Explore the vast range of titles available.

ObjectivesDiagnostic performance and potential radiation dose reduction of wide-area detector CT sequential acquisition (“wide-volume” acquisition (WV)) in unenhanced chest examination are unknown. This study aims to assess the image quality, the diagnostic performance, and the radiation dose reduction of WV mode compared with the classical helical acquisition for lung parenchyma analysis in an ultra-low-dose (ULD) protocol.MethodsAfter Institutional Review Board Approval and written informed consent, 64 patients (72% men; 67.6 ± 9.7 years old; BMI 26.1 ± 5.3 kg/m2) referred for a clinically indicated unenhanced chest CT were prospectively included. All patients underwent, in addition to a standard helical acquisition (120 kV, automatic tube current modulation), two ULD acquisitions (135 kV, fixed tube current at 10 mA): one in helical mode and one in WV mode. Image noise, subjective image quality (5-level Likert scale), and diagnostic performance for the detection of 9 predetermined parenchymal abnormalities were assessed by two radiologists and compared using the chi-square or Fisher non-parametric tests.ResultsSubjective image quality (4.2 ± 0.7 versus 4.2 ± 0.8, p = 0.56), image noise (41.7 ± 8 versus 40.9 ± 8.7, p = 0.3), and diagnostic performance were equivalent between ULD WV and ULD helical. Radiation dose was significantly lower for the ULD WV acquisition (mean dose-length product 14.1 ± 1.3 mGy cm versus 15.8 ± 1.3, p < 0.0001).ConclusionAn additional 11% dose reduction is achieved with the WV mode in ULD chest CT with fixed tube current, with equivalent image quality and diagnostic performance when compared with the helical acquisition.Key Points• Image quality and diagnostic performance of ultra-low-dose unenhanced chest CT are identical between wide-volume mode and the reference helical acquisition.• Wide-volume mode allows an additional radiation dose reduction of 11% (mean dose-length product 14.1 ± 1.3 mGy cm versus 15.8 ± 1.3, p < 0.0001).

Objectives: We aimed to study classical, publicly available convolutional neural networks (3D-CNNs) using a combination of several cine-MR orientation planes for the estimation of left ventricular ejection fraction (LVEF) without contour tracing. Methods: Cine-MR examinations carried out on 1082 patients from our institution were analysed by comparing the LVEF provided by the CVI42 software (V5.9.3) with the estimation resulting from different 3D-CNN models and various combinations of long- and short-axis orientation planes. Results: The 3D-Resnet18 architecture appeared to be the most favourable, and the results gradually and significantly improved as several long-axis and short-axis planes were combined. Simply pasting multiple orientation views into composite frames increased performance. Optimal results were obtained by pasting two long-axis views and six short-axis views. The best configuration provided an R2 = 0.83, a mean absolute error (MAE) = 4.97, and a root mean square error (RMSE) = 6.29; the area under the ROC curve (AUC) for the classification of LVEF < 40% was 0.99, and for the classification of LVEF > 60%, the AUC was 0.97. Internal validation performed on 149 additional patients after model training provided very similar results (MAE 4.98). External validation carried out on 62 patients from another institution showed an MAE of 6.59. Our results in this area are among the most promising obtained to date using CNNs with cardiac magnetic resonance. Conclusion: (1) The use of traditional 3D-CNNs and a combination of multiple orientation planes is capable of estimating LVEF from cine-MRI data without segmenting ventricular contours, with a reliability similar to that of traditional methods. (2) Performance significantly improves as the number of orientation planes increases, providing a more complete view of the left ventricle.

The aim of this work was to compare the classification of cardiac MR-images of AL versus ATTR amyloidosis by neural networks and by experienced human readers. Cine-MR images and late gadolinium enhancement (LGE) images of 120 patients were studied (70 AL and 50 TTR). A VGG16 convolutional neural network (CNN) was trained with a 5-fold cross validation process, taking care to strictly distribute images of a given patient in either the training group or the test group. The analysis was performed at the patient level by averaging the predictions obtained for each image. The classification accuracy obtained between AL and ATTR amyloidosis was 0.750 for cine-CNN, 0.611 for Gado-CNN and between 0.617 and 0.675 for human readers. The corresponding AUC of the ROC curve was 0.839 for cine-CNN, 0.679 for gado-CNN (p < 0.004 vs. cine) and 0.714 for the best human reader (p < 0.007 vs. cine). Logistic regression with cine-CNN and gado-CNN, as well as analysis focused on the specific orientation plane, did not change the overall results. We conclude that cine-CNN leads to significantly better discrimination between AL and ATTR amyloidosis as compared to gado-CNN or human readers, but with lower performance than reported in studies where visual diagnosis is easy, and is currently suboptimal for clinical practice.

