OBJECTIVES: A ventricular assist device (VAD) is an electromechanical device used to assist cardiac blood circulation, which can be employed for the treatment of end-stage heart failure and is most commonly placed in the left ventricle. Despite enhancing perfusion performance, the implantation of left ventricular assist device (LVAD) transforms the local intraventricular flow and thus may increase the risk of thrombogenesis. This study aims to investigate fluid-particle interactions and thromboembolic risk under different LVAD configurations using three-dimensional (3D) reconstruction models, focusing on the effects of outflow tract orientation and blood flow rates. METHODS: A patient-specific end-diastolic 3D reconstruction model was initially constructed in stereo lithography (STL) format using Mimics software based on CT images. Transient numerical simulations were performed to analyze fluid-particle interactions and thromboembolic risks for LVAD with varying outflow tract orientations under 2 flow rates (4 L/min and 5 L/min), using particles of uniform size (2 mm), and a blood flow rate optimization protocol was implemented for this patient. RESULTS: When the LVAD flow rate was 5 L/min, helicity and flow stagnation of the blood flow increased the particle residence time (RT) and the risk of thrombogenesis of the aortic root. The percentage of particles traveling toward the brachiocephalic trunk was up to 20.33%. When the LVAD flow rate was 4 L/min, blood turbulence in the aorta was reduced, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 10.54%. When the LVAD blood flow rate was 5 L/min and the direction of the outflow pipe was optimal, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 11.22%. A 18-month follow-up observation of the patient revealed that the LVAD was in good working order and the patient had no complications related to the implantation of LVAD. CONCLUSIONS Implantation of LVAD results in a higher risk of cerebral infarction; When implanting LVAD with the same outflow tract direction, optimizing flow velocity and outflow tract can reduce the risk of cerebral infarction occurrence.
Heart-Assist Devices/adverse effects* ; Humans ; Heart Failure/physiopathology* ; Blood Flow Velocity ; Thromboembolism/prevention & control* ; Models, Cardiovascular ; Heart Ventricles/physiopathology* ; Imaging, Three-Dimensional

Sympathectomy, as an emerging treatment method for cardiovascular diseases, has received extensive attention in recent years. Stereotactic radiotherapy (SRT), a precise and noninvasive therapeutic technique, has gradually been introduced into interventions targeting the sympathetic nervous system and has shown promising prospects in the management of cardiovascular conditions. Using three-dimensional imaging, SRT can accurately localize sympathetic ganglia and deliver high-energy radiation to disrupt nerve fibers, thereby achieving effects similar to conventional sympathectomy while reducing surgery-related complications and shortening recovery time. It also offers the advantages of being noninvasive and causing fewer adverse effects, and thus holds potential as an alternative to traditional approaches in the future. The integration of SRT with sympathectomy opens new avenues for the treatment of cardiovascular diseases and presents broad clinical application prospects.
Radiosurgery/methods* ; Cardiovascular Diseases/radiotherapy* ; Humans ; Imaging, Three-Dimensional ; Ganglionectomy/methods* ; Ganglia, Sympathetic/radiation effects* ; Blood Vessels/physiopathology* ; Heart/physiopathology*

The internal structures of cells as the basic units of life are a major wonder of the microscopic world. Cellular images provide an intriguing window to help explore and understand the composition and function of these structures. Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications. This study presents an innovative diffusion model-based approach for style transfer in cellular images, combining scientific rigor with artistic expression. By leveraging training-free large-scale pre-trained diffusion models, the proposed method integrates the intricate morphological and textural features of cellular images with diverse artistic styles. Key techniques such as the inversion of denoising diffusion implicit models (DDIMs), adaptive instance normalization (AdaIN), self-attention style injection, and attention temperature scaling ensure the preservation of cellular structures while enhancing visual expressiveness. The results showcase the potential of this strategy for interdisciplinary applications, enriching both the visualization and educational dissemination of cellular imagery through compelling storytelling and aesthetic appeal.
Humans ; Image Processing, Computer-Assisted/methods* ; Cells ; Diffusion

OBJECTIVES: We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion. METHODS: The sub-pixel intensity values and their second-order differences were obtained using linear interpolation techniques, and the new gradient information was then embedded into an anisotropic diffusion process, which was introduced into a penalty-weighted least squares model to reduce the noise in low-dose CT projection data. The high-quality CT image was finally reconstructed using the classical filtered back-projection (FBP) algorithm from the estimated data. RESULTS: In the Shepp-Logan phantom experiments, the structural similarity (SSIM) index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 28.13%, 5.49%, and 0.91%, the feature similarity (FSIM) index was increased by 21.08%, 1.78%, and 1.36%, and the root mean square error (RMSE) was reduced by 69.59%, 18.96%, and 3.90%, respectively. In the digital XCAT phantom experiments, the SSIM index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 14.24%, 1.43% and 7.89%, the FSIM index was increased by 9.61%, 1.78% and 5.66%, and the RMSE was reduced by 26.88%, 9.41% and 18.39%, respectively. In clinical experiments, the SSIM index of the image reconstructed using the proposed algorithm was increased by 19.24%, 15.63% and 3.68%, the FSIM index was increased by 4.30%, 2.92% and 0.43%, and the RMSE was reduced by 44.60%, 36.84% and 15.22% in comparison with FBP, PWLS-Gibbs and PWLS-TV algorithms, respectively. CONCLUSIONS The proposed method can effectively reduce the noises and artifacts while maintaining the structural details in low-dose CT images.
Tomography, X-Ray Computed/methods* ; Algorithms ; Phantoms, Imaging ; Anisotropy ; Image Processing, Computer-Assisted/methods* ; Humans ; Radiation Dosage

