes the reason of concrete cracking according to the situation, and puts forward the maintenance measures of concrete pavement under high temperature from the aspects of raw material quality management, concrete mixing, transportation, pouring and so on, which can provide reference for the quality control of concrete road under the high temperature

FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting

Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic flow data. Existing Transformer-based methods usually treat traffic flow forecasting as multivariate time series (MTS) forecasting. However, too many sensors can cause a vector with a dimension greater than 800, which is difficult to process without information loss. In addition, these methods design complex mechanisms to capture spatial dependencies in MTS, resulting in slow forecasting speed. To solve the abovementioned problems, we propose a Fast Pure Transformer Network (FPTN) in this paper. First, the traffic flow data are divided into sequences along the sensor dimension instead of the time dimension. Then, to adequately represent complex spatio-temporal correlations, Three types of embeddings are proposed for projecting these vectors into a suitable vector space. After that, to capture the complex spatio-temporal correlations simultaneously in these vectors, we utilize Transformer encoder and stack it with several layers. Extensive experiments are conducted with 4 real-world datasets and 13 baselines, which demonstrate that FPTN outperforms the state-of-the-art on two metrics. Meanwhile, the computational time of FPTN spent is less than a quarter of other state-of-the-art Transformer-based models spent, and the requirements for computing resources are significantly reduced