arXiv subjects

Kailiang Xu

Publications and source records attributed to Kailiang Xu.

4 recordsLinked to original sources

arXiv2026

Cross Fusion and Correlation Beamformer for Row-Column Array Based 3D Ultrasound Imaging

Row column addressed (RCA) transducers present a promising solution for ultrafast volumetric imaging with a reduced channel count and a large field of view. However, RCA-based 3D imaging is fundamentally limited by severe sidelobe artifacts and a low signal-to-noise ratio (SNR), primarily due to weak transmit focusing inherent in RCA based ultrafast imaging strategies. To overcome these challenges, we propose a cross fusion and correlation (CFAC) method that leverages the incoherence of sidelobe artifacts and noise across datasets acquired using orthogonal apertures and multiple steering angle sets. The performance of the proposed method was validated through simulations, in vitro imaging of a multi-purpose ultrasound phantom, and in vivo experiments, and benchmarked against four established techniques: orthogonal plane wave (OPW) imaging, XDoppler method, row-column-specific frame-multiply-and-sum beamforming (RC-FMAS), and coherent factor (CF) imaging. Simulation results demonstrated that CFAC reduced sidelobe levels by 42.0 dB, 38.9 dB, 28.3 dB, and 25.5 dB compared to OPW, XDoppler, RC-FMAS, and CF, respectively. In phantom experiments, CFAC improved the CNR by up to 17.5 dB. Furthermore, in vivo imaging of a rat kidney showed that CFAC enables visualization of a significantly more detailed microvascular network, achieving a CNR improvement of over 25 dB against all benchmarked methods. In conclusion, the proposed CFAC method effectively suppresses sidelobe artifacts and noise in RCA-based imaging under low-SNR conditions, enabling high-contrast 3D visualization while preserving the high frame rate capabilities of ultrafast ultrasound imaging.

physics.med-ph↗

arXiv2025

Deep-Learning Based Super-Resolution Functional Ultrasound Imaging of Transient Brain-Wide Neurovascular Activity on a Microscopic Scale

Transient brain-wide neuroimaging on a microscopic scale is pivotal for brain research, yet existing imaging modalities face challenges in meeting such spatiotemporal requirements. Functional ultrasound (fUS) enables transient neurovascular imaging through red blood cell backscattering, but suffers from diffraction-limited spatial resolution. Functional ultrasound localization microscopy (fULM) has addressed this limitation by integrating ULM with fUS; but this approach requires repeated stimulation and data accumulation. Here, we introduce super-resolution functional ultrasound (SR-fUS), a deep learning-based framework that reconstructs super-resolution ULM images from contrast-free ultrafast Doppler data. By incorporating red blood cell radial gradient fluctuation priors with uncertainty-driven loss, SR-fUS enables microscopic scale hemodynamic imaging with 25-{\mu}m spatial spatial resolution. In rat brains, SR-fUS visualized transient pain-evoked hemodynamic responses, distinguished stimulus-specific microvascular activation patterns during single-whisker stimulation, and dynamically tracked isoflurane anesthesia-induced microvascular dilation. The accuracy of SR-fUS was further preliminarily assessed through a comparative study with two-photon microscopy.

physics.med-ph↗

arXiv2025

Ultrafast ultrasound coded vector Doppler imaging of blood flow velocity and resistivity

Dynamic and precise measurement of cerebral blood flow velocity is crucial in neuroscience and the diagnosis of cerebrovascular diseases. Traditional color Doppler ultrasound can only measure the velocity component along the ultrasound beam, which restricts its ability to accurately capture the complete blood flow vector in complex environments. To overcome these limitations, we propose an ultrafast pulse-coded vector Doppler (PC-UVD) imaging method, utilizing Hadamard matrix-based pulse encoding to improve velocity estimation accuracy under low signal-to-noise ratio (SNR) conditions. Our study encompasses spiral flow simulations and in vivo rat brain experiments, showing significantly enhanced measurement precision compared to conventional ultrafast vector Doppler (UVD). This innovative approach enables the measurement of dynamic cerebral blood flow velocity within a single cardiac cycle, offering insights into the characteristics of cerebrovascular resistivity. The proposed PC-UVD method employs Hadamard matrix encoding of plane waves, boosting SNR without compromising temporal or spatial resolution. Velocity vectors are subsequently estimated using a weighted least squares (WLS) approach, with iterative residual-based weight optimization improving robustness to noise and minimizing the impact of outliers. The effectiveness of this technique is confirmed through simulations with a spiral blood flow phantom, demonstrating a marked improvement in velocity estimation accuracy, particularly in deep imaging regions with significant signal attenuation. In vivo experiments on rat brains further confirm that the proposed method offers greater accuracy than existing UVD approaches, particularly for small vessels. Notably, our method can precisely differentiate arterial from venous flow by analyzing pulsatility and resistivity within the cerebral vascular network.

physics.med-ph↗

arXiv2024

The Phantom of PCIe: Constraining Generative Artificial Intelligences for Practical Peripherals Trace Synthesizing

Peripheral Component Interconnect Express (PCIe) is the de facto interconnect standard for high-speed peripherals and CPUs. The development of PCIe devices for emerging applications requires realistic Transaction Layer Packet (TLP) traces that accurately simulate device-CPU interactions. While generative AI offers a promising avenue for synthesizing complex TLP sequences, it is prone to a critical challenge inherent in all generation tasks: hallucination. Naively applying these models often produces traces that violate fundamental PCIe protocol rules, such as ordering and causality, rendering them unusable for device simulation. To resolve this, our work introduces a methodology to bridge the gap between generative AI and high-fidelity device simulation. This paper presents Phantom, a framework that systematically addresses AI-generated hallucinations in TLP synthesis. Phantom achieves this by coupling a generative backbone with a novel post-processing filter that enforces PCIe-specific constraints, effectively eliminating invalid TLP sequences. We validate Phantom's effectiveness by synthesizing TLP traces for an actual PCIe network interface card. Experimental results show that Phantom produces practical, large-scale TLP traces, significantly outperforming existing models, with improvements of up to 1000$\times$ in task-specific metrics and up to 2.19$\times$ in Fr\'echet Inception Distance (FID) compared to backbone-only methods. The prototype implementation has been made open-source.

cs.LG↗