Purpose To explore the application value of contrast enhancement boost(CE-Boost)technique in image quality of tumors and their feeding arteries in preoperative evaluation of renal cancer patients.Materials and Methods A total of 36 renal cancer patients in People's Hospital Affiliated to Shandong First Medical University from August 2022 to May 2023 with pathologically confirmed were retrospective collected.All patients underwent renal enhanced CT.The cortical phase images were post-processed using the CE-Boost technique to obtain CE-Boost images.The cortical phase images were set as group A and the CE-Boost images were set as group B.The CT value and image noise(SD)of abdominal aorta,renal artery,tumor and its adjacent renal cortex,and SD of the vertical spinal muscle on both sides of the spine of two groups were measured and recorded,and then the signal-to-noise ratio and contrast-to-noise ratio of tumor,abdominal aorta and renal artery were calculated.The image quality of the tumor,tumor feeding artery and renal artery was scored on 4 points by 2 doctors with double-blind method.Results The signal-to-noise ratio,contrast-to-noise ratio and CT value of group B were significantly higher than those of group A(t=-27.385--5.267,all P<0.05).The SD of tumor,abdominal aorta,right and left renal artery were not significantly different between group A and B(t=-1.849-0.993,all P>0.05).The subjective score of tumor in group A and B were no significant difference(Z=-1.490,P=0.136).However,the subjective score of tumor feeding arteries and renal arteries were significantly higher in group B than in group A(Z=-3.512,P=0.000;Z=-2.127,P=0.033).Conclusion The CT CE-Boost technique can improve the image quality of renal enhanced CT and provide visualization of tumor feeding arteries.

Objective To evaluate the application value of percutaneous angle positioning system based on augmented reality in improving the accuracy of liver puncture.Methods A canine liver with an embedded marking ring was used as the target for puncture.A skilled physician with over 5 years of experience in liver puncture and a novice physician with limited experience in liver puncture separately performed liver puncture using either the augmented reality-based percutaneous angle localization system(navigation)or CT-guided technique alone(non-navigation).The corresponding puncturing data of non-navigation skilled group(Group A),non-navigation non-skilled group(Group B),navigation skilled group(Group C),and navigation non-skilled group(Group D)were obtained.The differences in the evaluation indicators,including the number of CT scans,number of needle adjustment,time spent for operation,and distance of error,between Group A and Group B,between Group C and Group D,between Group A and Group C,and between Group B and Group D,were analyzed.Results Statistically significant differences in the number of CT scans,number of needle adjustment,time spent for operation,and distance of error existed between Group A and Group B,between Group A and Group C,and between Group B and Group D(all P＜0.0 5),while the differences in the number of CT scans,number of needle adjustment,time spent for operation,and distance of error between Group C and Group D were not statistically significant(all P＞0.05)Conclusion In performing liver puncture,the use of percutaneous angle localization system can reduce the number of CT scans,number of needle adjustment,time spent for operation and distance of error,and improve the puncture accuracy as well,which provides a basis for the clinical utilization of this system and the employment of this system-guided puncture technology in primary hospitals.(J Intervent Radiol,2024,33:507-511)

Objective To explore the image quality improvement of portal vein computed tomography venography(CTV)using CE-Boost technique with low dose and low contrast media usage.Methods A total of 50 patients with suspected portal vein disorders who underwent abdominal non-contrast and biphasic contrast-enhanced CT scans using the Canon 320-row CT machine were retrospectively selected.Images of portal venous phase(PVP)were postprocessed with CE-Boost technique.The CT values of each area,standard deviation(SD)values of the paraspinal muscles,volume CT dose index(CTDIvol),and dose length product(DLP)before and after CE-Boost were measured and recorded.The signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR),effective dose(ED)of each blood vessel before and after CE-Boost were calculated.Subjective image quality was analyzed by two senior radiologists using a five-point scale in a double-blinded method.Statistical analysis was performed using paired t-test and Wilcoxon test.Results The CT values of each area with CE-Boost images were significantly higher than those without CE-Boost images(P＜0.001).SNR and CNR of each blood vessel with CE-Boost images were significantly higher than those without CE-Boost images(P＜0.001).The subjective scores of both images were above 3 points,which met the requirements of clinical diagnosis with good consistency(Kappa=0.772,0.697).The median subjective scores of images with CE-Boost were 5(5,5),which were significantly higher than those without CE-Boost images 5(5,4),(P=0.002).CTDIvol,DLP and ED were(1.85±1.12)mGy,(94.66±44.68)mGy·cm and(1.42±0.67)mSv,respectively.Conclusion CE-Boost technique can significantly improve the image quality of portal vein CTV with low dose and low contrast media usage.

