With the development of deep learning, the medical image field has also been widely used it to assist in research, and the main research in this paper is to solve the low-dose computed tomography (LDCT) denoising problem using deep learning. Although LDCT reduces the radiation hazard to patients, it also brings more noise which has some visual interference to the doctor's judgment and affects the diagnosis result. To solve this problem, referring to the architecture of cycle-generative adversarial networks (Cycle-GAN) for unsupervised learning, this paper innovatively proposes an end-to-end unsupervised LDCT denoising framework. It combines the U-Net structure for multiscale feature extraction, the attention mechanism for feature fusion, the combination of residual network for feature transformation, and also consider the comparison of GAN network models and introduce perceptual loss to improve the network for the characteristics of medical images. In addition, we build a real LDCT database and design a large number of comparative experiments to validate the method, using both experimental standards in the image field and evaluation standards in the medical field. The main feature of this paper compared with classical methods is that this paper solves the drawback that real data cannot be used for supervised learning, while this experimental result still have quite excellent performance compared with classical excellent methods, which are professionally judged by imaging physicians and meet the clinical needs of physicians.

PET/CT image precise fusion is crucial for integrating functional metabolic information and high-resolution anatomical structures, and can significantly improve the reliability of clinical diagnosis and treatment planning. However, existing GAN-based fusion methods generally have problems such as unstable training, insufficient feature complementarity extraction, and limited cross-modal interaction. To solve the above challenges, this study proposes CMC-GAN, a cross-modal coupled generative adversarial network that combines global-local feature extraction and a dual-discriminator framework. Specifically, the coupled generator with weight sharing achieves bidirectional interaction of PET and CT features, while the global-local feature fusion module can capture fine anatomical details and large-scale contextual information. Two types of modality discriminators jointly constrain the generation process to ensure the complete fidelity of structural and functional in-formation. Experimental results on the Lung-PET-CT-Dx dataset show that CMC-GAN improves the existing best method by an average of 4.46% on six metrics including SCD, MS-SSIM, and so on, verifying its application potential in clinical PET/CT fusion.

With the widespread application of medical imaging in clinical diagnosis and intelligent analysis, the sensitive information contained in such images is facing increasingly severe risks of leakage, making steganography a critical means for safeguarding image privacy. However, existing medical image steganography methods still face significant challenges in the accurate identification and protection of diagnostically relevant regions (ROIs), adaptability to the structural characteristics of medical images, and resistance to advanced steganalysis techniques. To address these issues, this paper proposes MARS-Net, a structure-aware medical image steganography framework that integrates zero-shot medical image segmentation and reinforcement learning-based adaptive probability optimization. The proposed method can precisely localize ROIs without manual annotation, and, through the introduction of an Embeddable Suppression Module (ESM), effectively restricts embedding in structurally high-risk regions such as large all-black backgrounds. In addition, the designed reinforcement learning-based policy network incorporates multiple loss constraints—including ROI exclusion, suppression regions, and modification rate—to achieve dynamic and adaptive optimization of the embedding probability distribution. Extensive experiments on the MSD Pancreas Tumour dataset demonstrate that MARS-Net significantly outperforms traditional steganographic methods in terms of embedding security, resistance to steganalysis, and preservation of image quality in critical regions.

In this article, we focus on nonhomogeneous Markov processes (NHMPs), which are generalizations of the well-known homogeneous Markov processes (HMPs) and nonhomogeneous Poisson processes, and compare two software reliability models (SRMs) which can be classified into a generalized binomial process (GBP) and a generalized Polya process (GPP). GBP and GPP are also characterized, respectively, as a Markov inverse death process and a Markov birth process, with state- and time-dependent transition rates. We develop a unified software reliability modeling framework based on the NHMPs and apply it to the software reliability prediction. Through numerical examples with the fault count data observed in actual closed-source software (CSS) and open-source software (OSS) development projects, we compare two SRMs (GBP and GPP) in terms of the goodness-of-fit and predictive performances, in addition to the quantitative software reliability assessment. We also consider software release problems with these generalized SRMs, and investigate the impact on the software release decision.

In this paper, we present the mechanical design of a new fMRI compatible haptic interface with 3DOFs, based on electrical DC actuation, for the study of brain mechanisms of human motor control. The 1DOF manipulator, which was evaluated successfully on the compatibility with fRMI environment in the preliminary experiments, was extended to the implementation of a 3DOFs parallel manipulator with 3- UPU kinematics. Kinematic properties were studied in different configurations to select the stroke of the prismatic joint and the radius difference between the moving platform and the base. Due to the dimensional constraints imposed by the fMRI environment, the choice of the dimensions and the adopted mechanical solution was a result of an optimization process presented in this work. A further optimization of the mechanical design was then conducted in order to reduce the torque requested to the actuators for gravity compensation and improve the mechanical stiffness with elastic compliance of the manipulator. The final design resulted in a system capable of satisfying all the environment and user requirements.