Feasibility of the Threshold-Based Quantification of Myocardial Fibrosis on Cardiac CT as a Prognostic Marker in Nonischemic Dilated Cardiomyopathy.

OBJECTIVE: This study investigated the feasibility and prognostic relevance of threshold-based quantification of myocardial delayed enhancement (MDE) on CT in patients with nonischemic dilated cardiomyopathy (NIDCM). MATERIALS AND METHODS: Forty-three patients with NIDCM (59.3 ± 17.1 years; 21 male) were included in the study and underwent cardiac CT and MRI. MDE was quantified manually and with a threshold-based quantification method using cutoffs of 2, 3, and 4 standard deviations (SDs) on three sets of CT images (100 kVp, 120 kVp, and 70 keV). Interobserver agreement in MDE quantification was assessed using the intraclass correlation coefficient (ICC). Agreement between CT and MRI was evaluated using the Bland-Altman method and the concordance correlation coefficient (CCC). Patients were followed up for the subsequent occurrence of the primary composite outcome, including cardiac death, heart transplantation, heart failure hospitalization, or appropriate use of an implantable cardioverter-defibrillator. The Kaplan-Meier method was used to estimate event-free survival according to MDE levels. RESULTS: Late gadolinium enhancement (LGE) was observed in 29 patients (67%, 29/43), and the mean LGE found with the 5-SD threshold was 4.1% ± 3.6%. The 4-SD threshold on 70-keV CT showed excellent interobserver agreement (ICC = 0.810) and the highest concordance with MRI (CCC = 0.803). This method also yielded the smallest bias with the narrowest range of 95% limits of agreement compared to MRI (bias, -0.119%; 95% limits of agreement, -4.216% to 3.978%). During a median follow-up of 1625 days (interquartile range, 712-1430 days), 10 patients (23%, 10/43) experienced the primary composite outcome. Event-free survival significantly differed between risk subgroups divided by the optimal MDE cutoff of 4.3% (log-rank P = 0.005). CONCLUSION: The 4-SD threshold on 70-keV monochromatic CT yielded results comparable to those of MRI for quantifying MDE as a marker of myocardial fibrosis, which showed prognostic value in patients with NIDCM.

Assessment of Feasibility and Interscan Variability of Short-time Cardiac MRI for Cardiotoxicity Evaluation in Breast Cancer.

Purpose To investigate the feasibility and interscan variability of short-time cardiac MRI protocol after chemotherapy in individuals with breast cancer. Materials and Methods A total of 13 healthy female controls (mean age, 52.4 years ± 13.2 [SD]) and 85 female participants with breast cancer (mean age, 51.8 years ± 9.9) undergoing chemotherapy prospectively underwent routine breast MRI and short-time cardiac MRI using a 3-T scanner with peripheral pulse gating in the prone position. Interscan, intercoil, and interobserver reproducibility and variability of native T1 and extracellular volume (ECV), as well as ventricular functional parameters, were measured using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), or coefficient of variation (CoV). Results Left ventricular functional parameters had excellent interscan reproducibility (ICC ≥ 0.80). Left ventricular ejection fraction showed low interscan variability in control and chemotherapy participants (SEM, 2.0 and 1.2; CoV, 3.1 and 1.9, respectively). Native T1 showed excellent interscan (ICC, 0.75) and intercoil (ICC, 0.81) reproducibility in the control group and good interscan reproducibility (ICC, 0.72 and 0.73, respectively) in the participants undergoing immediate and remote chemotherapy. Interscan reproducibility for ECV was excellent in the control group and in the remote chemotherapy group (ICC, 0.93 and 0.88, respectively) and fair in the immediate chemotherapy group (ICC, 0.52). In the regional analysis, interscan repeatability and variability of native T1 and ECV were superior in the anteroseptum or inferoseptum than in other segments in the immediate chemotherapy group. Native T1 and ECV had good to excellent interobserver agreement across all groups. Conclusion Short-time cardiac MRI showed excellent results for interscan, intercoil, and interobserver reproducibility and variability for ventricular functional or tissue characterization parameters, suggesting that this modality is feasible for routine surveillance of cardiotoxicity evaluation in individuals with breast cancer. Keywords: Cardiac MRI, Heart, Cardiomyopathy ClinicalTrials.gov registration no. NCT03301389 Supplemental material is available for this article. © RSNA, 2024.