Objective To investigate a novel stroke recurrence risk prediction model,which utilized radiomics machine learning methods and specifically combined carotid computed tomography angiography(CT A)with the Essen stroke risk score(ESRS).Methods A total of 136 patients who underwent carotid CT A were analyzed retrospectively.The features of carotid plaque were extrac-ted by machine learning to construct a radiomics feature model,as well as combined with ESRS.Based on clinical outcomes at one-year follow-up,the stroke recurrence risk prediction model was constructed using the logistic regression(LR)machine learning model.To construct an effective and robust model,the dataset was divided into a training set and a validation set in a ratio of 7∶3.The performance of this model was evaluated using area under the curve(AUC)of receiver operating characteristic(ROC)curve,sensi-tivity and specificity.Results The model had strong predictive value.In the training set,AUC,sensitivity and specificity of this model were 0.903,0.796 and 0.761,respectively.In the validation set,AUC,sensitivity and specificity of this model were 0.869,0.667 and 0.850,respectively.Conclusion The stroke recurrence risk prediction model constructed based on radiomics analysis of carotid plaque characteristics in carotid CTA,in combination with the ESRS,can provide reliable predictions for stroke prognosis.

Objective To observe the value of augmented reality(AR)navigation system for assisting CT-guided puncture of pulmonary nodules in dog models.Methods Five healthy dogs were selected,and 4 target lung rings were implanted in each dog to build pulmonary nodule models.Deferring to crossover design,CT-guided punctures were performed with or without AR navigation 2 and 4 weeks after successful modeling,respectively,while punctures with AR navigation were regarded as AR group and the others as conventional group,respectively.The time duration of puncturing,the times of CT scanning,of needle adjustment,and the deviation distance between needle pinpoint to the center of pulmonary nodule shown on three-dimensional reconstruction were compared between groups.Results The duration time of puncture in AR group and conventional group was(13.62±5.11)min and(20.16±4.76)min,respectively.In AR group,the times of CT scanning,of needle adjustment,and the deviation distance was 2.40±0.50,2.75±0.44 and(2.94±1.92)mm,respectively,while in conventional group was 3.10±0.64,3.70±0.57 and(4.90±3.38)mm,respectively.The introduction of AR navigation was helpful to shortening the duration of puncture,reducing times of CT scanning and needle adjustment,also decreasing positioning error of needle pinpoint(all P＜0.05).In contrast,the variance of puncture sequences and dogs had no obvious effect on the results(both P＞0.05).Conclusion AR navigation system could improve accuracy and efficiency in CT-guided puncture of pulmonary nodules in dog models.

Radiomics is a non-invasive method to extract valuable features from computed tomography(CT)images to characterize the correlation between tumor phenotype and clinical treatment outcomes,which is of great significance in the evaluation of the efficacy of non-small cell lung cancer(NSCLC).This paper reviews the research methods of CT Radiomics in the evaluation of curative effect of NSCLC.Firstly,the research content of CT radiomics in NSCLC is summarized.Then,from the perspective of different treatment methods,such as namely radiotherapy and chemotherapy,targeted therapy and immunotherapy,the research methods of CT radiomics in the evaluation of NSCLC efficacy were summarized,and the CT radiomics was compared with other commonly used efficacy evaluation systems.Finally,the development trend and improvement of the application of CT radiomics in the evaluation of NSCLC curative effect were summarized and prospected.

