OBJECTIVE: To compare the effectiveness of O-arm navigation and C-arm navigation for guiding percutaneous long sacroiliac screws in treatment of Denis type Ⅱ sacral fractures. METHODS: A retrospective study was conducted on clinical data of the 46 patients with Denis type Ⅱ sacral fractures between April 2021 and October 2022. Among them, 19 patients underwent O-arm navigation assisted percutaneous long sacroiliac screw fixation (O-arm navigation group), and 27 patients underwent C-arm navigation assisted percutaneous long sacroiliac screw fixation (C-arm navigation group). There was no significant difference in gender, age, causes of injuries, Tile classification of pelvic fractures, combined injury, the interval from injury to operation between the two groups ( P>0.05). The intraoperative preparation time, the placement time of each screw, the fluoroscopy time of each screw during placement, screw position accuracy, the quality of fracture reduction, and fracture healing time were recorded and compared, postoperative complications were observed. Pelvic function was evaluated by Majeed score at last follow-up. RESULTS: All operations were completed successfully, and all incisions healed by first intention. Compared to the C-arm navigation group, the O-arm navigation group had shorter intraoperative preparation time, placement time of each screw, and fluoroscopy time, with significant differences ( P<0.05). There was no significant difference in screw position accuracy and the quality of fracture reduction ( P>0.05). There was no nerve or vascular injury during screw placed in the two groups. All patients in both groups were followed up, with the follow-up time of 6-21 months (mean, 12.0 months). Imaging re-examination showed that both groups achieved bony healing, and there was no significant difference in fracture healing time between the two groups ( P>0.05). During follow-up, there was no postoperative complications, such as screw loosening and breaking or loss of fracture reduction. At last follow-up, there was no significant difference in pelvic function between the two groups ( P>0.05). CONCLUSION Compared with the C-arm navigation, the O-arm navigation assisted percutaneous long sacroiliac screws for the treatment of Denis typeⅡsacral fractures can significantly shorten the intraoperative preparation time, screw placement time, and fluoroscopy time, improve the accuracy of screw placement, and obtain clearer navigation images.
Humans ; Fracture Fixation, Internal/methods* ; Retrospective Studies ; Imaging, Three-Dimensional ; Bone Screws ; Surgery, Computer-Assisted ; Tomography, X-Ray Computed ; Spinal Fractures/surgery* ; Fractures, Bone/surgery* ; Pelvic Bones/injuries* ; Postoperative Complications ; Neck Injuries

Objective:To explore the characteristics and therapeutic strategies of Pott's puffy tumor（PPT）. Methods:The clinical data of two patients with PPT were retrospectively analyzed and combined with the literature, focusing on the comprehensive analysis of perioperative diagnosis and treatment strategies. Both patients underwent muti-disciplinary treatment, including timely administration of sufficient antibiotics capable of penetrating the blood-brain barrier. Early removal of PPT lesions was performed using a combined internal and external approach under nasal endoscopic guidance. Results:After standardized perioperative management, the symptoms of the two patients were completely relieved, with no recurrence after one=year follow=up. Postoperative complications such as frontal pain, numbness, local depression, or scar hyperplasiawere not present. Conclusion:PPT, being relatively rare and severe, requires careful attention. Key strategies for standardized perioperative management include multi-disciplinary consultation, timely and adequate antibiotic administration, and surgical intervention using a combined intranasal and extranasal endoscopic approach for lesion removal.
Humans ; Pott Puffy Tumor/complications* ; Retrospective Studies ; Tomography, X-Ray Computed ; Endoscopy/adverse effects* ; Postoperative Complications ; Anti-Bacterial Agents/therapeutic use* ; Frontal Sinusitis/complications*

Humans ; Female ; Pregnancy ; Placenta ; Lung/pathology* ; Tomography, X-Ray Computed

Humans ; Meningioma/surgery* ; Tomography, X-Ray Computed ; Meningeal Neoplasms/surgery*

OBJECTIVE: To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging. METHODS: The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm. RESULTS: Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size. CONCLUSIONS A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.
Tomography, X-Ray Computed/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Signal-To-Noise Ratio ; Perception

OBJECTIVE: To propose a non-contrast CT-based algorithm for automated and accurate detection of pancreatic lesions at a low cost. METHODS: With Faster RCNN as the benchmark model, an advanced Faster RCNN (aFaster RCNN) model for pancreatic lesions detection based on plain CT was constructed. The model uses the residual connection network Resnet50 as the feature extraction module to extract the deep image features of pancreatic lesions. According to the morphology of pancreatic lesions, 9 anchor frame sizes were redesigned to construct the RPN module. A new Bounding Box regression loss function was proposed to constrain the training process of RPN module regression subnetwork by comprehensively considering the constraints of the lesion shape and anatomical structure. Finally, a detection frame was generated using the detector in the second stage. The data from a total of 728 cases of pancreatic diseases from 4 clinical centers in China were used for training (518 cases, 71.15%) and testing (210 cases, 28.85%) of the model. The performance of aFaster RCNN was verified through ablation experiments and comparison experiments with 3 classical target detection models SSD, YOLO and CenterNet. RESULTS: The aFaster RCNN model for pancreatic lesion detection achieved recall rates of 73.64% at the image level and 92.38% at the patient level, with an average precision of 45.29% and 53.80% at the image and patient levels, respectively, which were higher than those of the 3 models for comparison. CONCLUSION The proposed method can effectively extract the imaging features of pancreatic lesions from non-contrast CT images to detect the pancreatic lesions.
Humans ; Pancreas/diagnostic imaging* ; Algorithms ; China ; Pancreatic Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed

OBJECTIVE: To propose a tissue- aware contrast enhancement network (T- ACEnet) for CT image enhancement and validate its accuracy in CT image organ segmentation tasks. METHODS: The original CT images were mapped to generate low dynamic grayscale images with lung and soft tissue window contrasts, and the supervised sub-network learned to recognize the optimal window width and level setting of the lung and abdominal soft tissues via the lung mask. The self-supervised sub-network then used the extreme value suppression loss function to preserve more organ edge structure information. The images generated by the T-ACEnet were fed into the segmentation network to segment multiple abdominal organs. RESULTS: The images obtained by T-ACEnet were capable of providing more window setting information in a single image, which allowed the physicians to conduct preliminary screening of the lesions. Compared with the suboptimal methods, T-ACE images achieved improvements by 0.51, 0.26, 0.10, and 14.14 in SSIM, QABF, VIFF, and PSNR metrics, respectively, with a reduced MSE by an order of magnitude. When T-ACE images were used as input for segmentation networks, the organ segmentation accuracy could be effectively improved without changing the model as compared with the original CT images. All the 5 segmentation quantitative indices were improved, with the maximum improvement of 4.16%. CONCLUSION The T-ACEnet can perceptually improve the contrast of organ tissues and provide more comprehensive and continuous diagnostic information, and the T-ACE images generated using this method can significantly improve the performance of organ segmentation tasks.
Learning ; Image Enhancement ; Tomography, X-Ray Computed

OBJECTIVE: To propose a framework that combines sinogram interpolation with unsupervised image-to-image translation (UNIT) network to correct metal artifacts in CT images. METHODS: The initially corrected CT image and the prior image without artifacts, which were considered as different elements in two different domains, were input into the image transformation network to obtain the corrected image. Verification experiments were carried out to assess the effectiveness of the proposed method using the simulation data, and PSNR and SSIM were calculated for quantitative evaluation of the performance of the method. RESULTS: The experiment using the simulation data showed that the proposed method achieved better results for improving image quality as compared with other methods, and the corrected images preserved more details and structures. Compared with ADN algorithm, the proposed algorithm improved the PSNR and SSIM by 2.4449 and 0.0023 when the metal was small, by 5.9942 and 8.8388 for images with large metals, and by 8.8388 and 0.0130 when both small and large metals were present, respectively. CONCLUSION The proposed method for metal artifact correction can effectively remove metal artifacts, improve image quality, and preserve more details and structures on CT images.
Artifacts ; Algorithms ; Computer Simulation ; Tomography, X-Ray Computed

BACKGROUND: The effect of arteriosclerotic intracranial arterial vessel wall enhancement (IAVWE) on downstream collateral flow found in vessel wall imaging (VWI) is not clear. Regardless of the mechanism underlying IAVWE on VWI, damage to the patient's nervous system caused by IAVWE is likely achieved by affecting downstream cerebral blood flow. The present study aimed to investigate the effect of arteriosclerotic IAVWE on downstream collateral flow. METHODS: The present study recruited 63 consecutive patients at the Second Hospital of Hebei Medical University from January 2021 to November 2021 with underlying atherosclerotic diseases and unilateral middle cerebral artery (MCA) M1-segment stenosis who underwent an magnetic resonance scan within 3 days of symptom onset. The patients were divided into 4 groups according to IAVWE and the stenosis ratio (Group 1, n = 17; Group 2, n = 19; Group 3, n = 13; Group 4, n = 14), and downstream collateral flow was analyzed using three-dimensional pseudocontinuous arterial spin labeling (3D-pCASL) and RAPID software. The National Institutes of Health Stroke Scale (NIHSS) scores of the patients were also recorded. Two-factor multivariate analysis of variance using Pillai's trace was used as the main statistical method. RESULTS: No statistically significant difference was found in baseline demographic characteristics among the groups. IAVWE, but not the stenosis ratio, had a statistically significant significance on the late-arriving retrograde flow proportion (LARFP), hypoperfusion intensity ratio (HIR), and NIHSS scores ( F = 20.941, P <0.001, Pillai's trace statistic = 0.567). The between-subject effects test showed that IAVWE had a significant effect on the three dependent variables: LARFP ( R2 = 0.088, F = 10.899, P = 0.002), HIR ( R2 = 0.234, F = 29.354, P <0.001), and NIHSS ( R2 = 114.339, F = 33.338, P <0.001). CONCLUSIONS: Arteriosclerotic IAVWE significantly reduced downstream collateral flow and affected relevant neurological deficits. It was an independent factor affecting downstream collateral flow and NIHSS scores, which should be a focus of future studies. TRIAL REGISTRATION ChiCTR.org.cn, ChiCTR2100053661.
Humans ; Constriction, Pathologic/pathology* ; Magnetic Resonance Imaging/methods* ; Middle Cerebral Artery/pathology* ; Tomography, X-Ray Computed

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.
Humans ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Lung Neoplasms/drug therapy* ; B7-H1 Antigen/therapeutic use* ; Retrospective Studies ; Programmed Cell Death 1 Receptor ; Tomography, X-Ray Computed ; Immunotherapy