Development and validation of cardiac diffusion weighted magnetic resonance imaging for the diagnosis of myocardial injury in small animal models.

Cardiac diffusion weighted-magnetic resonance imaging (DWI) has slowly developed due to its technical difficulties. However, this limitation could be overcome by advanced techniques, including a stimulated echo technique and a gradient moment nulling technique. This study aimed to develop and validate a high-order DWI sequence, using echo-planar imaging (EPI) and second-order motion-compensated (M012) diffusion gradient applied to cardiac imaging in small-sized animals with fast heart and respiratory rates, and to investigate the feasibility of cardiac DWI, diagnosing acute myocardial injury in isoproterenol-induced myocardial injury rat models. The M012 diffusion gradient sequence was designed for diffusion tensor imaging of the rat myocardium and validated in the polyvinylpyrrolidone phantom. Following sequence optimization, 23 rats with isoproterenol-induced acute myocardial injury and five healthy control rats underwent cardiac MRI, including cine imaging, T1 mapping, and DWI. Diffusion gradient was applied using a 9.4-T MRI scanner (Bruker, BioSpec 94/20, gradient amplitude = 440 mT/m, maximum slew rate = 3440 T/m/s) with double gating (electrocardiogram and respiratory gating). Troponin I was used as a serum biomarker for myocardial injury. Histopathologic examination of the heart was subsequently performed. The developed DWI sequence using EPI and M012 provided the interpretable images of rat hearts. The apparent diffusion coefficient (ADC) values were significantly higher in rats with acute myocardial injury than in the control group (1.847 ± 0.326 * 10-3 mm2/s vs. 1.578 ± 0.144 * 10-3 mm2/s, P < 0.001). Troponin I levels were increased in the blood samples of rats with acute myocardial injury (P < 0.001). Histopathologic examinations detected myocardial damage and subendocardial fibrosis in rats with acute myocardial injury. The newly developed DWI technique has the ability to detect myocardial injury in small animal models, representing high ADC values on the myocardium with isoproterenol-induced injury.

Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging.

Background: The reliability and diagnostic performance of deep learning (DL)-based automated T2 measurements on T2 map of 3.0-T cardiac magnetic resonance imaging (MRI) using multi-institutional datasets have not been investigated. We aimed to evaluate the performance of a DL-based software for measuring automated T2 values from 3.0-T cardiac MRI obtained at two centers. Methods: Eighty-three subjects were retrospectively enrolled from two centers (42 healthy subjects and 41 patients with myocarditis) to validate a commercial DL-based software that was trained to segment the left ventricular myocardium and measure T2 values on T2 mapping sequences. Manual reference T2 values by two experienced radiologists and those calculated by the DL-based software were obtained. The segmentation performance of the DL-based software and the non-inferiority of automated T2 values were assessed compared with the manual reference standard per segment level. The software's performance in detecting elevated T2 values was assessed by calculating the sensitivity, specificity, and accuracy per segment. Results: The average Dice similarity coefficient for segmentation of myocardium on T2 maps was 0.844. The automated T2 values were non-inferior to the manual reference T2 values on a per-segment analysis (45.35 vs. 44.32 ms). The DL-based software exhibited good performance (sensitivity: 83.6-92.8%; specificity: 82.5-92.0%; accuracy: 82.7-92.2%) in detecting elevated T2 values. Conclusions: The DL-based software for automated T2 map analysis yields non-inferior measurements at the per-segment level and good performance for detecting myocardial segments with elevated T2 values compared with manual analysis.

Chemotherapy-Related Cardiac Dysfunction: Quantitative Cardiac Magnetic Resonance Image Parameters and Their Prognostic Implications.