OBJECTIVES: We propose a sparse-view cone-beam CT reconstruction algorithm based on bidirectional flow field guided projection completion (BBC-Recon) to solve the ill-posed inverse problem in sparse-view cone-beam CT imaging. METHODS: The BBC-Recon method consists of two main modules: the projection completion module and the image restoration module. Based on flow field estimation, the projection completion module, through the designed bidirectional and multi-scale correlators, fully calculates the correlation information and redundant information among projections to precisely guide the generation of bidirectional flow fields and missing frames, thus achieving high-precision completion of missing projections and obtaining pseudo complete projections. The image restoration module reconstructs the obtained pseudo complete projections and then refines the image to remove the residual artifacts and further improve the image quality. RESULTS: The experimental results on the public datasets of Mayo Clinic and Guilin Medical University showed that in the case of a 4-fold sparse angle, compared with the suboptimal method, the BBC-Recon method increased the PSNR index by 1.80% and the SSIM index by 0.29%, and reduced the RMSE index by 4.12%; In the case of an 8-fold sparse angle, the BBC-Recon method increased the PSNR index by 1.43% and the SSIM index by 1.49%, and reduced the RMSE index by 0.77%. CONCLUSIONS The BBC-Recon algorithm fully exploits the correlation information between projections to allow effective removal of streak artifacts while preserving image structure information, and demonstrates significant advantages in maintaining inter-slice consistency.
Algorithms ; Cone-Beam Computed Tomography/methods* ; Image Processing, Computer-Assisted/methods* ; Humans

OBJECTIVES: We propose a segmented backprojection tensor degradation feature encoding (SBP-MAC) model for motion artifact correction in dental cone beam computed tomography (CBCT) to improve the quality of the reconstructed images. METHODS: The proposed motion artifact correction model consists of a generator and a degradation encoder. The segmented limited-angle reconstructed sub-images are stacked into the tensors and used as the model input. A degradation encoder is used to extract spatially varying motion information in the tensor, and the generator's skip connection features are adaptively modulated to guide the model for correcting artifacts caused by different motion waveforms. The artifact consistency loss function was designed to simplify the learning task of the generator. RESULTS: The proposed model could effectively remove motion artifacts and improve the quality of the reconstructed images. For simulated data, the proposed model increased the peak signal-to-noise ratio by 8.28%, increased the structural similarity index measurement by 2.29%, and decreased the root mean square error by 23.84%. For real clinical data, the proposed model achieved the highest expert score of 4.4221 (against a 5-point scale), which was significantly higher than those of all the other comparison methods. CONCLUSIONS The SBP-MAC model can effectively extract spatially varying motion information in the tensors and achieve adaptive artifact correction from the tensor domain to the image domain to improve the quality of reconstructed dental CBCT images.
Cone-Beam Computed Tomography/methods* ; Artifacts ; Humans ; Motion ; Image Processing, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

OBJECTIVES: We propose a novel deep learning segmentation algorithm (DSRF) based on multi-scale supervision and residual feedback strategy for precise segmentation of the optic chiasm and optic nerves in CT images of nasopharyngeal carcinoma (NPC) patients. METHODS: We collected 212 NPC CT images and their ground truth labels from SegRap2023, StructSeg2019 and HaN-Seg2023 datasets. Based on a hybrid pooling strategy, we designed a decoder (HPS) to reduce small organ feature loss during pooling in convolutional neural networks. This decoder uses adaptive and average pooling to refine high-level semantic features, which are integrated with primary semantic features to enable network learning of finer feature details. We employed multi-scale deep supervision layers to learn rich multi-scale and multi-level semantic features under deep supervision, thereby enhancing boundary identification of the optic chiasm and optic nerves. A residual feedback module that enables multiple iterations of the network was designed for contrast enhancement of the optic chiasm and optic nerves in CT images by utilizing information from fuzzy boundaries and easily confused regions to iteratively refine segmentation results under supervision. The entire segmentation framework was optimized with the loss from each iteration to enhance segmentation accuracy and boundary clarity. Ablation experiments and comparative experiments were conducted to evaluate the effectiveness of each component and the performance of the proposed model. RESULTS: The DSRF algorithm could effectively enhance feature representation of small organs to achieve accurate segmentation of the optic chiasm and optic nerves with an average DSC of 0.837 and an ASSD of 0.351. Ablation experiments further verified the contributions of each component in the DSRF method. CONCLUSIONS The proposed deep learning segmentation algorithm can effectively enhance feature representation to achieve accurate segmentation of the optic chiasm and optic nerves in CT images of NPC.
Humans ; Tomography, X-Ray Computed/methods* ; Optic Chiasm/diagnostic imaging* ; Optic Nerve/diagnostic imaging* ; Algorithms ; Nasopharyngeal Carcinoma ; Deep Learning ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods*

