The development of high-atomic-number (high-Z) nanomaterials as computed tomography (CT) contrast agents has attracted considerable interest because of their strong X-ray attenuation. In this study, hyaluronic acid (HA)-functionalized bismuth sulfide nanoparticles (Bi 2 S 3 @BSA-HA) were synthesized and characterized, and their CT imaging performance was evaluated over a tube-voltage range of 80–130 kVp. CT attenuation was assessed as a function of Bi concentration and tube voltage and compared with non-functionalized Bi 2 S 3 @BSA nanoparticles and the clinical iodinated contrast agent Omnipaque. Contrast-to-noise ratio (CNR), cytocompatibility, and in vitro cellular CT imaging were also evaluated. Bi 2 S 3 @BSA-HA exhibited significantly higher CT attenuation than both Bi 2 S 3 @BSA and Omnipaque, reaching 146.8 ± 6.9 HU at 1.5 mg Bi mL −1 and 80 kVp. CT attenuation increased linearly with Bi concentration (R 2 > 0.93) and decreased with increasing tube voltage. The HA-functionalized nanoparticles consistently exhibited higher CNR values across all investigated tube voltages. MTT assays demonstrated low cytotoxicity, with cell viability exceeding 90% after 24 h. In vitro cellular CT imaging showed higher CT numbers and CNR in HT-29 cells incubated with Bi 2 S 3 @BSA-HA than with Bi 2 S 3 @BSA. These findings demonstrate improved in vitro CT imaging performance with good cytocompatibility under the investigated conditions.
Although low-dose CT can effectively reduce radiation risk, the projections are contaminated by quantum noise, resulting in severe noise and artifacts in the reconstructed image, which in turn compromises the accuracy of clinical diagnosis. To tackle this problem, we propose a reconstruction network, termed CSRCT, which incorporates convolutional sparse prior and a generalized sparse transform for low-dose CT imaging. Specifically, we adopt convolutional sparse representation (CSR) to model the reconstructed image through the convolution sum of dictionary and its corresponding coefficient maps, which suppresses random noise and preserves prior information. Furthermore, we propose a generalized sparse transform that leverages a gradient network to enhance sparsity, thereby enabling effective learning of sparse features, such as image details. By employing two optimization algorithms-the Alternating Direction Method of Multipliers (ADMM) and the Chambolle-Pock (CP) method, we solve the CSRCT model by deriving two deep unrolling networks: CSRCT-ADMM and CSRCT-CP. The experiments on simulated datasets demonstrate that, compared to existing methods, CSRCT exhibits superior performance in noise removal, artifact suppression, and texture detail preservation.
BackgroundPanoramic dental X-ray segmentation is critical clinically. However, inherent challenges-low contrast, blurred boundaries, complex root apices, and variable wisdom teeth-hinder pure CNN or Transformer models from effectively extracting global structures and local fine-grained features.ObjectiveLGMUNet is proposed, a dual-branch framework seamlessly integrating Mamba's long-range dependency modeling with CNN's local fine-grained extraction. This global-local synergy not only tackles the segmentation difficulties of complex dental topologies but also eliminates the semantic gaps inherent in conventional hybrid architectures.MethodsLGMUNet employs dynamic convolutional stems in shallow layers to extract local textures. In deep layers, the LGM module first aggregates channel information through an MLP, then feeds into a parallel dual-branch architecture: the Mamba branch models long-range dependencies via cross-scanning, and the DwConv branch captures local high-frequency details, with both branches interacting at same feature depth. The model was evaluated on the multi-source STDS and MICCAI 2023 datasets.ResultsIndependent repeat trials confirm that LGMUNet achieves state-of-the-art performance. On the STDS and MICCAI 2023 datasets, it achieves Dice coefficients of 92.64 ± 0.31% and 94.00 ± 0.19%, outperforming models like nnUNet and TMamba. It demonstrates superior accuracy and clinical robustness when addressing highly challenging cases, including impacted wisdom teeth and ambiguous boundaries.
To address the severe radial-edge artifacts and the loss of feature details in CT image reconstruction, this paper proposes a novel WF-Mamba algorithm integrating the Adaptive Wavelet Frequency Interaction Network (AW-net) with the Restructured Mamba Adjustment Module (RMAM). The AW-net leverages frequency-domain properties to initially enhance high-frequency components corresponding to edges and fine details in CT images. By integrating the Transformer architecture with the learnable 2D discrete wavelet transform (2D-DWT), we design the Optimized Encoding Block (OEB) and Wavelet Decoding Block (WDB) to improve the representation of image details and textures. Additionally, the Wavelet Aggregation Unit (WAU) is proposed to further enhance reconstruction quality by facilitating effective multi-scale information interaction. The RMAM incorporates a six-directional scanning mechanism to refine the initial reconstruction results generated by the AW-net, thereby enhancing the capture capability of fine-grained information and reducing computational complexity. To optimize model performance, a hybrid objective loss function is established by combining the L2 loss with Self-Supervised Wavelet Loss (SSWL). Experimental results demonstrate that the WF-Mamba achieves superior performance in eliminating radial edge artifacts, restoring textural details, and preserving boundary features compared with other discussed methods. Quantitatively, the WF-Mamba achieves a PSNR of 31.85 dB, an SSIM of 0.948, and a normalized MSE of 6.53 × 10-4 on the actual projection dataset, outperforming all compared methods. Furthermore, the reconstructed marked contour curves more closely approximate the reference image, demonstrating highly accurate restoration of the raw detailed structure.
BACKGROUND:Automatic detection and segmentation of dental diseases from panoramic x-ray images have been a difficult task to achieve due to the problems of class imbalance and varying sizes of lesions, as well as the inconspicuous nature of early caries. This paper introduces SwinDent-Seg, which is a hybrid architecture of Swin Transformer-CNN that includes CSFF module, ECMA attention, and high-resolution P2 detection head. METHODS:The proposed model was trained and evaluated on a dataset of 1018 annotated panoramic radiographs comprising seven dental pathology classes. Model development and hyperparameter optimization were performed using a validation set (220 images). SwinDent-Seg was compared with multiple CNN- and Transformer-based baselines, including YOLOv8, YOLO11 and RT-DETR-L. A confidence-weighted ensemble combining SwinDent-Seg and YOLOv8m-seg was subsequently evaluated on an independent test set (81 images). RESULTS:On the validation set, SwinDent-Seg achieved the best single-model performance, with an mAP50 of 0.8947, outperforming all baseline models, including YOLOv8m-seg (mAP50 = 0.6770). The proposed architecture markedly improved caries detection, increasing AP50 from 0.134 to 0.8953. On the independent test set, the confidence-weighted ensemble (SwinDent-Seg + YOLOv8m-seg) achieved the best overall performance, with an mAP50 of 0.9289 and a macro-AUC of 0.9515, exceeding the performance of either individual model. CONCLUSION:SwinDent-Seg demonstrates superior single-model validation performance, while the confidence-weighted ensemble provides the highest independent test-set performance. These findings indicate that combining Transformer-based global contextual representation with CNN-based localization improves robust multi-class dental pathology segmentation and supports the development of reliable computer-aided diagnostic systems for panoramic radiographs.