OBJECTIVE: To quantitatively analyze the cardiac magnetic resonance imaging (CMR) characteristics of chemotherapy-related cardiac dysfunction (CTRCD) and explore their prognostic value for major adverse cardiovascular events (MACE). MATERIALS AND METHODS: A total of 145 patients (male:female = 76:69, mean age = 63.0 years) with cancer and heart failure who underwent CMR between January 2015 and January 2021 were included. CMR was performed using a 3T scanner (Siemens). Biventricular functions, native T1 T2, extracellular volume fraction (ECV) values, and late gadolinium enhancement (LGE) of the left ventricle (LV) were compared between those with and without CTRCD. These were compared between patients with mild-to-moderate CTRCD and those with severe CTRCD. Cox proportional hazard regression analysis was used to evaluate the association between the CMR parameters and MACE occurrence during follow-up in the CTRCD patients. RESULTS: Among 145 patients, 61 had CTRCD and 84 did not have CTRCD. Native T1, ECV, and T2 were significantly higher in the CTRCD group (1336.9 ms, 32.5%, and 44.7 ms, respectively) than those in the non-CTRCD group (1303.4 ms, 30.5%, and 42.0 ms, respectively; P = 0.013, 0.010, and < 0.001, respectively). They were not significantly different between patients with mild-to-moderate and severe CTRCD. Indexed LV mass was significantly smaller in the CTRCD group (65.0 g/m² vs. 78.9 g/m²; P < 0.001). According to the multivariable Cox regression analysis, T2 (hazard ratio [HR]: 1.14, 95% confidence interval [CI]: 1.01-1.27; P = 0.028) and quantified LGE (HR: 1.07, 95% CI: 1.01-1.13; P = 0.021) were independently associated with MACE in the CTRCD patients. CONCLUSION: Quantitative parameters from CMR have the potential to evaluate myocardial changes in CTRCD. Increased T2 with reduced LV mass was demonstrated in CTRCD patients even before the development of severe cardiac dysfunction. T2 and quantified LGE may be independent prognostic factors for MACE in patients with CTRCD.

Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety.

With the increased availability of magnetic resonance imaging (MRI) and a progressive rise in the frequency of cardiac device implantation, there is an increased chance that patients with implanted cardiac devices require MRI examination during their lifetime. Though MRI is generally contraindicated in patients who have undergone pacemaker implantation with electronic circuits, the recent introduction of MR Conditional pacemaker allows physicians to take advantage of MRI to assess these patients during diagnosis and treatment. When MRI examinations of patients with pacemaker are requested, physicians must confirm whether the device is a conventional pacemaker or an MR Conditional pacemaker by reviewing chest radiographs or the electronic medical records (EMRs). The purpose of this study was to evaluate the utility of a deep convolutional neural network (DCNN) trained to detect pacemakers on chest radiographs and to determine the device's subclassification. The DCNN perfectly detected pacemakers on chest radiographs and the accuracy of the subclassification of pacemakers using the internal and external test datasets were 100.0% (n = 106/106) and 90.1% (n = 279/308). The DCNN can be applied to the radiologic workflow for double-checking purposes, thereby improving patient safety during MRI and preventing busy physicians from making errors.

T1 Map-Based Radiomics for Prediction of Left Ventricular Reverse Remodeling in Patients With Nonischemic Dilated Cardiomyopathy.

OBJECTIVE: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). MATERIALS AND METHODS: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. RESULTS: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). CONCLUSION: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

Comparison of Coronary Computed Tomography Angiography Image Quality With High-concentration and Low-concentration Contrast Agents: The Randomized CONCENTRATE Trial.

PURPOSE: To confirm that the image quality of coronary computed tomography (CT) angiography with a low tube voltage (80 to 100 kVp), iterative reconstruction, and low-concentration contrast agents (iodixanol 270 to 320 mgI/mL) was not inferior to that with conventional high tube voltage (120 kVp) and high-concentration contrast agent (iopamidol 370 mgI/mL). MATERIALS AND METHODS: This prospective, multicenter, noninferiority, randomized trial enrolled a total of 318 patients from 8 clinical sites. All patients were randomly assigned 1: 1: 1 for each contrast medium of 270, 320, and 370 mgI/mL. CT scans were taken with a standard protocol in the high-concentration group (370 mgI/mL) and with 20 kVp lower protocol in the low-concentration group (270 or 320 mgI/mL). Image quality and radiation dose were compared between the groups. Image quality was evaluated with a score of 1 to 4 as subject image quality. RESULTS: The mean HU, signal-to-noise ratio, and contrast-to-noise ratio of the 3 groups were significantly different (all P<0.0001). The signal-to-noise ratio and contrast-to-noise ratio of the low-concentration groups were significantly lower than those of the high-concentration group (P<0.05). However, the image quality scores were not significantly different among the 3 groups (P=0.745). The dose length product and effective dose of the high-concentration group were significantly higher than those of the low-concentration group (P<0.0001 and 0.003, respectively). CONCLUSIONS: The CT protocol with iterative reconstruction and lower tube voltage for low-concentration contrast agents significantly reduced the effective radiation dose (mean: 3.7±2.7 to 4.1±3.1 mSv) while keeping the subjective image quality as good as the standard protocol (mean: 5.7±3.4 mSv).

Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs.

Importance: Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures. Objectives: To develop and validate a deep learning-based synthetic bone-suppressed (DLBS) nodule-detection algorithm for pulmonary nodule detection on chest radiographs. Design, Setting, and Participants: This decision analytical modeling study used data from 3 centers between November 2015 and July 2019 from 1449 patients. The DLBS nodule-detection algorithm was trained using single-center data (institute 1) of 998 chest radiographs. The DLBS algorithm was validated using 2 external data sets (institute 2, 246 patients; and institute 3, 205 patients). Statistical analysis was performed from March to December 2021. Exposures: DLBS nodule-detection algorithm. Main Outcomes and Measures: The nodule-detection performance of DLBS model was compared with the convolution neural network nodule-detection algorithm (original model). Reader performance testing was conducted by 3 thoracic radiologists assisted by the DLBS algorithm or not. Sensitivity and false-positive markings per image (FPPI) were compared. Results: Training data consisted of 998 patients (539 men [54.0%]; mean [SD] age, 54.2 [9.82] years), and 2 external validation data sets consisted of 246 patients (133 men [54.1%]; mean [SD] age, 55.3 [8.7] years) and 205 patients (105 men [51.2%]; mean [SD] age, 51.8 [9.1] years). Using the external validation data set of institute 2, the bone-suppressed model showed higher sensitivity compared with that of the original model for nodule detection (91.5% [109 of 119] vs 79.8% [95 of 119]; P < .001). The overall mean of FPPI with the bone-suppressed model was reduced compared with the original model (0.07 [17 of 246] vs 0.09 [23 of 246]; P < .001). For the observer performance testing with the data of institute 3, the mean sensitivity of 3 radiologists was 77.5% (95% [CI], 69.9%-85.2%), whereas that of radiologists assisted by DLBS modeling was 92.1% (95% CI, 86.3%-97.3%; P < .001). The 3 radiologists had a reduced number of FPPI when assisted by the DLBS model (0.071 [95% CI, 0.041-0.111] vs 0.151 [95% CI, 0.111-0.210]; P < .001). Conclusions and Relevance: This decision analytical modeling study found that the DLBS model was more sensitive to detecting pulmonary nodules on chest radiographs compared with the original model. These findings suggest that the DLBS model could be beneficial to radiologists in the detection of lung nodules in chest radiographs without need of the specialized equipment or increase of radiation dose.

Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm.

OBJECTIVE: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. MATERIALS AND METHODS: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. RESULTS: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). CONCLUSION: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

Aortic Unfolding Measurement Using Non-Contrast Cardiac CT: Normal Range of Low-Risk Subjects.

Purpose: This study aimed to assess the factors influencing aortic unfolding (AU) defined by aortic width on coronary artery calcium (CAC) scan and determine the normal limits for AU. Materials and Methods: In this retrospective study, we measured AU in 924 asymptomatic subjects who underwent CAC scanning during routine health screening from June 2015 to June 2018. Multivariate regression analysis was used to evaluate the factors influencing AU. After the exclusion of subjects with risk factors associated with AU, 283 subjects were included in the analysis of normal values of AU. Mean AU, standard deviation, and upper normal limit were calculated. Results: Sex, age, CAC score, body mass index, body surface area, hypertension, left ventricular hypertrophy, plasma creatinine, and smoking were significantly associated with AU. The mean AU was 102.2 ± 12.8 mm for men and 93.1 ± 10.7 mm for women. AU increased with advancing age (9.6 mm per decade). Conclusion: AU determined from a single measurement on CAC scans was associated with cardiovascular risk factors. The normal limits of AU were defined by age, sex, and body surface area in low-risk subjects in this study.

Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions.

