arXiv subjects

Huiying Pan

Publications and source records attributed to Huiying Pan.

4 recordsLinked to original sources

arXiv2026

A scattering correction method for CT reconstruction based on the Wavelet Adaptive Material-dependent Boltzmann Transport Equation (WAM-BTE)

X-ray computed tomography (CT) is an essential imaging technology in clinical diagnosis. However, scattered photons can reduce image contrast and introduce CT value bias, which severely degrades image quality. Recently, scatter correction methods based on the Boltzmann transport equation (BTE) have attracted increasing attention due to their high physical accuracy and flexibility. Nevertheless, existing BTE-based methods usually employ single-material models, which cannot accurately describe the nonlinear energy dependence of photon interaction cross-sections in different materials. In this work, a scatter correction method based on the wavelet adaptive material-dependent BTE (WAM-BTE) is proposed. The conventional single-material model is extended to a multi-material model by introducing material-dependent scattering distributions. Furthermore, an adaptive multi-scale framework is established through wavelet decomposition. The low-frequency wavelet coefficients are used for coarse-scale scatter estimation to reduce computational complexity, while the high-frequency wavelet energy of high attenuation materials is utilized for adaptive local refinement. Theoretical analysis demonstrates that, when using the Haar basis function, the low-frequency wavelet coefficients at the $w$-th level are mathematically equivalent to block-average downsampling with a scale factor of $2^w$, except for a deterministic normalization factor. Experimental results show that the proposed WAM-BTE method achieves comparable accuracy to the Monte Carlo method while preserving the computational efficiency of coarse-scale estimation. The scatter calculation time for a single projection view is reduced to the millisecond level.

physics.med-ph↗

arXiv2025

Physics-Inspired Gaussian Kolmogorov-Arnold Networks for X-ray Scatter Correction in Cone-Beam CT

Cone-beam CT (CBCT) employs a flat-panel detector to achieve three-dimensional imaging with high spatial resolution. However, CBCT is susceptible to scatter during data acquisition, which introduces CT value bias and reduced tissue contrast in the reconstructed images, ultimately degrading diagnostic accuracy. To address this issue, we propose a deep learning-based scatter artifact correction method inspired by physical prior knowledge. Leveraging the fact that the observed point scatter probability density distribution exhibits rotational symmetry in the projection domain. The method uses Gaussian Radial Basis Functions (RBF) to model the point scatter function and embeds it into the Kolmogorov-Arnold Networks (KAN) layer, which provides efficient nonlinear mapping capabilities for learning high-dimensional scatter features. By incorporating the physical characteristics of the scattered photon distribution together with the complex function mapping capacity of KAN, the model improves its ability to accurately represent scatter. The effectiveness of the method is validated through both synthetic and real-scan experiments. Experimental results show that the model can effectively correct the scatter artifacts in the reconstructed images and is superior to the current methods in terms of quantitative metrics.

cs.CV↗

arXiv2024

First performance of hybrid spectra CT reconstruction: a general Spectrum-Model-Aided Reconstruction Technique (SMART)

Hybrid spectral CT integrates energy integrating detectors (EID) and photon counting detectors (PCD) into a single system, combining the large field-of-view advantage of EID with the high energy and spatial resolution of PCD. This represents a new research direction in spectral CT imaging. However, the different imaging principles and inconsistent geometric paths of the two detectors make it difficult to reconstruct images using data from hybrid detectors. In addition, the quality reconstructed images considering spectrum is affected by the accuracy of spectral estimation and the scattered photons. In this work, Firstly, we propose a general hybrid spectral reconstruction method that takes into account both the spectral CT imaging principles of the two different detectors and the influence of scattered photons in the forward process modelling. Furthermore, we also apply volume fraction constraints to the results reconstructed from the two detector data. By alternately solving the spectral estimation and the spectral image reconstruction by the ADMM method, the estimated spectra and the reconstructed images reinforce each other, thus improving the accuracy of the spectral estimation and the quality of the reconstructed images. The proposed method is the first to achieve hybrid spectral CT reconstruction for both detectors, allowing simultaneous recovery of spectrum and image reconstruction from hybrid spectral data containing scattering. In addition, the method is also applicable to spectral CT imaging using a single type of detector. We validated the effectiveness of the proposed method through numerical experiments and successfully performed the first hybrid spectral CT reconstruction experiment on our self-developed hybrid spectral CT system.

physics.med-ph↗

arXiv2022

Fast Iterative Reconstruction for Multi-spectral CT by a Schmidt Orthogonal Modification Algorithm (SOMA)

Multi-spectral CT (MSCT) is increasingly used in industrial non-destructive testing and medical diagnosis because of its outstanding performance like material distinguishability. The process of obtaining MSCT data can be modeled as nonlinear equations and the basis material decomposition comes down to the inverse problem of the nonlinear equations. For different spectra data, geometric inconsistent parameters cause geometrical inconsistent rays, which will lead to mismatched nonlinear equations. How to solve the mismatched nonlinear equations accurately and quickly is a hot issue. This paper proposes a general iterative method to invert the mismatched nonlinear equations and develops Schmidt orthogonalization to accelerate convergence. The validity of the proposed method is verified by MSCT basis material decomposition experiments. The results show that the proposed method can decompose the basis material images accurately and improve the convergence speed greatly.

math.NA↗