Journal of Computer Assisted Tomography最新文献

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; I²=95.39%). In contrast, there were no significant differences in implantation success (OR: 1.04; 95% CI: 0.98-1.11; P=0.18; I²=45.61%), contrast volume (WMD: -0.07; 95% CI: -0.28 to 0.14; P=0.51; I²=77.38%), peri-device leak (OR: 0.56; 95% CI: 0.29-1.11; P=0.10; I²=87.33%), device-related thrombus (OR: 0.70; 95% CI: 0.36-1.35; P=0.29; I²=0%), pericardial effusion requiring intervention (OR: 1.09; 95% CI: 0.95-1.25; P=0.21; I²=0%), major adverse events (OR: 0.99; 95% CI: 0.89-1.09; P=0.78; I²=0%), and all-cause mortality (OR: 0.79; 95% CI: 0.54-1.16; P= 0.23; I²=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.

Claustrophobia during MRI is a well-documented challenge that affects diagnostic accuracy and patient care. Each year, nearly 2 million MRI scans are disrupted due to anxiety, thus leading to early termination of the scan, image degradation from motion, and increasing healthcare costs. This review examines the prevalence of MRI-related claustrophobia, along with the financial and operational burdens. This review also highlights the latest strategies to improve patient tolerance, which range from technological advancements, behavioral techniques and pharmacological interventions, all of which show promise in reducing scan-related distress. Ultimately, a holistic patient-centered approach is key to optimizing both imaging efficiency and the overall MRI experience.

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.

Objective: To assess whether few-shot prompting improves the performance of 2 popular large language models (LLMs) (ChatGPT o1 and DeepSeek-R1) in assigning Coronary Artery Disease Reporting and Data System (CAD-RADS™ 2.0) categories.

Methods: A detailed few-shot prompt based on CAD-RADS™ 2.0 framework was developed using 20 reports from the MIMIC-IV database. Subsequently, 100 modified reports from the same database were categorized using zero-shot and few-shot prompts through the models' user interface. Model accuracy was evaluated by comparing assignments to a reference radiologist's classifications, including stenosis categories and modifiers. To assess reproducibility, 50 reports were reclassified using the same few-shot prompt. McNemar tests and Cohen kappa were used for statistical analysis.

Results: Using zero-shot prompting, accuracy was low for both models (ChatGPT: 14%, DeepSeek: 8%), with correct assignments occurring almost exclusively in CAD-RADS 0 cases. Hallucinations occurred frequently (ChatGPT: 19%, DeepSeek: 54%). Few-shot prompting significantly improved accuracy to 98% for ChatGPT and 93% for DeepSeek (both P<0.001) and eliminated hallucinations. Kappa values for agreement between model-generated and radiologist-assigned classifications were 0.979 (0.950, 1.000) (P<0.001) for ChatGPT and 0.916 (0.859, 0.973) (P<0.001) for DeepSeek, indicating almost perfect agreement for both models without a significant difference between the models (P=0.180). Reproducibility analysis yielded kappa values of 0.957 (0.900, 1.000) (P<0.001) for ChatGPT and 0.873 [0.779, 0.967] (P<0.001) for DeepSeek, indicating almost perfect and strong agreement between repeated assignments, respectively, with no significant difference between the models (P=0.125).

Conclusion: Few-shot prompting substantially enhances LLMs' accuracy in assigning CAD-RADS™ 2.0 categories, suggesting potential for clinical application and facilitating system adoption.

Objective: The second-generation motion correction algorithm, Snapshot Freeze 2.0 (SSF2), is designed to suppress coronary artery motion in cardiac CT angiography. This study aimed to evaluate whether SSF2 improves unenhanced CT images and to compare the coronary artery calcium score (CACS) values reconstructed with and without SSF2.

Methods: One hundred nineteen patients with coronary artery calcium (CACS >0) were enrolled in this study. Unenhanced CT for CACS was performed with a phase window limited to 75% of the R-R interval, using 120 kVp and automatic tube current modulation. CACS values were measured on images with and without SSF2, and absolute differences were calculated. Two radiologists assessed the overall image quality, focusing on coronary artery motion, using a 4-point scale (1=uninterpretable, 4=no motion artifacts).

Results: The absolute differences in CACS for patients with heart rates of 60-95 bpm (n=85) were larger than those with heart rates of up to 59 bpm (n=21) or above 95 bpm (n=13) (median: 10.6, range: 0.1 to 171.2; median: 9.3, range: 0.8 to 31.8; median: 6.0, range: 1.6 to 43.4, respectively). In patients with heart rates of 60 to 95 bpm, SSF2 improved image quality scores (P<0.001); however, for heart rates of up to 59 bpm or above 95 bpm, the improvements were not significant (P=0.18 and 0.10, respectively).

Conclusions: SSF2 reduces motion artifacts in the coronary arteries on unenhanced CT and significantly alters the CACS values. A more accurate calcification assessment is anticipated with SSF2, especially in patients with heart rates of 60 to 95 bpm.

Objective: Pulmonary air trapping is critical for diagnosing and prognostication of various lung diseases. Expiratory CT imaging serves as an accessible method to assess air trapping, which correlates with small airway disease outcomes. Air trapping manifests as mosaic attenuation on inspiratory chest CT that is difficult for visual estimation. The primary aim of this study was to develop an automated tool to quantify mosaic attenuation on inspiratory CT and air trapping on paired expiratory CT. Secondary aims included comparing CT-derived parameters with PFT measurements and dyspnea scores.

Methods: This retrospective analysis of noncontrast chest CTs from 2 academic hospitals was conducted between January 1, 2018, and December 31, 2019. Patients with paired inspiratory and expiratory CT chest scans and PFTs performed on the same day were included. A chest radiologist manually annotated lung parenchyma in a reference cohort. Several histogram-based metrics were computed from lung parenchymal CT values, with the maximum peak position showing the strongest correlation with manually determined thresholds. This threshold, derived from the histogram peak, was applied in the adaptive thresholding process to quantify mosaic attenuation and air trapping.

Results: We analyzed 267 patients (65.5% female, median age 68). Most exhibited normal physiological patterns (44.0%). Patients with elevated residual volume (RV) by PFTs (28.1%) had significantly higher inspiratory CT mosaic attenuation (1629.6 vs. 1311.5 mL, P<0.01) and expiratory CT air trapping volumes (1413.7 vs. 886.2 mL, P<0.01). Correlation analyses demonstrated strong relationships between CT-derived mosaic attenuation and air trapping measures and RV. The correlation with PFT parameters was even stronger in subgroup analyses in patients with obstructive PFT patterns. These models had good predictive ability for an abnormal RV (AUC of 0.92, sensitivity of 72.4%, and specificity of 92.0%) and clinical utility based on good correlation with the mMRC dyspnea score (r=0.71; 95% CI: 0.65-0.77).

Conclusions: This automated adaptive thresholding on inspiratory and expiratory chest CT scans showed a high correlation of lung volume and air trapping parameters with PFTs, revealing that measures of lung function have a complex interplay with air trapping.

Objective: Whole-brain computed tomography perfusion (CTP) imaging is a dose-intensive imaging technique. We aimed to investigate optimal scanning protocol of the whole-brain CTP using a 320-detector row CT in reducing radiation dose for acute ischemic stroke (AIS) patients.

Methods: This study included 54 consecutive AIS patients who underwent whole-brain CTP on a 320-detector row CT scanner. We evaluated the penumbra and ischemic core volumes of CTPfull, CTP3/4, and CTP1/2, created using full, 3/4 and 1/2 scanning data, respectively. Wilcoxon signed-rank test, Spearman correlation coefficient, and Bland-Altman analysis were used for the statistical analysis. In addition, hypothetical treatment decisions based on the DEFUSE-3 criteria were also evaluated to determine whether there were any differences in the treatment decisions when using reduced sampling data (CTP3/4 and CTP1/2) compared with full data to assess its clinical efficacy.

Results: The penumbra and ischemic core median volumes on CTPfull, CTP3/4, and CTP1/2 were 111.5 mL [interquartile range (IQR): 52.0-173.0] and 5.5 mL (IQR: 0 to 24.0), 106.5 mL (IQR: 47.0 to 170.0) and 6.5 mL (IQR: 0 to 24.0), 106.5 mL (IQR: 48.0 to 178.0), and 5.5 mL (IQR: 0 to 23.0), respectively. There were no significant differences in penumbra (P>0.05) and ischemic core (P>0.05) volumes between CTPfull, CTP3/4, and CTP1/2. Spearman correlation analysis showed significant correlations between CTPfull and CTP3/4 and CTP1/2 for both penumbra (r=0.989 to 0.998, P<0.001) and ischemic core (r=0.997 to 0.982, P<0.001) volumes. The hypothetical treatment strategies determined using reduced sampling data (CTP3/4, and CTP1/2) were largely consistent compared with those using CTPfull.

Conclusions: The use of half-scanning data for the whole-brain CTP image with a 320-detector row CT may help to lower the radiation exposure to AIS patients without significant loss of perfusion information.

It is estimated that Generation Z will outnumber the next closest generation, the Millennials (born between 1981 and 1996), by 2040.Only a small number of them are currently in residency training; however, they have already entered the workforce in other professions. Many companies have studied Generation Z and have recognized major differences compared with older generations. Medical professionals can learn from the work already done to adjust for a smooth transition to medical training and postgraduate practice.

Purpose: This study aimed to evaluate the clinical value of the fat attenuation index (FAI) of pericoronary adipose tissue (PCAT) and fractional flow reserve derived from coronary computed tomography angiography (CT-FFR) in predicting coronary revascularization.

Methods: Patients with known or suspected acute coronary syndrome (ACS) who underwent coronary computed tomography angiography (CCTA) and subsequent invasive coronary angiography (ICA) were screened. FAI, lesion-specific CT-FFR, and distal-tip CT-FFR were analyzed by core laboratories blinded to patient management. Per-vessel and per-patient logistic univariable and multivariable analyses were performed to predict revascularization. Three multivariable logistic regression models were compared, with ROC curves generated for each model and AUCs compared. Incremental predictive value between models 2 and 3 was also measured using continuous net reclassification improvement (NRI).

Results: A total of 94 patients who received CCTA followed by ICA were identified and analyzed; 282 vessels were included. Overall, 54 (57.4%) patients with 72 (25.5%) vessels underwent revascularization. Lesion-specific CT-FFR, FAI, and significant stenosis were significantly associated with revascularization in both univariable and multivariable analyses. Lesion-specific CT-FFR, FAI, and significant stenosis were independent predictors of coronary revascularization. In the per-vessel analysis, those with 2 or 3 risk factors had a markedly higher revascularization rate [50 of 69 (72.5%) vs. 22 of 213 (10.3%); P < 0.001]. In the per-patient analysis, those with 2 or 3 risk factors had a markedly higher revascularization rate [35 of 42 (83.3%) vs. 19 of 52 (36.5%); P < 0.001]. The continuous net reclassification improvement (NRI) for the addition of FAI and CT-FFR to standard CCTA analysis (model 3 over model 2) was 0.273 (95% CI, 0.166-0.379, P < 0.0001).

Conclusions: This study demonstrated the application value of CT-FFR and FAI in predicting coronary revascularization in patients with documented ACS. CT-FFR and FAI obtained from quantitative CCTA improved the prediction of future revascularization. These parameters can potentially identify patients likely to receive revascularization upon referral for cardiac catheterization. However, the clinical use of FAI may be limited by the lack of standardization in PCAT values and the absence of a clear established cutoff for clinical relevance.

Purpose: Magnetic resonance cholangiopancreatography (MRCP) may assist in the workup of infantile cholestasis as nonvisualization of the biliary tree is seen with biliary atresia (BA). However, this finding can also be seen with other causes of infantile cholestasis. The purpose of this study is to differentiate BA from other causes of infantile cholestasis using a classification tool integrating MRCP-based radiomics and clinical signatures in patients with nonvisualization of the extrahepatic biliary tree on MRCP.

Methods: Data from infants with cholestasis due to BA, cytomegalovirus infection, or idiopathic neonatal hepatitis (INH) from 2 sites was collected. Radiomics features from MRCP images were selected using Spearman and LASSO methods, followed by applying the optimal machine learning model to develop a radiomics signature. Clinical factors showing significant differences between BA and non-BA groups in training cohort were used to develop a clinical signature using the model. A nomogram model incorporating the signatures was developed. The nomogram model and signatures' performance were assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score. The DeLong test, decision curve analysis (DCA), calibration curves, and the Hosmer-Lemeshow test were utilized to evaluate the nomogram model.

Results: The training cohort consisted of 112 cases (62 BA and 50 non-BA) from site 1, while the external validation cohort included 35 cases (20 BA and 15 non-BA) from site 2. After screening, 2 clinical factors and 8 radiomics features were included. The signatures were fitted using the K-Nearest Neighbors model. The nomogram model showed an AUC of 0.981 in the training cohort and 0.913 in the external validation cohort, significantly outperforming both the signatures in the training cohort and the clinical signature in the external validation cohort, as confirmed by the DeLong test. The DCA indicated the clinical utility of the model. The Calibration curves and the Hosmer-Lemeshow test confirmed the model's adequate fit.

Conclusion: The nomogram model may hold clinical utility. In our cohorts, it was effective for identifying BA among cases with infantile cholestasis attributed to BA, cytomegalovirus infection, or INH in scenarios where the extrahepatic biliary system is not visualized on MRCP.