Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Functional ultrasound localization micro- scopy (fULM) enables brain-wide mapping of neural activity at micron-scale resolution but suffers from limited sensitivity due to sparse and noisy microbubble (MB) detections. Extending fULM into three dimensions (3D) further exacerbates these challenges because of low-frequency matrix arrays, reduced localization efficiency, and severe data sparsity. To address these limitations, we developed a statistical framework that models MB arrivals in 3D as a Poisson process accounting for localization efficiency, detection probability, and backscattered amplitude. This analysis predicts that integrating amplitude with count-based fULM improves functional sensitivity, particularly under high MB concentrations where localization saturates. Three-dimensional MB advection simulations confirmed these predictions, showing that backscattering fULM (B-fULM) maintains sensitivity at higher MB concentrations where conventional fULM fails. In rat brain experiments, B-fULM yielded stronger and more robust stimulus-evoked responses, with SNR gains of 18% in the somatosensory cortex and 61% in the thalamus, while preserving super-resolved spatial detail ( $33.4~\mu $ m for B-fULM vs $35.7~\mu $ m for fULM). These results establish B-fULM as a practical and sensitive approach for super-resolved 3D functional neuroimaging.

Electroencephalogram (EEG)-based emotion recognition holds significant potential in healthcare, traffic safety, and entertainment. However, cross-subject emotion recognition remains challenging due to individual differences and the difficulty in extracting domain-invariant features. To address these issues, this paper proposes a novel Prototype Regularization Domain Adaptation (PR-DA) framework. Experimental results on three benchmark datasets (SEED, SEED-IV, and SEED-VII) demonstrate that the proposed PR-DA framework achieves superior performance compared to state-of-the-art methods, with accuracies of 96.30%±2.87%, 86.56%±4.67%, and 50.43%±6.71 %, respectively. The proposed PR-DA framework of-fers a promising approach for cross-subject EEG-based emotion recognition. The source code is available at the following link: https://github.com/seizeall/PR-DA.

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of [Formula: see text] was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC’2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems.

Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.