Journal of Computer Assisted Tomography最新文献

Objective: To evaluate the diagnostic value of ultra-high-resolution computed tomography (UHCT) in assessing small airway disease (SAD) in patients with emphysema and analyse its correlation with quantitative expiratory CT parameters, while comparing the performance of different CT systems.

Methods: In this prospective study, 120 emphysema patients underwent CT scanning using 3 different systems: GE Revolution CT (256-slice), Philips IQon Elite Spectral CT, and GE Discovery CT750 HD (64-slice). Small airway measurements were performed from the third to seventh-generation airways, and quantitative CT parameters were analysed.

Results: The GE Revolution CT (256-slice) enabled visualization of airways up to the seventh generation in 82% of cases, compared with 68% using Philips IQon Elite Spectral CT and 53% using GE Discovery CT750 HD ( P <0.001). Fourth-generation airway WA% showed strong negative correlation with FEV1% predicted ( r =-0.72, P <0.001). The GE Revolution CT (256-slice) demonstrated superior reproducibility for wall-area measurements and lower radiation doses while maintaining superior image quality.

Conclusion: Ultra-high-resolution CT demonstrates superior capabilities in evaluating SAD in patients with emphysema, providing more precise and reproducible measurements compared with conventional CT systems. The strong correlations between structural and functional parameters validate the clinical relevance of these imaging biomarkers. The improved visualisation and quantification of distal airways, achieved with acceptable radiation exposure, represents an important advancement in SAD assessment.

Objectives: This study aimed to simultaneously evaluate 4D cardiac magnetic resonance (MR)-derived kinetic energy (KE) of intra-left ventricular (LV) blood flow, wall kinetics, and myocardial perfusion under pharmacological stress in patients with coronary artery disease (CAD) using a hybrid PET/MR system.

Methods: Sixty-five patients (mean 68±12 y; male, 53) with CAD who underwent rest-stress 13 N-ammonia PET/MR were included. MR acquisition was performed simultaneously during the PET scan to obtain rest-stress 4D flow, and followed by cine MR to measure LV ejection fraction (LVEF) and myocardial strain, including global longitudinal strain (GLS). The maximum KE during the cardiac cycle was calculated and indexed to the end-diastolic LV volume (maxKEi, μJ/mL) at rest and during stress. Perfusion defect, myocardial flow (MBF), and flow reserve (MFR) were assessed through rest-stress PET.

Results: MaxKEi showed a significant correlation with LVEF and GLS for both rest and stress (r=0.3, P =0.01, r=-0.4, P =0.04 for rest LVEF and GLS; r=0.4, P =0.0009, r=-0.4, P =0.003, for stress LVEF and GLS, respectively). Stress maxKEi showed a significant correlation with stress MBF and MFR (r=0.3, P =0.006, and r=0.3, P =0.03, for stress MBF and MFR, respectively).

Conclusion: Noninvasive assessment of 4D flow MR-derived intra-LV KE demonstrated a significant association with wall kinetics and endothelial function under pharmacological stress.

Objective: This study aims to investigate the diagnostic value of dual-energy computed tomography (DECT)-derived quantitative imaging parameters, including arterial iodine fraction (AIF), extracellular volume fraction (fECV), and iodine washout rate (IWR), in the assessment of liver function grading in patients with cirrhosis, to identify the optimal diagnostic parameters.

Methods: We collected DECT data from 68 patients with cirrhosis and 25 healthy controls at our institution and used the albumin-bilirubin (ALBI) scoring system for liver function grading. DECT images were processed for iodine quantification, and AIF, fECV, and IWR parameters were calculated. The correlation between DECT iodine parameters and ALBI grading was analyzed using the Spearman correlation coefficient; independent-sample t tests or nonparametric Mann-Whitney U tests were used to compare the differences in parameters between the cirrhosis group and the normal control group. The nonparametric Kruskal-Wallis H test was used to compare DECT iodine parameters across different ALBI groups within the cirrhosis cohort. The diagnostic efficacy of the iodine parameters in discriminating different ALBI grades was analyzed using the receiver operating characteristic (ROC) curve.

Results: Significant correlations were observed between AIF, fECV, and IWR with ALBI grades ( r =0.873, 0.908, and -0.846, respectively; all P <0.001). In the cirrhosis group, AIF, fECV, and IWR exhibited statistically significant differences across ALBI grades (all P <0.001). The AUC values of fECV for distinguishing Control versus ALBI1+2+3, ALBI1 versus ALBI2+3, and ALBI1+2 versus ALBI3 were 0.955 (0.918 to 0.993), 0.945 (0.900 to 0.994), and 0.974 (0.942 to 1.000), respectively, with corresponding optimal cutoff values of 27.41%, 29.34%, and 35.40%, the diagnostic effect was better than AIF and IWR.

Conclusion: DECT-derived AIF, fECV, and IWR effectively assess liver function in cirrhosis through ALBI grading, with fECV showing superior standalone performance; multiparametric integration enhances diagnostic accuracy.