To reduce the iodine load required for CT Transcatheter Aortic Valve Replacement (TAVR) planning on a 320-row scanner by acquiring the two CT TAVR steps (ECG-gated aortic root CTA and non-gated aorto-ilio-femoral CTA) within a single contrast media bolus injection. 50 consecutive patients (82.6±6.9 years; 56% female) were prospectively enrolled and underwent a TAVR planning using a 320-row CT, with ECG-gated aortic root CTA immediately followed by a non-gated aorto-iliac acquisition, all within a single bolus of 40-70mL of Iohexol 350mgI/mL. The Iodine load, image quality, SNR, CNR and radiation dose were compared using a Mann-Whitney test to that of 24 consecutive patients (84.3±4.8 years, 58% female) previously imaged on a 64-row scanner with a conventional two-step protocol. Iodine load was reduced by 44%. All examinations were of diagnostic quality, with improvement of the aortic root CTA image quality (4.9±0.3 versus 4.6±0.5, p<0.01) and a non-significant decrease of the aorto-iliac CTA image quality (4.7±0.6 versus 4.9±0.3, p = 0.07). SNR and CNR were significantly improved in the aortic root CTA (14.0±5.3 and 10.4±4.5 versus 10.3±4.2 and 6.8±3.3, p<0.01 for both) and non-significantly higher in the aorto-iliac CTA (16.5±8.0 and 14.1±7.9 versus 14.7±5.5 and 12.5±5.0, p = 0.42 and p = 0.66). Total radiation dose was reduced by 32%. 320-row CT scanner enables a 44% reduction of iodine load in TAVR planning, while maintaining excellent aorto-ilio-femoral arterial enhancement and lowering radiation dose.

To evaluate the diagnostic performance of Ultra-Low-Dose Chest CT (ULD CT) for the detection of any asbestos-related lesions (primary endpoint) and specific asbestos-related abnormalities, i.e. non-calcified and calcified pleural plaques, diffuse pleural thickening, asbestosis and significant lung nodules (secondary endpoints). 55 male patients (55.7±8.1 years old) with occupational asbestos exposure for at least 15 years and where CT screening was indicated were prospectively included. They all underwent a standard unenhanced chest CT (120kV, automated tube current modulation), considered as the reference, and an ULD CT (135kV, 10mA), both with iterative reconstruction. Two chest radiologists independently and blindly read the examinations, following a detailed protocol. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy and error rate of ULD CT were calculated using the exact method of Pearson with a confidence interval of 95%. Radiation dose was 17.9±1.2mGy.cm (0.25mSv) for the ULD-CT versus 288.8 ±151mGy.cm (4mSv); p <2.2e-16. Prevalence of abnormalities was 20%. The ULD CT's diagnostic performance in joint reading was high for the primary endpoint (sensitivity = 90.9%, specificity = 100%, positive predictive value = 100%, negative predictive value = 97.8%), high for lung nodules, diffuse pleural thickening and calcified pleural plaques (sensitivity, specificity, PPV and NPV = 100%) and fair for asbestosis (sensitivity = 75%, specificity = 100%, PPV = 00%, NPV = 98.1%). Intra-reader accuracy between the ULD CT and the reference CT for the primary endpoint was 98% for the senior and 100% for the junior radiologist. Inter-reader agreement for the primary endpoint was almost perfect (Cohen's Kappa of 0.81). ULD CT in the screening of asbestos exposure related diseases has 90.9% sensitivity and 100% specificity, and could therefore be proposed as a first line examination.

Background: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. Method: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. Results: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (p < 0.0008) and an AUC of 0.630 vs. 0.824 (p < 0.0001). Patient-based comparisons provided an accuracy of 0.660 vs. 0.825 (p < 0.008) and an AUC of 0.727 vs. 0.895 (p < 0.002). Conclusion: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis.

ObjectivesTo investigate the radiation dose exposure, image quality, and diagnostic performance of enhanced 100-kVp abdominopelvic single-energy CT protocol with tin filter (TF).MethodsNinety-three consecutive patients referred for a single-phase enhanced abdominopelvic CT were prospectively included after informed consent. They underwent in addition to a standard protocol (SP) an acquisition with TF. Both examinations were performed on a third-generation dual-source CT system (DSCT), in single energy, using automatic tube current modulation, identical pitch, and identical level of iterative reconstruction. Radiation metrics were compared. Size-specific dose estimates (SSDE), contrast to noise ratio (CNR), and figure of merit (FOM) were calculated. Diagnostic confidence for the assessment of a predetermined list of abdominal lesions was rated by two independent readers.ResultsThe mean dose of the TF protocol was significantly lower (CDTI 1.56 ± 0.43 mGy vs. 8.13 ± 3.32, p < 0.001; SSDE 9.94 ± 3.08 vs. 1.93 ± 0.39, p < 0.001), with an effective dose close to 1 mSv (1.14 mSv ± 0.34; p < 0.001). TF group exhibited non-significant lower liver CNR (2.76 vs. 3.03, p = 0.56) and was more dose efficient (FOM 10.6 vs. 2.49/mSv, p < 0.001) in comparison to SP. The mean diagnostic confidence for visceral, bone, and peritoneal tumors was equivalent between both groups.ConclusionsEnhanced 100-kVp abdominopelvic CT acquired after spectral shaping with tin filtration can achieve similar diagnostic performance and CNR compared to a standard CT protocol, while reducing the radiation dose by 81%.Key Points• 100-kVp spectral filtration enables enhanced abdominal CT with high-dose efficiency.• The radiation dose reaches the 1-mSv range.• Predetermined abdominopelvic lesions can be assessed without impairing on diagnostic confidence.