Rectal cancer is one of the most common gastrointestinal malignancies in China. Accurate and reasonable assessment of the preoperative staging of rectal cancer can significantly enhance treatment outcomes and improve patient prognosis. Magnetic resonance imaging is the technique of choice for local staging of rectal cancer and has significant advantages in the diagnosis of rectal primary tumors (T) and peri-intestinal lymph nodes (N). In this review paper, the research ideas and progress of traditional radiomics and deep learning methods for preoperative TN staging prediction of rectal cancer were reviewed around multimodal magnetic resonance images, with the aim of providing new ideas for realizing fully automated TN staging algorithms for rectal cancer.

Objective:To design a lung function prediction method that combines transfer learning and multimodal feature fusion, aiming to improve the accuracy of lung function prediction in patients with idiopathic pulmonary fibrosis (IPF).Methods:CT images and clinical text data were reprocessed, and an adaptive module was designed to find the most suitable lung function attenuation function for IPF patients. The feature extraction module was utilized to comprehensively extract features. The feature extraction module comprises three sub-modules, including CT feature extraction, clinical text feature extraction, and lung function feature extraction. A multimodal feature prediction network was used to comprehensively evaluate the attenuation of lung function. The pre-trained model was fine-tuned to improve the predictive performance of the model.Results:Based on the OSIC pulmonary fibrosis progression competition dataset, it is found through the adaptive module that the linear attenuation hypothesis is more in line with the trend of pulmonary function decline in patients. Different modal data prediction experiments show that the model incorporating clinical text features has better predictive ability than the model using only CT images. The model combining CT images, clinical text features, and lung function features have optimal predictive results. The lung function prediction method combining transfer learning and multimodal feature fusion has modified version of the Laplace log likelihood (LLLm) of ?6.706 5, root mean squared error (RMSE) of 184.5, and mean absolute error (MAE) of 146.2, which outperforms other methods in terms of performance. The pre-trained model has higher prediction accuracy compared to the zero base training model.Conclusions:The lung function prediction method designed by combining transfer learning and multimodal feature fusion can effectively predict the lung function status of IPF patients at different weeks, providing important support for patient health management and disease diagnosis.

Lung cancer is one of the most serious malignant tumors threatening human health. Early detection and accurate risk assessment are crucial for improving the survival rate and prognosis of lung cancer patients. In this review paper, the research progress in early-stage lung cancer risk assessment methods based on predictive factors and medical imaging was summarized. The results indicated that by utilizing more diverse machine learning algorithms and larger-scale datasets, independent risk prediction factors can be further mined to achieve an accurate assessment of individual lung cancer risk.

Objective To investigate the impact of prescribed adaptive statistical iterative reconstruction V (Pre-ASiR-V) on imaging quality and radiation dosage of pure ground glass nodules (pGGN) in chest phantom,in order to obtain the optimal level of Pre-ASiR-V.Methods CT scanning for a chest phantom containing 4 artificial pGGNs was performed with Revolution CT,and the Pre-ASiR-V level was set as 0,20％,40％,60％,80％ and 100％ group,respectively.The mean noise,effective dose (ED) and the subjective scores of pGGN imaging were recorded and compared.Results The mean noise of groups (Pre-ASiR-V 0,20％,40％,60％,80％ and 100％) was (17.93±2.20)HU,(17.30±3.68)HU,(18.20± 3.44)HU,(18.80±0.20)HU,(19.87±2.56)HU and (15.90±4.56)HU,respectively (F=0.568,P=0.723).ED of these groups was 7.40 mSv,5.16 mSv,3.36 mSv,1.97 mSv,0.97 mSv and 0.33 mSv,respectively.Compared with imaging of PreASiR-V 0,the reduction percentage of ED was 30.27％,54.59％,73.38％,86.89％ and 95.54％,respectively.The subjective score of the image quality evaluated by the 2 observers had high agreement (Kappa=0.778,P=0.003),and all the scores were greater than 3.The subjective score of Pre-ASiR-V 80％ and 100％ group was slightly lower than those in other groups.Conclusion Different Pre-ASiR-V level slightly impacts the noise of high-resolution CT images of chest phantom,while reduces radiation dosage significantly.Pre-ASiR-V level of 60％ is the optimal protocol.