Objectives: To determine the feasibility of reduced-dose chest computed tomographic angiography (CTA) with convolutional neural network (CNN) denoising for detecting pulmonary arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (cwHHT).

Methods: Fifteen cwHHT underwent a chest CTA (ie, a controlled "study" dose). Noise was inserted to simulate a quarter dose (QD) exam. Images were reconstructed using iterative reconstruction (IR) and our self-trained CNN denoising model. For each case, 3 sets of images were created: study dose (SD)+IR, QD+IR, and QD+CNN. Two thoracic radiologists independently scored each set to assess quality, spatial resolution, artifacts, and the presence of pAVMs using 4-level ordinal scales. Quantitative assessments of image quality were performed using contrast-to-noise ratios (CNRs) with comparisons made between the experimental conditions.

Results: Thirteen of the 15 patients recruited with hereditary hemorrhagic telangiectasia (mean age: 9.3±4.5 y) were positive for pAVM by transthoracic contrast echocardiography. The sensitivities using QD+CNN were 0.85 and 1.00 for readers 1 and 2, respectively. This was compared with 0.69 and 0.84 using QD+IR versus 0.85 and 0.92 for SD+IR. Inter-reader agreement for pAVM detection utilizing QD+CNN was moderate and resulted in kappa=0.59 ( P =0.012). The subjective assessments for QD+CNN were comparable to the SD technique. Regression analysis of reader scores revealed improved quality in QD+CNN versus QD+IR ( P =0.001). Similarly, the QD+CNN condition demonstrated the highest CNRs.

Conclusions: Reduced-dose chest CTA with CNN denoising provides a level of sensitivity comparable to standard dose CTA and high CNRs for the detection of pAVMs in cwHHT.

Diseases involving the pediatric masticator space often vary from those encountered in adults, with congenital abnormalities encountered more commonly and the assortment of benign and malignant neoplasms encountered differing in type and frequency. Familiarity with the imaging findings for these diseases is important for timely and accurate diagnosis and management. This article briefly reviews masticator space anatomy and provides detailed description of the imaging findings associated with benign and malignant diseases that may be encountered in the pediatric masticator space.

Infective endocarditis is a serious infection of the heart's inner lining and valves, with a high risk of systemic complications due to septic emboli. These complications can affect various organs, including the brain, lungs, abdomen, vasculature, and musculoskeletal system. Diagnosing infective endocarditis can be challenging, often with underappreciated complications that significantly impact treatment decisions, including the potential need for surgery. While echocardiography remains the primary diagnostic imaging modality, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) can be crucial for identifying and characterizing complications. This comprehensive review emphasizes the key role of radiologists in identifying secondary features of infective endocarditis, which can manifest in various organs. It explores the diverse presentations of infective endocarditis through patient cases, highlighting the strengths of different imaging modalities-echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-in diagnosing cardiovascular, pulmonary, and systemic complications. Understanding the imaging spectrum of infective endocarditis, is essential to enhancing diagnostic accuracy, guiding treatment decisions, and improving patient outcomes.

Objective: Our aim is to evaluate the impact of preoperative cardiac CT on LAAC.

Methods: This research followed the protocols outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist, and it adhered to the previously described established methodologies. A search strategy was designed to utilize PubMed, EMBASE, Cochrane Library, and Web of Science for studies showing the effect of preoperative CCTA on LAAC from December 08, 2017, to June 12, 2023. For continuous outcome variables, the weighted mean difference (WMD) was used to estimate the effect size, whereas the odds ratio (OR) was used for dichotomous outcomes. We performed meta-regression to explore the heterogeneity among the included studies.

Results: Eight cohort studies (including one published only as an abstract) that evaluated the impact of preoperative CCTA for LAAC were identified and included in this meta-analysis. Compared with the CCTA negative group, patients in the CCTA positive group experienced a significantly shorter LAAC procedure time (WMD: -0.69; 95% CI: -1.11 to -0.28; P =0.00; I ²=95.39%). In contrast, there were no significant differences in implantation success (OR: 1.04; 95% CI: 0.98-1.11; P =0.18; I ²=45.61%), contrast volume (WMD: -0.07; 95% CI: -0.28 to 0.14; P =0.51; I ²=77.38%), peri-device leak (OR: 0.56; 95% CI: 0.29-1.11; P =0.10; I ²=87.33%), device-related thrombus (OR: 0.70; 95% CI: 0.36-1.35; P =0.29; I ²=0%), pericardial effusion requiring intervention (OR: 1.09; 95% CI: 0.95-1.25; P =0.21; I ²=0%), major adverse events (OR: 0.99; 95% CI: 0.89-1.09; P =0.78; I ²=0%), and all-cause mortality (OR: 0.79; 95% CI: 0.54-1.16; P = 0.23; I ²=0%).

Conclusions: Preoperative CCTA is associated with a shorter procedure time, but other parameters did not differ significantly between patients who underwent preoperative CCTA and those who did not.