We aimed to determine the effects of deep learning-based reconstruction (DLR) on radiomic features obtained from cardiac computed tomography (CT) by comparing with iterative reconstruction (IR), and filtered back projection (FBP). A total of 284 consecutive patients with 285 cardiac CT scans that were reconstructed with DLR, IR, and FBP, were retrospectively enrolled. Radiomic features were extracted from the left ventricular (LV) myocardium, and from the periprosthetic mass if patients had cardiac valve replacement. Radiomic features of LV myocardium from each reconstruction were compared using a fitting linear mixed model. Radiomics models were developed to diagnose periprosthetic abnormality, and the performance was evaluated using the area under the receiver characteristics curve (AUC). Most radiomic features of LV myocardium (73 of 88) were significantly different in pairwise comparisons between all three reconstruction methods (P < 0.05). The radiomics model on IR exhibited the best diagnostic performance (AUC 0.948, 95% CI 0.880-1), relative to DLR (AUC 0.873, 95% CI 0.735-1) and FBP (AUC 0.875, 95% CI 0.731-1), but these differences did not reach significance (P > 0.05). In conclusion, applying DLR to cardiac CT scans yields radiomic features distinct from those obtained with IR and FBP, implying that feature robustness is not guaranteed when applying DLR.

Myocardial T2* Imaging at 3T and 1.5T: A Pilot Study with Phantom and Normal Myocardium.

BACKGROUND: Myocardial T2* mapping at 1.5T remains the gold standard, but the use of 3T scanners is increasing. We aimed to determine the conversion equations in different scanners with clinically available, vendor-provided T2* mapping sequences using a phantom and evaluated the feasibility of the phantom-based conversion method. METHODS: T2* of a phantom with FeCl3 (five samples, 3.53-20.09 mM) were measured with 1.5T (MR-A1) and 3T scanners (MR-A2, A3, B), and the site-specific equation was determined. T2* was measured in the interventricular septum of three healthy volunteers at 1.5T (T2*1.5T, MR-A1) and 3T (T2*3.0T, MR-B). T2*3.0T was converted based on the equation derived from the phantom (T2*eq). RESULTS: R2* at 1.5T and 3T showed linear association, but a different relationship was observed according to the scanners (MR-A2, R2*1.5T = 0.76 × R2*3.0T - 2.23, R2 = 0.999; MR-A3, R2*1.5T = 0.95 × R2*3.0T - 34.28, R2 = 0.973; MR-B, R2*1.5T = 0.76 × R2*3.0T - 3.02, R2 = 0.999). In the normal myocardium, T2*eq and T2*1.5T showed no significant difference (35.5 ± 3.5 vs. 34.5 ± 1.2, p = 0.340). The mean squared error between T2*eq and T2*1.5T was 16.33, and Bland-Altman plots revealed a small bias (-0.94, 95% limits of agreement: -8.86-6.99). CONCLUSIONS: a phantom-based, site-specific equation can be utilized to estimate T2* values at 1.5T in centers where only 3T scanners are available.

Phantom-based correction for standardization of myocardial native T1 and extracellular volume fraction in healthy subjects at 3-Tesla cardiac magnetic resonance imaging.

OBJECTIVES: To investigate the effect of the phantom-based correction method for standardizing myocardial native T1 and extracellular volume fraction (ECV) in healthy subjects. METHODS: Seventy-one healthy asymptomatic adult (≥ 20 years) volunteers of five different age groups (34 men and 37 women, 45.5 ± 15.5 years) were prospectively enrolled in three academic hospitals. Cardiac MRI including Modified Look - Locker Inversion recovery T1 mapping sequence was performed using a 3-Tesla system with a different type of scanner for each hospital. Native T1 and ECV were measured in the short-axis T1 map and analyzed for mean values of the 16 entire segments. The myocardial T1 value of each subject was corrected based on the site-specific equation derived from the T1 Mapping and ECV Standardization phantom. The global native T1 and ECV were compared between institutions before and after phantom-based correction, and the variation in native T1 and ECV among institutions was assessed using a coefficient of variation (CoV). RESULTS: The global native T1 value significantly differed between the institutions (1198.7 ± 32.1 ms, institution A; 1217.7 ± 39.9 ms, institution B; 1232.7 ± 31.1 ms, institution C; p = 0.002), but the mean ECV did not (26.6-27.5%, p = 0.355). After phantom-based correction, the global native T1 and ECV were 1289.7 ± 32.4 ms and 25.0 ± 2.7%, respectively, and CoV for native T1 between the three institutions decreased from 3.0 to 2.5%. The corrected native T1 value did not significantly differ between institutions (1284.5 ± 31.5 ms, institution A; 1296.5 ± 39.1 ms, institution B; 1291.3 ± 29.3 ms, institution C; p = 0.440), and neither did the ECV (24.4-25.9%, p = 0.078). CONCLUSIONS: The phantom-based correction method can provide standardized reference T1 values in healthy subjects. KEY POINTS: • After phantom-based correction, the global native T1 of 16 entire myocardial segments on 3-T cardiac MRI is 1289.4 ± 32.4 ms, and the extracellular volume fraction was 25.0 ± 2.7% for healthy subjects. • After phantom - based correction was applied, the differences in the global native T1 among institutions became insignificant, and the CoV also decreased from 3.0 to 2.5%.

