Several deep‐learning models have been proposed to shorten MRI scan time. Prior deep‐learning models that utilize real‐valued kernels have limited capability to learn rich representations of complex ...MRI data. In this work, we utilize a complex‐valued convolutional network (ℂNet) for fast reconstruction of highly under‐sampled MRI data and evaluate its ability to rapidly reconstruct 3D late gadolinium enhancement (LGE) data. ℂNet preserves the complex nature and optimal combination of real and imaginary components of MRI data throughout the reconstruction process by utilizing complex‐valued convolution, novel radial batch normalization, and complex activation function layers in a U‐Net architecture. A prospectively under‐sampled 3D LGE cardiac MRI dataset of 219 patients (17 003 images) at acceleration rates R = 3 through R = 5 was used to evaluate ℂNet. The dataset was further retrospectively under‐sampled to a maximum of R = 8 to simulate higher acceleration rates. We created three reconstructions of the 3D LGE dataset using (1) ℂNet, (2) a compressed‐sensing‐based low‐dimensional‐structure self‐learning and thresholding algorithm (LOST), and (3) a real‐valued U‐Net (realNet) with the same number of parameters as ℂNet. LOST‐reconstructed data were considered the reference for training and evaluation of all models. The reconstructed images were quantitatively evaluated using mean‐squared error (MSE) and the structural similarity index measure (SSIM), and subjectively evaluated by three independent readers. Quantitatively, ℂNet‐reconstructed images had significantly improved MSE and SSIM values compared with realNet (MSE, 0.077 versus 0.091; SSIM, 0.876 versus 0.733, respectively; p < 0.01). Subjective quality assessment showed that ℂNet‐reconstructed image quality was similar to that of compressed sensing and significantly better than that of realNet. ℂNet reconstruction was also more than 300 times faster than compressed sensing. Retrospective under‐sampled images demonstrate the potential of ℂNet at higher acceleration rates. ℂNet enables fast reconstruction of highly accelerated 3D MRI with superior performance to real‐valued networks, and achieves faster reconstruction than compressed sensing.
A complex‐valued convolutional neural network (ℂNet) utilizes complex convolutional layers, novel radial batch normalization, and complex ReLU in U‐net architecture for fast reconstruction of highly under‐sampled 3D cardiac MR data. A large dataset of 17003 cardiac late gadolinium enhancement MR images was used for training and evaluating ℂNet. ℂNet achieved more than 300‐fold of acceleration in reconstruction time than compressed sensing methods and outperformed the conventional real‐valued networks using quantitative and qualitative measures.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Background
In patients with suspected or known hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) provides diagnostic and prognostic value. However, contraindications and long‐term ...retention of gadolinium have raised concern about repeated gadolinium administration in this population. Alternatively, native T1‐mapping enables identification of focal fibrosis, the substrate of LGE. However HCM‐specific heterogeneous fibrosis distribution leads to subtle T1‐maps changes that are difficult to identify.
Purpose
To apply radiomic texture analysis on native T1‐maps to identify patients with a low likelihood of LGE(+), thereby reducing the number of patients exposed to gadolinium administration.
Study Type
Retrospective interpretation of prospectively acquired data.
Subjects
In all, 188 (54.7 ± 14.4 years, 71% men) with suspected or known HCM.
Field Strength/Sequence
A 1.5T scanner; slice‐interleaved native T1‐mapping (STONE) sequence and 3D LGE after administration of 0.1 mmol/kg of gadobenate dimeglumine.
Assessment
Left ventricular LGE images were location‐matched with native T1‐maps using anatomical landmarks. Using a split‐sample validation approach, patients were randomly divided 3:1 (training/internal validation vs. test cohorts). To balance the data during training, 50% of LGE(−) slices were discarded.
Statistical Tests
Four sets of texture descriptors were applied to the training dataset for capture of spatially dependent and independent pixel statistics. Five texture features were sequentially selected with the best discriminatory capacity between LGE(+) and LGE(−) T1‐maps and tested using a decision tree ensemble (DTE) classifier.