OBJECTIVES: We propose a low-dose CT image restoration method based on central guidance and alternating optimization (FedGP). METHODS: The FedGP framework revolutionizes the traditional federated learning model by adopting a structure without a fixed central server, where each institution alternatively serves as the central server. This method uses an institution-modulated CT image restoration network as the core of client-side local training. Through a federated learning approach of central guidance and alternating optimization, the central server leverages local labeled data to guide client-side network training to enhance the generalization capability of the CT imaging model across multiple institutions. RESULTS: In the low-dose and sparse-view CT image restoration tasks, the FedGP method showed significant advantages in both visual and quantitative evaluation and achieved the highest PSNR (40.25 and 38.84), the highest SSIM (0.95 and 0.92), and the lowest RMSE (2.39 and 2.56). Ablation study of FedGP demonstrated that compared with FedGP(w/o GP) without central guidance, the FedGP method better adapted to data heterogeneity across institutions, thus ensuring robustness and generalization capability of the model in different imaging conditions. CONCLUSIONS FedGP provides a more flexible FL framework to solve the problem of CT imaging heterogeneity and well adapts to multi-institutional data characteristics to improve generalization ability of the model under diverse imaging geometric configurations.
Tomography, X-Ray Computed/methods* ; Humans ; Radiation Dosage ; Image Processing, Computer-Assisted/methods* ; Algorithms

OBJECTIVES: To establish a three-dimensional (3D) visualization-based classification of the left hepatic portal vein (LHPV) and left hepatic vein (LHV) systems using 3D reconstruction technology to facilitate precise segmental/subsegmental resection of left liver lesions. METHODS: Thin-slice contrast-enhanced CT datasets from 244 patients were reconstructed using MI-3DV Works software. The spatial anatomy (origins, branching patterns, and spatial relationships) of the LHPV and LHV branches was analyzed to determine their 3D classifications and segmental liver divisions for guiding surgical planning for anatomical left liver resections. RESULTS: The 3D models of the third- and fourth-order branches of the LHPV and LHV were successfully reconstructed for all the 244 patients. Two types of the LHPV system were identified, where the LHPV either had independent origins [242 cases (99.1%)] or had right anterior portal branches arising from the LHPV trunk [2 cases (0.9%)]. 3D classifications identified two types of the Segment II of the LHPV (based on branch number), 3 types of the Segment III (by spatial distribution of the branches), compact vs dispersed types of the left lateral lobe (determined by Segment II/III branches proximity), 3 types of the Segment IV (by branch number and origin), and 3 types the fourth hilar vessels (transverse branches of the left portal vein) for their supplied segments. The LHV system had two drainage types into the inferior vena cava, and the umbilical fissure veins were classified into 3 types by drainage patterns and distance to the venous roots. These classifications combined with liver segmentations allowed individualized surgical planning for segment-specific resections. CONCLUSIONS The 3D classification of the LHPV and LHV provides valuable clinical guidance for precise anatomical resections of left liver lesions using liver segments or subsegments as anatomical units to enhance surgical accuracy and improve the outcomes of hepatobiliary surgery.
Humans ; Hepatectomy/methods* ; Imaging, Three-Dimensional ; Hepatic Veins/anatomy & histology* ; Portal Vein/anatomy & histology* ; Liver/surgery* ; Liver Neoplasms/blood supply* ; Tomography, X-Ray Computed ; Female

OBJECTIVES: We propose a multi-scale jaw cyst segmentation model, AConvLSTM U-Net, which is based on bidirectional dense connections and attention mechanisms to achieve accurate automatic segmentation of mandibular cyst images. METHODS: A dataset consisting of 2592 jaw cyst images was used. AConvLSTM U-Net designs a MBC on the encoding path to enhance feature extraction capabilities. A DPD was used to connect the encoder and decoder, and a bidirectional ConvLSTM was introduced in the jump connection to obtain rich semantic information. A decoding block based on scSE was then used on the decoding path to enhance the focus on important information. Finally, a DS was designed, and the model was optimized by integrating a joint loss function to further improve the segmentation accuracy. RESULTS: The experiment with AConvLSTM U-Net for jaw cyst lesion segmentation showed a MCC of 93.8443%, a DSC of 93.9067%, and a JSC of 88.5133%, outperforming all the other comparison segmentation models. CONCLUSIONS The proposed algorithm shows a high accuracy and robustness on the jaw cyst dataset, demonstrating its superior performance over many existing methods for automatic segmentation of jaw cyst images and its potential to assist clinical diagnosis.
Humans ; Jaw Cysts/diagnostic imaging* ; Algorithms ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer