Journal of Thoracic Imaging最新文献

Chest pain is a common chief complaint among patients presenting to the emergency department. However, in the scenario where the clinical presentation is consistent with acute coronary syndrome and no culprit lesions are identified on angiography, clinicians and cardiac imagers should be informed of the differential diagnosis and appropriate imaging modalities used to investigate the potential causes. This review describes an imaging-based algorithm that highlights the diagnostic possibilities, their differentiating imaging features, and the important role of cardiovascular magnetic resonance imaging for narrowing the differential diagnosis.

Purpose: This study aimed to determine the association between functional impairment in small airways and symptoms of dyspnea in patients with Long-coronavirus disease (COVID), using imaging and computational modeling analysis.

Patients and methods: Thirty-four patients with Long-COVID underwent thoracic computed tomography and hyperpolarized Xenon-129 magnetic resonance imaging (HP Xe MRI) scans. Twenty-two answered dyspnea-12 questionnaires. We used a computed tomography-based full-scale airway network (FAN) flow model to simulate pulmonary ventilation. The ventilation distribution projected on a coronal plane and the percentage lobar ventilation modeled in the FAN model were compared with the HP Xe MRI data. To assess the ventilation heterogeneity in small airways, we calculated the fractal dimensions of the impaired ventilation regions in the HP Xe MRI and FAN models.

Results: The ventilation distribution projected on a coronal plane showed an excellent resemblance between HP Xe MRI scans and FAN models (structure similarity index: 0.87 ± 0.04). In both the image and the model, the existence of large clustered ventilation defects was not identifiable regardless of dyspnea severity. The percentage lobar ventilation of the HP Xe MRI and FAN model showed a strong correlation (ρ = 0.63, P < 0.001). The difference in the fractal dimension of impaired ventilation zones between the low and high dyspnea-12 score groups was significant (HP Xe MRI: 1.97 [1.89 to 2.04] and 2.08 [2.06 to 2.14], P = 0.005; FAN: 2.60 [2.59 to 2.64] and 2.64 [2.63 to 2.65], P = 0.056).

Conclusions: This study has identified a potential association of small airway functional impairment with breathlessness in Long-COVID, using fractal analysis of HP Xe MRI scans and FAN models.

Purpose: To investigate intraindividual cardiac structural and functional changes before and after COVID-19 infection in a previously healthy population with a 3T cardiac magnetic resonance (CMR).

Materials and methods: A total of 39 unhospitalized patients with COVID-19 were recruited. They participated in our previous study as non-COVID-19 healthy volunteers undergoing baseline CMR examination and were recruited to perform a repeated CMR examination after confirmed COVID-19 infection in December 2022. The CMR parameters were measured and compared between before and after COVID-19 infection with paired t tests. The laboratory measures including myocardial enzymes and inflammatory indicators were also collected when performing repeated CMR.

Results: The median duration was 393 days from the first to second CMR and 26 days from clinical symptoms onset to the second CMR. Four patients (10.3%, 4/39) had the same late gadolinium enhancement pattern at baseline and repeated CMR and 5 female patients (12.8%, 5/39) had myocardial T2 ratio >2 (2.07 to 2.27) but with normal T2 value in post-COVID-19 CMR. All other CMR parameters were in normal ranges before and after COVID-19 infection. Between before and after the COVID-19 infection, there were no significant differences in cardiac structure, function, and tissue characterization, no matter with or without symptoms (fatigue, chest discomfort, palpitations, shortness of breath, and insomnia/sleep disorders) (all P >0.05). The laboratory measures at repeated CMR were in normal ranges in all participants.

Conclusions: These intraindividual CMR studies showed unhospitalized patients with COVID-19 with normal myocardial enzymes had no measurable CMR abnormalities, which can help alleviate wide social concerns about COVID-19-related myocarditis.

Purpose: The primary imaging modality for the diagnosis of mitral valve prolapse (MVP) is echocardiography supplemented by electrocardiography (ECG)-gated cardiac computed tomography (CT) angiography. However, we have recently encountered patients with MVP who were initially identified on non-ECG-gated enhanced chest CT. The purpose of this study is to evaluate the diagnostic accuracy of non-ECG-gated enhanced chest CT to predict the presence of MVP.

Patients and methods: Of 92 patients (surgically confirmed MVP who underwent non-ECG-gated chest CT), 27 patients were excluded for motion artifact or insufficient surgical correlation, and 65 patients were ultimately included. As a control, 65 patients with dyspnea and without MVP (non-ECG-gated chest CT and echocardiography were performed within 1 month) were randomly selected. We retrospectively analyzed an asymmetric double line sign on axial CT images for the presence of MVP. The asymmetric double line sign was defined as the presence of a linear structure, not located in the plane traversing the mitral annulus.

Results: Use of the asymmetric double line sign to predict MVP on non-ECG-gated CT showed modest sensitivity, high specificity, modest negative predictive value, and high positive predictive value of 59% (38/65), 99% (64/65), 70% (64/91), and 97% (38/39), respectively.

Conclusion: The asymmetric double line sign on non-ECG-gated enhanced chest CT may be a valuable finding to predict the presence of MVP. Familiarity with this CT finding may lead to prompt diagnosis and proper management of MVP.

Purpose: The purpose of this study was to investigate the effect of integrated evaluation of resting static computed tomography perfusion (CTP) and coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR CT ) on therapeutic decision-making and predicting major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease.

Materials and methods: In this post hoc analysis of a prospective trial of CCTA in patients assigned to either CCTA or CCTA plus FFR CT arms, 500 patients in the CCTA plus FFR CT arm were analyzed. Both resting static CTP and FFR CT were evaluated by using the conventional CCTA. Perfusion defects in the myocardial segments with ≥50% degree of stenosis in the supplying vessels were defined as resting static CTP positive, and any vessel with an FFR CT value of ≤0.80 was considered positive. Patients were divided into 3 groups: (1) negative CTP-FFR CT match group (resting static CTP-negative and FFR CT -negative group); (2) mismatch CTP-FFR CT group (resting static CTP-positive and FFR CT -negative or resting static CTP-negative and FFR CT -positive group); and (3) positive CTP-FFR CT match group (resting static CTP-positive and FFR CT -positive group). We compared the revascularization-to-invasive coronary angiography ratio and the MACE rate among 3 subgroups at 1- and 3-year follow-ups. The adjusted Cox hazard proportional model was used to assess the prognostic value of FFR CT and resting static CTP to determine patients at risk of MACE.

Results: Patients in the positive CTP-FFR CT match group were more likely to undergo revascularization at the time of invasive coronary angiography compared with those in the mismatch CTP-FFR CT group (81.4% vs 57.7%, P =0.033) and the negative CTP-FFR CT match group (81.4% vs 33.3%, P= 0.001). At 1- and 3-year follow-ups, patients in the positive CTP-FFR CT match group were more likely to have MACE than those in the mismatch CTP-FFR CT group (10.5% vs 4.2%, P= 0.046; 35.6% vs 9.4%, P <0.001) and the negative CTP-FFR CT match group (10.5% vs 0.9%, P <0.001; 35.6% vs 5.4%, P <0.001). A positive CTP-FFR CT match was strongly related to MACE at 1-year (hazard ratio=8.06, P= 0.003) and 3-year (hazard ratio=6.23, P <0.001) follow-ups.

Conclusion: In patients with suspected coronary artery disease, the combination of FFR CT with resting static CTP could guide therapeutic decisions and have a better prognosis with fewer MACE in a real-world scenario.

Purpose: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA).

Patients and methods: A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers.

Results: A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%.

Conclusions: The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

Purpose: To evaluate the ability of radiomics score (RS)-based machine learning to identify moderate to severe coronary artery calcium (CAC) on chest x-ray radiographs (CXR).

Materials and methods: We included 559 patients who underwent a CAC scan with CXR obtained within 6 months and divided them into training (n = 391) and validation (n = 168) cohorts. We extracted radiomic features from annotated cardiac contours in the CXR images and developed an RS through feature selection with the least absolute shrinkage and selection operator regression in the training cohort. We evaluated the incremental value of the RS in predicting CAC scores when combined with basic clinical factor in the validation cohort. To predict a CAC score ≥100, we built an RS-based machine learning model using random forest; the input variables were age, sex, body mass index, and RS.

Results: The RS was the most prominent factor for the CAC score ≥100 predictions (odds ratio = 2.33; 95% confidence interval: 1.62-3.44; P < 0.001) compared with basic clinical factor. The machine learning model was tested in the validation cohort and showed an area under the receiver operating characteristic curve of 0.808 (95% confidence interval: 0.75-0.87) for a CAC score ≥100 predictions.

Conclusions: The use of an RS-based machine learning model may have the potential as an imaging marker to screen patients with moderate to severe CAC scores before diagnostic imaging tests, and it may improve the pretest probability of detecting coronary artery disease in clinical practice.

Purpose: To investigate right ventricular (RV) volume and mass by cardiac magnetic resonance (CMR) and the added value of tissue tracking strain analysis as markers of RV dysfunction in pediatric patients with end-stage renal disease (ESRD) and preserved RV ejection fraction.

Materials and methods: Twenty-five children with ESRD and preserved RVEF (>50%) and 10 healthy control children were enrolled. Tissue tracking CMR was used to assess Global Longitudinal, circumferential (GCS), and radial short and long axes (GRS SAX and GRS LAX) RV strains in the patients group compared with controls. Correlations between strain parameters and other CMR parameters and clinical biomarkers were assessed. Binary logistic regression was used to test the independence of cofounders and detect their significance.

Results: RV end-diastolic volume and mass (RVMi) were significantly higher in patients (97.2±19.3 mL/m 2 and 26.6±7gr/m 2 ) than control (71±7.8 mL/m 2 and 11.9±2 gr/m 2 , P values 0.000). All RV global strain parameters were significantly impaired in patients compared with control (all P values <0.05). RV Global Longitudinal was significantly correlated to LVEF (r=-0.416, P =0.039), LVEDVi (r=0.481, P =0.015), LVMi (r=0.562, P =0.004), and systolic blood pressure index (r=0.586, P =0.002). RV GRS (LAX) was significantly correlated to LV GCS (r=-0.462, P =0.020) and LV GRS (SAX) (r=0.454, P =0.023). GRS (SAX) and GCS demonstrated the highest diagnostic accuracy (area under curve: 0.82 and 0.81) to detect strain impairment. Univariate binary logistic regression with patients versus control as dependent variables identified LVMi, RV end-diastolic volume, RVMi, weight, body surface area, RV GCS, RV GRS (LAX), RV GRS (SAX), LV GCS, and LV GRS (SAX) as significantly correlated to patients with ESRD. When adjusted to other cofounders in the multivariable model, only RVMi remained as an independent significant cofounder (Odds ratio:0.395, P =0.046).

Conclusion: RV global strain, volume, and mass by CMR are markers of RV dysfunction in ESRD pediatric patients with preserved RVEF.