In computed tomography (CT), metal artifacts pose a persistent challenge to achieving high-quality imaging. Despite advancements in metal artifact reduction (MAR) techniques, many existing approaches have not fully leveraged the intrinsic a priori knowledge related to metal artifacts, improved model interpretability, or addressed the complex texture of CT images effectively. To address these limitations, we propose a novel and interpretable framework, the wavelet-inspired oriented adaptive dictionary network (WOADNet). WOADNet builds on sparse coding with orientational information in the wavelet domain. By exploring the discriminative features of artifacts and anatomical tissues, we adopt a high-precision filter parameterization strategy that incorporates multiangle rotations. Furthermore, we integrate a reweighted sparse constraint framework into the convolutional dictionary learning process and employ a cross-space, multiscale attention mechanism to construct an adaptive convolutional dictionary unit for the artifact feature encoder. This innovative design allows for flexible adjustment of weights and convolutional representations, resulting in significant image quality improvements. The experimental results using synthetic and clinical datasets demonstrate that WOADNet outperforms both traditional and state-of-the-art MAR methods in terms of suppressing artifacts.

Deformable image registration (DIR) is critical for accurate clinical diagnosis and effective treatment planning. However, patient movement, significant intensity differences, and large breathing deformations hinder accurate anatomical alignment in multi-modal image registration. These factors exacerbate the entanglement of anatomical and modality-specific style information, thereby severely limiting the performance of multi-modal registration. To address this, we propose a novel LAtent Diffusion-based Domain-Adaptive feature disentangling (LADDA) framework for unsupervised multi-modal medical image registration, which explicitly addresses the representation disentanglement. First, LADDA extracts reliable anatomical priors from the Latent Diffusion Model (LDM), facilitating downstream content-style disentangled learning. A Domain-Adaptive Feature Disentangling (DAFD) module is proposed to promote anatomical structure alignment further. This module disentangles image features into content and style information, boosting the network to focus on cross-modal content information. Next, a Neighborhood-Preserving Hashing (NPH) is constructed to further perceive and integrate hierarchical content information through local neighbourhood encoding, thereby maintaining cross-modal structural consistency. Furthermore, a Unilateral-Query-Frozen Attention (UQFA) module is proposed to enhance the coupling between upstream prior and downstream content information. The feature interaction within intra-domain consistent structures improves the fine recovery of detailed textures. The proposed framework is extensively evaluated on large-scale multi-center datasets, demonstrating superior performance across diverse clinical scenarios and strong generalization on out-of-distribution (OOD) data.

Cone-beam Computed Tomography (CBCT) provides real-time three-dimensional (3D) imaging support for intraoperative navigation. However, high-attenuation metal implants introduce severe metal artifacts in reconstructed CBCT images. These artifacts compromise image quality and therefore may affect diagnostic accuracy. Current CBCT metal artifact reduction (MAR) algorithms overlook the complementary information available across CBCT views, leading to inaccurate projection-domain interpolation and secondary artifacts in the reconstructed images. To tackle these challenges, we propose a novel Unsupervised Projection-domain Multiview Constraint Learning Network (UPMCL-Net), which directly learns from metal-affected data for CBCT MAR without ground truths. In addition, a transformer-based MultiView Consistency Module (MVCM) is constructed to interpolate the projection-domain metal region for cross-view consistency. Finally, a Hybrid Feature Attention Module (HFAM) is designed to adaptively fuse interview and intraview features. Comprehensive experiments conducted on real clinical datasets confirm the performance of UPMCL-Net, showcasing its potential as an efficient, accurate, and reliable approach for CBCT MAR in clinical intraoperative interventions.

Computed tomography (CT) scanners are widely used to obtain detailed internal images in clinical diagnosis. Highly attenuated metallic implants resulting from strong and energy-dependent attenuation cause metal artifacts in CT scanning. However, current supervised deep network-based metal artifact reduction (MAR) methods hardly generalize in clinical diagnosis and treatment because of difficult acquisition for the paired artifact-affected and artifact-free data. In addition, these deep model-based methods cannot ensure the sinogram-domain data consistency for the exact metal trace inpainting. To address the above problems, we propose an UnsuPervised sinoGRam-domAin Data-consistEnt network for MAR, i.e., UPGRADE-Net. First, UPGRADE-Net fully leverages the prior knowledge to guide the generative conditional diffusion model for fine-grained metal trace inpainting. Second, without the artifact-free ground truth, a deep unsupervised MAR framework in the reverse process is constructed to contextually learn the known background data distribution for the unknown metal trace restoration in sinogram-domain. Third, to further maintain the sinogram-domain data consistency, two physics-based consistency constraint loss functions, including conjugate-ray and accumulation-ray consistency loss, are designed for the conjugate point constraint and the accumulation constraint. The proposed UPGRADE-Net is trained and evaluated on a publicly available dataset and a clinical dataset. Extensive experimental results validate that the proposed method outperforms the state-of-the-art competing methods for MAR.