Objective: Low keV virtual monoenergetic (VME) images are effective in enhancing vessel opacification but require dual-energy CT (DECT), limiting widespread clinical use. Recent advancements in deep learning (DL) enable the generation of VME images from single-energy CT (SECT). However, the performance of the methods has not been evaluated in any clinical use case. The purpose of this work was to assess both objective and subjective image quality of deep learning-based VME images derived from heterogeneous SECT data for pulmonary angiography.

Methods: In this retrospective study, 52 sets of SECT pulmonary angiography images were processed using a deep learning method to estimate material basis images. 40 keV VME images were generated from heterogeneous SECT data using a pretrained physics-constrained Deep-En-Chroma DL model. Two thoracic radiologists, blinded to the image reconstruction method, evaluated pulmonary vessel opacification and overall image quality on DL-VME and SECT images using 5-point Likert scales. Objective image quality was assessed by measuring enhanced vessel contrast and contrast-to-noise ratio (CNR). Statistical analysis was performed using paired t tests and Mann-Whitney U tests.

Results: Compared with SECT, DL-VME images demonstrated significantly higher subjective image quality score and vessel opacification score ( P ≤0.008). DL-VME yielded a higher average contrast for emboli (1085 vs. 331 HU, P <0.001) and improved CNR (17.8 vs. 11.1, P <0.001). Results of subgroup analysis indicate no significant variation in VME performance across patient sex, scanner model, radiation dose, and tube potential. The vessel opacification scores of both VME and SECT demonstrate dependence on patient weight, with VME providing better vessel opacity for both lighter and heavier patients.

Conclusions: A measure of 40 keV DL-VME derived from SECT effectively enhances both vessel opacification and image quality in CT pulmonary angiography. The image quality advantage of DL-VME over SECT remains robust across variations in data acquisition and patient variables.

Purpose: To evaluate the feasibility of intravoxel incoherent motion (IVIM) MRI for noninvasive assessment of uterine perfusion and diffusion characteristics across different phases of the menstrual cycle.

Methods: This prospective study included 27 volunteers with regular menstrual cycles (28±7 d) from December 2020 to May 2023. A zoomed field-of-view single-shot spin-echo echo-planar imaging (SE-EPI) sequence was used to obtain multi-b DWI images. Mono-exponential DWI derived apparent diffusion coefficient (ADC) and IVIM-derived parameters including diffusion D, pseudo diffusion D* and perfusion fraction f in the uterine corpus and cervix were analyzed during the menstrual cycle. Statistics analysis was performed by 1-way analysis of variance (ANOVA).

Results: A total of 21 healthy female participants (mean age: 26.64±4.72 y) were recruited for analysis. Both D and ADC values in each layer of the uterine corpus were lowest during the menstrual phase. However, there was no statistically significant difference in ADC of myometrium between the menstrual and ovulatory phases ( P =0.177). The D values of the 3 layers of the uterine corpus gradually increase from the menstrual phase to the luteal phase, whereas the 3-zone structure of the cervix has the highest D value in the ovulatory phase. During the luteal phase, the D* and f values of the endometrium and cervical mucus layer were lower than those of the other 2 layers ( P <0.05).

Conclusions: IVIM MRI enables noninvasive quantification of uterine perfusion and diffusion dynamics and reveals characteristic variations in the uterine corpus and cervix throughout the menstrual cycle.

Objective: To assess the diagnostic accuracy and clinical applicability of the artificial intelligence (AI) program "Canon Automation Platform" for the automated detection and localization of pulmonary embolisms (PEs) in chest computed tomography pulmonary angiograms (CTPAs).

Methods: A total of 1474 CTPAs suspected of PEs were retrospectively evaluated by 2 senior radiology residents with 5 years of experience. The final diagnosis was verified through radiology reports by 2 thoracic radiologists with 20 and 25 years of experience, along with the patients' clinical records and histories. The images were transferred to the Canon Automation Platform, which integrates with the picture archiving and communication system (PACS), and the diagnostic success of the platform was evaluated. This study examined all anatomic levels of the pulmonary arteries, including the left pulmonary artery, right pulmonary artery, and interlobar, segmental, and subsegmental branches.

Results: The confusion matrix data obtained at all anatomic levels considered in our study were as follows: AUC-ROC score of 0.945 to 0.996, accuracy of 95.4% to 99.7%, sensitivity of 81.4% to 99.1%, specificity of 98.7% to 100%, PPV of 89.1% to 100%, NPV of 95.6% to 99.9%, F1 score of 0.868 to 0.987, and Cohen Kappa of 0.842 to 0.986. Notably, sensitivity in the subsegmental branches was lower (81.4% to 84.7%) compared with more central locations, whereas specificity remained consistent (98.7% to 98.9%).

Conclusions: The results showed that the chest pain package of the Canon Automation Platform accurately provides rapid automatic PE detection in chest CTPAs by leveraging deep learning algorithms to facilitate the clinical workflow. This study demonstrates that AI can provide physicians with robust diagnostic support for acute PE, particularly in hospitals without 24/7 access to radiology specialists.