arXiv subjects

Haodi Wu

Publications and source records attributed to Haodi Wu.

3 recordsLinked to original sources

arXiv2026

Dual-energy cardiac DSA imaging using a dual-layer flat-panel detector: in vivo validation study

Background: The temporal misregistration in conventional X-ray digital subtraction angiography (DSA) remains a key challenge for its practical application in cardiac catheterization, leading to incomplete separation of cardiac vessels from background anatomical structures. Purpose: In this study, the X-ray spectral information is unveiled for DSA imaging to generate better visualization of cardiac vessels with negligible anatomical background. Methods: A dedicated dual-energy DSA imaging model is proposed based on the subtraction residuals between pre-contrast and post-contrast spectral projections, enabling accurate material decomposition without requiring precise scatter correction. Specifically, a dual-layer flat-panel detector (DL-FPD) is assumed to acquire the needed temporally registered low-energy and high-energy projections. Moreover, a penalized likelihood material decomposition algorithm is proposed to enhance the material decomposition performance. To validate, in vivo swine experiments were conducted for the first time on a clinical DSA system equipped with a DL-FPD. Results: Monte Carlo simulation results demonstrated that the impact of scatter on the subtraction residuals can be reduced by more than 80%, substantially improving material decomposition accuracy. Compared with analytical material decomposition, the penalized-likelihood material decomposition algorithm significantly improves image contrast-to-noise ratio (CNR), enabling the visualization of small vessels and even the myocardial blush effect. Furthermore, compared with conventional dual-energy subtraction, the penalized-likelihood approach provides more quantitative material maps with fewer overlapping anatomical background artifacts. Conclusion: This preliminary study verifies that dual-energy DSA imaging method is able to generate high-quality cardiovascular imaging results.

physics.med-ph↗

arXiv2026

SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks

Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based \textbf{S}pike-\textbf{D}riven \textbf{T}racking (SDTrack) pipeline. It incorporates a novel event frame aggregation method called Global Trajectory Prompt (GTP) and a Transformer-based tracker. The GTP method effectively captures global trajectory information and aggregates it with event streams into event frames to enhance spatiotemporal representation. The Transformer-based tracker comprises a fully spike-driven SNN backbone and a simple tracking head. The SDTrack pipeline operates end-to-end without data augmentation or post-processing. Extensive experiments demonstrate that our SDTrack-Tiny pipeline achieves competitive accuracy with only 19.61$M$ parameters and 8.16$mJ$ energy consumption, while our Base version achieves state-of-the-art accuracy across three datasets. Our work establishes a solid foundation for future neuromorphic vision research.

cs.NE↗

arXiv2020

DIRECT-Net: a unified mutual-domain material decomposition network for quantitative dual-energy CT imaging

By acquiring two sets of tomographic measurements at distinct X-ray spectra, the dual-energy CT (DECT) enables quantitative material-specific imaging. However, the conventionally decomposed material basis images may encounter severe image noise amplification and artifacts, resulting in degraded image quality and decreased quantitative accuracy. Iterative DECT image reconstruction algorithms incorporating either the sinogram or the CT image prior information have shown potential advantages in noise and artifact suppression, but with the expense of large computational resource, prolonged reconstruction time, and tedious manual selections of algorithm parameters. To partially overcome these limitations, we develop a domain-transformation enabled end-to-end deep convolutional neural network (DIRECT-Net) to perform high quality DECT material decomposition. Specifically, the proposed DIRECT-Net has immediate accesses to mutual-domain data, and utilizes stacked convolution neural network (CNN) layers for noise reduction and material decomposition. The training data are numerically simulated based on the underlying physics of DECT imaging.The XCAT digital phantom, iodine solutions phantom, and biological specimen are used to validate the performance of DIRECT-Net. The qualitative and quantitative results demonstrate that this newly developed DIRECT-Net is promising in suppressing noise, improving image accuracy, and reducing computation time for future DECT imaging.

physics.med-ph↗