Results
The selected texture features discriminated between LGE(+) and LGE(−) T1‐maps with a c‐statistic of 0.75 (95% confidence interval CI: 0.70–0.80) using 10‐fold cross‐validation during internal validation in the training dataset and 0.74 (95% CI: 0.65–0.83) in the independent test dataset. The DTE classifier provided adequate labeling of all (100%) LGE(+) patients and 37% of LGE(−) patients during testing.
Data Conclusion
Radiomic analysis of native T1‐images can identify ~1/3 of LGE(−) patients for whom gadolinium administration can be safely avoided.
Level of Evidence: 2
Technical Efficacy Stage: 2
J. Magn. Reson. Imaging 2020. J. Magn. Reson. Imaging 2020;52:906–919.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
In patients with heart failure with preserved ejection fraction (HFpEF), diabetes mellitus (DM) and obesity are important comorbidities as well as major risk factors. Their conjoint impact on the ...myocardium provides insight into the HFpEF aetiology. We sought to investigate the association between obesity, DM, and their combined effect on alterations in the myocardial tissue in HFpEF patients. One hundred and sixty-two HFpEF patients (55 ± 12 years, 95 men) and 45 healthy subjects (53 ± 12 years, 27 men) were included. Patients were classified according to comorbidity prevalence (36 obese patients without DM, 53 diabetic patients without obesity, and 73 patients with both). Myocardial remodeling, fibrosis, and longitudinal contractility were quantified with cardiovascular magnetic resonance imaging using cine and myocardial native T
1
images. Patients with DM and obesity had impaired global longitudinal strain (GLS) and increased myocardial native T
1
compared to patients with only one comorbidity (DM + Obesity vs. DM and Obesity; GLS, − 15 ± 2.1 vs − 16.5 ± 2.4 and − 16.7 ± 2.2%; native T
1
, 1162 ± 37 vs 1129 ± 25 and 1069 ± 29 ms; P < 0.0001 for all). A negative synergistic effect of combined obesity and DM prevalence was observed for native T
1
(np
2
= 0.273, p = 0.002) and GLS (np
2
= 0.288, p < 0.0001). Additionally, severity of insulin resistance was associated with GLS (R = 0.590, P < 0.0001), and native T
1
(R = 0.349, P < 0.0001). The conjoint effect of obesity and DM in HFpEF patients is associated with diffuse myocardial fibrosis and deterioration in GLS. The negative synergistic effects observed on the myocardium may be related to severity of insulin resistance.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Celiac disease (CD), a serious autoimmune disorder that occurs in people who are genetically predisposed, is induced by dietary gluten intake and affects primarily the small intestine. Many studies ...have identified an increased risk of cardiovascular problems in patients with CD. Moreover, these patients are susceptible to certain liver diseases, as well as fibrosis.
The aim of this study was to assess the presence of fibrosis using the De Ritis ratio, determining its effect on the electromechanical features of the left atrium and its susceptibility to atrial fibrillation (AF) in patients with CD.
A total of 97 patients diagnosed with CD by antibody test and biopsy were included in this prospective study. Two groups were created from these patients, a fibrosis-prone (FP) group and a non-fibrosis-prone (NFP) group, according to the cut-off value, as defined in previously published reports, for the AST/ALT ratio. Electrocardiographic and echocardiographic examinations were performed as part of the study.
There were no differences in the baseline characteristics and conventional echocardiographic parameters of the defined groups. However, the patients in the FP group, as compared to those in the NFP group, had significantly increased PWD (56.68±6.48 ms vs. 37.49±6.22 ms, P<0.001). Additionally, significantly higher interatrial (60.50±13.05 ms vs. 29.40±11.55 ms, P<0.001), intra-left atrial (44.18±14.12 ms vs. 21.02±11.99 ms, P<0.001), and intra-right atrial (15.61±8.91 ms vs. 8.38±4.50 ms, P<0.001) EMD was found among the patients in the FP group compared to that of the NFP group.
It is believed that the susceptibility to AF cited in previous studies may be related to fibrosis. Our study is the first to examine the possible effects of fibrosis on AF susceptibility in patients with CD, whereby we propose a new biomarker for prediction of AF susceptibility of these patients.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Before we came, contrast-enhanced computerized tomography angiography (CTA) for pulmonary arteries was performed, and bilaterally massive thrombus was seen on main pulmonary arteries (Fig. 1), and ...she was hemodynamically unstable. ...an urgent transthoracic echocardiography (TTE) was performed and showed an mobile thrombus in the right atrium, also revealed a patent foramen ovale (PFO) and systolic pulmonary artery pressure was 80 mm Hg. The risks and benefits of embolectomy were extensively discussed with patient, her family and surgeons. Because the patient's age made her a high-risk surgical candidate, cardiothoracic surgeons decided that she is inoperable for his surgical intervention, thus the decision was made to proceed with thrombolytic therapy, and 25 mg of intravenous tissue plasminogen activator (TPA) was administered within 12 hours as a slow infusion up to a total of 50 mg TPA.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Objective This study was designed to investigate the role of visceral adiposity along with other clinical parameters in predicting poor coronary collateral circulation (CCC) among patients with ...severe obstructive coronary artery disease (CAD). Subjects and methods A total of 135 patients with severe obstructive CAD and good (n = 70) or poor (n = 65) CCC were included. Data on angiographically detected CCC, the quality criteria for CCC (Rentrop scores) and visceral fat index (VFI) obtained via bioelectrical impedance were compared between good and poor CCC groups. Independent predictors of poor CCC, the correlation between VFI and Rentrop score and the role of VFI in the identification of CCC were analyzed. Results A significant negative correlation was noted between VFI and Rentrop scores (r = -0.668, < 0.001). The presence of hypertension (OR 4.244, 95% CI 1.184 to 15.211, p = 0.026) and higher VFI (OR 1.955, 95% CI 1.342 to 2.848, p < 0.001) were shown to be independent predictors of an increased risk for poor CCC. ROC analysis revealed a VFI > 9 (AUC area under the curve (95% CI): 0.898 (0.834-0.943), p < 0.0001) to be a potential predictor of poor CCC with a sensitivity of 95.38% and specificity of 85.71%. Conclusion In conclusion, our findings revealed comorbid hypertension and higher VFI to significantly predict the risk of poor CCC in patients with severe obstructive CAD.
Propose:
The purpose of this study was to compare the performance of deep learning networks trained with complex-valued and magnitude images in suppressing the aliasing artifact for highly ...accelerated real-time cine MRI.
Methods:
Two 3D U-net models (Complex-Valued-Net and Magnitude-Net) were implemented to suppress aliasing artifacts in real-time cine images. ECG-segmented cine images (
n
= 503) generated from both complex k-space data and magnitude-only DICOM were used to synthetize radial real-time cine MRI. Complex-Valued-Net and Magnitude-Net were trained with fully sampled and synthetized radial real-time cine pairs generated from highly undersampled (12-fold) complex
k
-space and DICOM images, respectively. Real-time cine was prospectively acquired in 29 patients with 12-fold accelerated free-breathing tiny golden-angle radial sequence and reconstructed with both Complex-Valued-Net and Magnitude-Net. Cardiac function, left-ventricular (LV) structure, and subjective image quality 1(non-diagnostic)-5(excellent) were calculated from Complex-Valued-Net– and Magnitude-Net–reconstructed real-time cine datasets and compared to those of ECG-segmented cine (reference).
Results:
Free-breathing real-time cine reconstructed by both networks had high correlation (all R
2
> 0.7) and good agreement (all
p
> 0.05) with standard clinical ECG-segmented cine with respect to LV function and structural parameters. Real-time cine reconstructed by Complex-Valued-Net had superior image quality compared to images from Magnitude-Net in terms of myocardial edge sharpness (Complex-Valued-Net = 3.5 ± 0.5; Magnitude-Net = 2.6 ± 0.5), temporal fidelity (Complex-Valued-Net = 3.1 ± 0.4; Magnitude-Net = 2.1 ± 0.4), and artifact suppression (Complex-Valued-Net = 3.1 ± 0.5; Magnitude-Net = 2.0 ± 0.0), which were all inferior to those of ECG-segmented cine (4.1 ± 1.4, 3.9 ± 1.0, and 4.0 ± 1.1).
Conclusion:
Compared to Magnitude-Net, Complex-Valued-Net produced improved subjective image quality for reconstructed real-time cine images and did not show any difference in quantitative measures of LV function and structure.
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