Direct parametric reconstruction algorithms have been developed to improve the statistical reliability of parametric images estimated from dynamic PET imaging data. However, these estimates are degraded by noise due to measurement error and noise propagation during reconstruction. In this study, we develop a deep image prior (DIP) regularized direct reconstruction method, where the DIP network is used to represent the estimated parametric image. By initializing the DIP with pre-trained weights and updating its network to learn the intermediate information during reconstruction, the DIP regularization leverages the available population and subject-specific features. The proposed method is applied to reconstructK1from both simulated and patient data acquired by82Rb dynamic PET myocardial perfusion imaging. Benefiting from the nonlinear representation capability of the DIP network, the proposed method achieves superior noise versus bias/mean performance compared with the indirect and direct reconstruction methods with various regularizations formed by quadratic smoothness, dictionary learning, or fully-connected neural network. To summarize, the proposed method demonstrates its potential in improving the precision of dynamic PET imaging measurements, which will contribute to diagnostic accuracy and disease monitoring.

Cyanine-based molecules have gained significant attention in photothermal therapy due to their unique fluorescence brightness and tunable spectral properties. However, the development of new photothermal agents is often constrained by the complexity of the chemical landscape and the need for biocompatibility. To address these challenges, we present an innovative transfer learning approach for rapidly identifying promising photothermal agent candidates with excellent photothermal properties, high synthetic feasibility, and superior biocompatibility. Using natural language processing, our pretrained model generated a molecular library based on cyanine scaffolds. The most promising candidates were screened rigorously through a weighted analysis of chemical indicators, such as photothermal performance and synthetic accessibility and biological indicators, including bio-toxicity. From these, three molecules were selected for retrosynthetic analysis. This artificial intelligence-driven approach provides a robust solution to the traditional challenges in photothermal agent design, significantly enhancing their potential applications in cancer bioimaging, mitochondrial phototherapy, and image-guided surgery.

This study focuses on enhancing the precision and efficacy of pedicle screw placement in spinal surgeries, particularly for patients with osteoporosis. It emphasizes the importance of accurate screw positioning to maximize pullout strength and biomechanical efficacy. The research highlights the relationship between CT values, Bone Mineral Density (BMD), and the Young’s modulus of bone tissue, suggesting that higher CT values, indicative of denser bone, lead to stronger mechanical properties. This understanding is crucial for assessing bone health, especially in osteoporosis and fracture risk analysis. The study introduces an innovative automated planning method for pedicle screw insertion in spinal vertebrae, beneficial for osteoporotic patients. This method uses PointNet++ combined with a Siamese network for semi-supervised segmentation of vertebrae in CT images, converting them into point clouds for individualized planning. The approach aims to improve accuracy and efficiency in pedicle screw placement, especially in complex or osteoporotic vertebrae. The method was validated using osteoporotic in vitro models, demonstrating its potential effectiveness for surgeons facing challenges in pedicle screw placement in osteoporotic patients. The complete workflow begins with semi-supervised vertebra segmentation using a Siamese PointNet++ architecture. The extracted vertebra point cloud is then aligned with a predefined pedicle screw model via an adaptively tuned Super4PCS registration algorithm to generate patient-specific trajectories. The paper also reviews traditional surgical path planning, which relied on surgeons’ experience and intuition, and the shift towards Computer-Aided Design (CAD) and Virtual Reality (VR) technologies for pre-operative planning. Despite these advancements, challenges remain in accurately simulating tissue properties and managing physiological variations. The study proposes a bifurcated approach to automated trajectory planning: segmenting individual vertebrae and planning pedicle screw implantation for each segmented vertebra. The method involves converting CT scans into 3D point clouds, using PointNet++ and the Siamese method for vertebra segmentation, and registering the pedicle screw point cloud with the vertebra model for precise surgical planning. The study demonstrates the method’s feasibility through clinical data from Shengjing Hospital, showing high applicability and consistency in surgical path planning, particularly in the lumbar and lower thoracic regions. The planning results were consistent with surgeons’ experience, indicating the algorithm’s adaptability and stability.

In myocardial perfusion imaging with dynamic positron emission tomography (PET), direct parametric reconstruction from the projection data allows accurate modeling of the Poisson noise in the projection domain to provide more reliable estimate of the parametric images. In this study, we propose to incorporate a superior denoiser to efficiently suppress the unfavorable noise propagation during the direct reconstruction. The dictionary learning (DL) based sparse representation serves as a regularization term to constrain the intermediate${K}_{{1}}$estimation. We rewrite the DL regularizer into a voxel-separable form to facilitate the decoupling of a DL penalized curve fitting from the reconstruction of dynamic frames. The nonlinear fitting is then solved by a damped Newton method with uniform initialization. Using simulated and patient82Rb dynamic PET data, we study the performance of the proposed DL direct algorithm and quantitatively compare it with the indirect method with or without post-filtering, the direct reconstruction without regularization, and the quadratic penalty regularized direct algorithm. The DL regularized direct reconstruction achieves improved noise versus bias performance in the reconstructed${K}_{{1}}$images as well as superior recovery of a reduced myocardial blood flow defect. The dictionary learned from a 3D self-created hollow sphere image yields comparable results to those using the dictionary learned from the corresponding magnetic resonance image. The uniform initializations converge to${K}_{{1}}$estimations similar to the result from initializing with the indirect reconstruction. To summarize, we demonstrate the potential of the proposed DL constrained direct parametric reconstruction in improving quantitative dynamic PET imaging.

Multiagent-based system has been widely used in hotel reservations. However, they mainly use one-way negotiation for decisions that agents only represent travelers selecting from a list of hotels, while hotel providers cannot communicate with travelers. To address such problems, this paper proposes a Multiagent-Based Two-Way Negotiation for Hotel Reservation (MAB-TNHR) with three kinds of agents. Traditional tenant-dominant reservation is converted into a form of tendering to implement this two-way negotiation, which means that Landlord Agents can actively respond to the selection by sending their tenders to suitable tenants and bargain on the price with the Tenant Agent to pursue their interests. In this paper, rules used in the application and a case study are presented to illustrate the implementation of MAB-TNHR. Verification shows that the MAB-TNHR can meet intricate and dynamic reservation demands automatically without the participations of tenants and landlords.