http://hdl.handle.net/10453/148697 2026-04-04T18:57:41Z http://hdl.handle.net/10453/180512 Title: Adaptive and Iterative Learning with Multi-perspective Regularizations for Metal Artifact Reduction. Authors: Zhang, J; Mao, H; Chang, D; Yu, H; Wu, W; Shen, D Abstract: Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.e., the errors introduced by local manipulations in the sinogram domain would propagate throughout the whole image during backprojection and lead to serious secondary artifacts, while it is difficult to distinguish artifacts from actual image features in the image domain. To alleviate these limitations, this study analyzes the desirable properties of wavelet transform in-depth and proposes to perform MAR in the wavelet domain. First, wavelet transform yields components that possess spatial correspondence with the image, thereby preventing the spread of local errors to avoid secondary artifacts. Second, using wavelet transform could facilitate identification of artifacts from image since metal artifacts are mainly high-frequency signals. Taking these advantages of the wavelet transform, this paper decomposes an image into multiple wavelet components and introduces multi-perspective regularizations into the proposed MAR model. To improve the transparency and validity of the model, all the modules in the proposed MAR model are designed to reflect their mathematical meanings. In addition, an adaptive wavelet module is also utilized to enhance the flexibility of the model. To optimize the model, an iterative algorithm is developed. The evaluation on both synthetic and real clinical datasets consistently confirms the superior performance of the proposed method over the competing methods. 2024-04-30T00:00:00Z http://hdl.handle.net/10453/180476 Title: Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks Authors: Cai, T; Lei, Q; Sheng, QZ; Cui, N; Yang, S; Yang, J; Zhang, WE; Mahmood, A Abstract: Influence Maximization (IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users' influence propagation in the future period. In this paper, we target a new research problem named, Reconnecting Top-l Relationships (RT l R) query, which aims to find l number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT l R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT l R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT l R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods. 2024-05-01T00:00:00Z http://hdl.handle.net/10453/180275 Title: Self-fusion Simplex Noise-based Diffusion Model for Self-supervised Low-dose Digital Radiography Denoising Authors: Wang, Y; Li, Z; Wu, W; Zhang, J; Wu, W Abstract: Low-dose Digital Radiography (DR) is a commonly used imaging technique in clinical practice to minimize the harmful radiation for patients. At the same time, DR images are susceptible to noise and they often have degraded information contents, calling for effective image denoising techniques. The typical existing image denoising methods are supervised and they require clean samples at the training stage. Such requirement limits their clinical applicability since the clean samples are difficult to obtain in practice. To address this challenge, we propose a self-fusion diffusion model, denoted as S3-DDPM, with simplex noise in the frequency domain. Specifically, the proposed method firstly incorporates spectrum learning to capture rich image information in various frequency ranges without requirement of clean training samples. Secondly, it utilizes simplex noise instead of the typical Gaussian noise to enhance the diversity of the generated noise. Thirdly, our method employs an alternating compensation strategy during each diffusion process, where a series of intermediate results are weighted and combined through self-fusion to derive a new denoised sample. The experimental results on clinical low-dose DR and CT images demonstrate the superiority of the proposed method over the state-of-the-art unsupervised methods, achieving favorable denoising performance. 2024-01-01T00:00:00Z http://hdl.handle.net/10453/180243 Title: Wavelet-Inspired Multi-channel Score-based Model for Limited-angle CT Reconstruction. Authors: Zhang, J; Mao, H; Wang, X; Guo, Y; Wu, W Abstract: Score-based generative model (SGM) has demonstrated great potential in the challenging limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of the ground truth data and generates reconstruction results by sampling from it. Nevertheless, direct application of the existing SGM methods to LA-CT suffers multiple limitations. Firstly, the directional distribution of the artifacts attributing to the missing angles is ignored. Secondly, the different distribution properties of the artifacts in different frequency components have not been fully explored. These drawbacks would inevitably degrade the estimation of the probability density and the reconstruction results. After an in-depth analysis of these factors, this paper proposes a Wavelet-Inspired Score-based Model (WISM) for LA-CT reconstruction. Specifically, besides training a typical SGM with the original images, the proposed method additionally performs the wavelet transform and models the probability density in each wavelet component with an extra SGM. The wavelet components preserve the spatial correspondence with the original image while performing frequency decomposition, thereby keeping the directional property of the artifacts for further analysis. On the other hand, different wavelet components possess more specific contents of the original image in different frequency ranges, simplifying the probability density modeling by decomposing the overall density into component-wise ones. The resulting two SGMs in the image-domain and wavelet-domain are integrated into a unified sampling process under the guidance of the observation data, jointly generating high-quality and consistent LA-CT reconstructions. The experimental evaluation on various datasets consistently verifies the superior performance of the proposed method over the competing method. 2024-02-19T00:00:00Z