The automatic classification of various types of cardiomyopathies is desirable but has never been performed using a convolutional neural network (CNN). The purpose of this study was to evaluate currently available CNN models to classify cine magnetic resonance (cine-MR) images of cardiomyopathies. Method: Diastolic and systolic frames of 1200 cine-MR sequences of three categories of subjects (395 normal, 411 hypertrophic cardiomyopathy, and 394 dilated cardiomyopathy) were selected, preprocessed, and labeled. Pretrained, fine-tuned deep learning models (VGG) were used for image classification (sixfold cross-validation and double split testing with hold-out data). The heat activation map algorithm (Grad-CAM) was applied to reveal salient pixel areas leading to the classification. Results: The diastolic–systolic dual-input concatenated VGG model cross-validation accuracy was 0.982 ± 0.009. Summed confusion matrices showed that, for the 1200 inputs, the VGG model led to 22 errors. The classification of a 227-input validation group, carried out by an experienced radiologist and cardiologist, led to a similar number of discrepancies. The image preparation process led to 5% accuracy improvement as compared to nonprepared images. Grad-CAM heat activation maps showed that most misclassifications occurred when extracardiac location caught the attention of the network. Conclusions: CNN networks are very well suited and are 98% accurate for the classification of cardiomyopathies, regardless of the imaging plane, when both diastolic and systolic frames are incorporated. Misclassification is in the same range as inter-observer discrepancies in experienced human readers.

The purpose of this work was to evaluate CMR T1 and T2 mapping sequences in patients with intracardiac thrombi and masses in order to assess T1 and T2 relaxometry usefulness and to allow better etiological diagnosis. This observational study of patients scheduled for routine CMR was performed from September 2014 to August 2015. All patients referred to our department for a 1.5 T CMR were screened to participate. T1 mapping were acquired before and after Gadolinium injection; T2 mapping images were obtained before injection. 41 patients were included. 22 presented with cardiac thrombi and 19 with cardiac masses. The native T1 of thrombi was 1037 ± 152 ms (vs 1032 ± 39 ms for myocardium, p = 0.88; vs 1565 ± 88 ms for blood pool, p < 0.0001). T2 were 74 ± 13 ms (vs 51 ± 3 ms for myocardium, p < 0.0001; vs 170 ± 32 ms for blood pool, p < 0.0001). Recent thrombi had a native T1 shorter than old thrombi (911 ± 177 vs 1169 ± 107 ms, p = 0.01). The masses having a shorter T1 than the myocardium were lipomas (278 ± 29 ms), calcifications (621 ± 218 ms), and melanoma (736 ms). All other masses showed T1 values higher than myocardial T1, with T2 consistently >70 ms. T1 and T2 mapping CMR sequences can be useful and represent a new approach for the evaluation of cardiac thrombi and masses.

To compare cine MR b-TFE sequences acquired before and after gadolinium injection, on a 3T scanner with a parallel RF transmission technique in order to potentially improve scanning time efficiency when evaluating LV function. 25 consecutive patients scheduled for a cardiac MRI were prospectively included and had their b-TFE cine sequences acquired before and right after gadobutrol injection. Images were assessed qualitatively (overall image quality, LV edge sharpness, artifacts and LV wall motion) and quantitatively with measurement of LVEF, LV mass, and telediastolic volume and contrast-to-noise ratio (CNR) between the myocardium and the cardiac chamber. Statistical analysis was conducted using a Bayesian paradigm. No difference was found before or after injection for the LVEF, LV mass and telediastolic volume evaluations. Overall image quality and CNR were significantly lower after injection (estimated coefficient cine after > cine before gadolinium: -1.75 CI = [-3.78;-0.0305], prob(coef>0) = 0% and -0.23 CI = [-0.49;0.04], prob(coef>0) = 4%) respectively), but this decrease did not affect the visual assessment of LV wall motion (cine after > cine before gadolinium: -1.46 CI = [-4.72;1.13], prob(coef>0) = 15%). In 3T cardiac MRI acquired with parallel RF transmission technique, qualitative and quantitative assessment of LV function can reliably be performed with cine sequences acquired after gadolinium injection, despite a significant decrease in the CNR and the overall image quality.