Recent studies have shown that although DNNs perform well on visual tasks, they are still vulnerable to clean-label backdoor attacks. As poisoned samples come from the target class, the model learns both original and trigger features, weakening the association between the trigger and the target label and thereby reducing the attack success rate (ASR). To address this issue, we propose a novel clean-label backdoor attack framework, named CLEAR, i.e., Clean-Label Embedding Attack with Representation-Guidance, which strengthens the correlation between triggers and target labels while maintaining high stealth. Specifically, the "benign-label push" mechanism perturbs clean samples selected from the target class (the samples designated for poisoning), pushing them away from their original class in the feature space, while the "target-label pull" mechanism pulls the poisoned sample representations closer to the target class through saliency-guided embedding. CLEAR consists of three key components: integrating a diffusion model with PGD to generate natural but semantically perturbed adversarial samples; selecting backdoor-susceptible samples near the decision boundary based on classification loss and embedding triggers into high-saliency regions identified using a Feature Pyramid Network (FPN) combined with a local self-attention mechanism. Experimental results on CIFAR10 and GTSRB with ResNet18 and VGG16 demonstrate that CLEAR generally improves ASR while maintaining strong stealth.

Cone-beam CT is extensively used in medical diagnosis and treatment. Despite its large longitudinal field of view (FoV), the horizontal FoV of CBCT systems is severely limited due to the detector width. Certain commercial CBCT systems increase the horizontal FoV by employing the offset detector method. However, this method necessitates 360° full circular scanning trajectory which increases the scanning time and is not compatible with specific CBCT system models. In this paper, we investigate the feasibility of large FoV imaging under short scan trajectories with an additional X-ray source. A dual-source CBCT geometry is proposed as well as two corresponding image reconstruction algorithms. The first one is based on cone-parallel rebinning and the subsequent employs a modified Parker weighting scheme. Theoretical calculations demonstrate that the proposed geometry achieves a wider horizontal FoV than the ${90}\%$ detector offset geometry (radius of ${214}.{83}\textit {mm}$ vs. ${198}.{99}\textit {mm}$ ) with a significantly reduced rotation angle (less than 230° vs. 360°). As demonstrated by experiments, the proposed geometry and reconstruction algorithms obtain comparable imaging qualities within the FoV to conventional CBCT imaging techniques. Implementing the proposed geometry is straightforward and does not substantially increase development expenses. It possesses the capacity to expand CBCT applications even further.

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In computer vision and natural language processing, masked autoencoders (MAE) have been recognized as a powerful self-pretraining method for transformers, due to their exceptional capability to extract representative features. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective streamlined low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE's robustness and generability across a variety of noise levels. Experimental findings show that the proposed LoMAE enhances the denoising capabilities of the transformer and substantially reduce their dependency on high-quality, ground-truth data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. In summary, the proposed LoMAE provides promising solutions to the major issues in LDCT including interpretability, ground truth data dependency, and model robustness/generalizability.

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics.

Intraoperative Cone-Beam Computed Tomography (CBCT) provides fast multiplanar cross-sectional imaging and three-dimensional reconstructions in clinical scene. However, high-attenuation metallic implants result in the obstruction of low-energy X-rays and further lead to metal artifacts in the reconstructed CT images. Existing metal artifact reduction approaches do not consider automated fine-grained metallic implants segmentation. Imprecise metallic implants segmentation severely limits the clinical practical applicability of MAR approaches. To address the above challenge, in this study, we present a novel DUAL-domain multi-view Weak supervIsed SEgmentation network for Metal Artifact Reduction (DUALWISE-MAR) to coarse-to-fine segment metallic implants without segmentation ground-truth. First, an Iterative Segmentation Refinement Mechanism is constructed to progressively enhance initial coarse label and guide segmentation network close to the ideal metal mask. Second, a Multi-view Consistent Segmentation Network is constructed for data-consistent segmentation to capture the metal shape relationship between adjacent views in projection-domain. Third, a Segmentation Recalibration Module is proposed to calibrate the inaccurate prediction regions in the imagedomain. Extensive experiments have been conducted on the real clinical CBCT dataset and demonstrate that the proposed DUALWISE-MAR framework achieves excellent performance in metal segmentation compared to the state-of-the-art methods.

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer.

This paper presents the methods used in our submission to 2018 Facial Micro-Expression Grand Challenge (MEGC). The object of the challenge is to recognize micro-expression in two provided databases, including holdout-database recognition and composite database recognition. Considering the small size of the databases, we follow a rout of transfer learning to implement convolutional neural network to recognize the micro-expression. ResNet10 pre-trained on ImageNet dataset was fine-tuned on macro-expression datasets with large size and then on the provided micro-expression datasets. Experimental results show that the method can achieve weighted average recall (WAR) of 0.561 and unweighted average recall (UAR) of 0.389 in Holdout-database Evaluation Task, and F1 Score of 0.64 in Composite Database Evaluation Task, which are much higher than what baseline methods (LBP-TOP, HOOF, HOG3D) can achieve.