The image quality and diagnostic accuracy of T1-mapping-based synthetic late gadolinium enhancement imaging: comparison with conventional late gadolinium enhancement imaging in real-life clinical situation.

BACKGROUNDS: Synthetic late gadolinium enhancement (LGE) images are less sensitive to inversion time (TI) and robust to motion artifact, because it is generated retrospectively by post-contrast T1-mapping images. To explore the clinical applicability of synthetic LGE, we investigated the image quality and diagnostic accuracy of synthetic LGE images, in comparison to that of conventional LGE for various disease groups. METHOD AND MATERIALS: From July to November 2019, a total of 98 patients who underwent cardiovascular magnetic resonance imaging (CMR), including LGE and T1-mapping sequences, with suspicion of myocardial abnormality were retrospectively included. Synthetic magnitude inversion-recovery (IR) and phase-sensitive IR (PSIR) images were generated through calculations based on the post-contrast T1-mapping sequence. Three cardiothoracic radiologists independently analyzed the image quality of conventional and synthetic LGE images on an ordinal scale with per-segment basis and the image qualities were compared with chi-square test. The agreement of LGE detection was analyzed on per-patient and per-segment basis with Cohen's kappa test. In addition, the LGE area and percentage were semi-quantitatively analyzed for LGE positive ischemic (n = 14) and hypertrophic cardiomyopathy (n = 13) subgroups by two cardiothoracic radiologists. The difference of quantified LGE area and percentage between conventional and synthetic LGE images were assessed with Mann-Whitney U-test and the inter-reader agreement was assessed with Bland-Altman analysis. RESULTS: The image quality of synthetic images was significantly better than conventional images in both magnitude IR and PSIR through all three observers (P < 0.001, all). The agreements of per-patient and per-segment LGE detection rates were excellent (kappa = 0.815-0.864). The semi-quantitative analysis showed no significant difference in the LGE area and percentage between conventional and synthetic LGE images. In the inter-reader agreement showed only small systematic differences in both magnitude IR and PSIR and synthetic LGE images showed smaller systematic biases compared to conventional LGE images. CONCLUSION: Compared to conventional LGE images, synthetic LGE images have better image quality in real-life clinical situation.

Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.

OBJECTIVES: To evaluate the quality of radiomics studies using cardiac magnetic resonance imaging (CMR) according to the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, and the standards defined by the Image Biomarker Standardization Initiative (IBSI) and identify areas needing improvement. MATERIALS AND METHODS: PubMed and Embase were searched to identify radiomics studies using CMR until March 10, 2021. Of the 259 identified articles, 32 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and IBSI standards by two cardiac radiologists. RESULTS: The mean RQS was 14.3% of the maximum (5.16 out of 36). RQS were low for the demonstration of validation (-60.6%), calibration statistics (1.6%), potential clinical utility (3.1%), and open science (3.1%) items. No study conducted a phantom study or cost-effectiveness analysis. The adherence to TRIPOD guidelines was 55.9%. Studies were deficient in reporting title (3.1%), stating objective in abstract and introduction (6.3% and 9.4%), missing data (0%), discrimination/calibration (3.1%), and how to use the prediction model (3.1%). According to the IBSI standards, non-uniformity correction, image interpolation, grey-level discretization, and signal intensity normalization were performed in two (6.3%), four (12.5%), six (18.8%), and twelve (37.5%) studies, respectively. CONCLUSION: The quality of radiomics studies using CMR is suboptimal. Improvements are needed in the areas of validation, calibration, clinical utility, and open science. Complete reporting of study objectives, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps are necessary. KEY POINTS: • The quality of science in radiomics studies using CMR is currently inadequate. • RQS were low for validation, calibration, clinical utility, and open science; no study conducted a phantom study or cost-effectiveness analysis. • In stating the study objective, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps, improvements are needed.

Evaluation of the Ostium in Anomalous Origin of the Right Coronary Artery with an Interarterial Course Using Dynamic Cardiac CT and Implications of Ostial Findings.

OBJECTIVE: We aimed to evaluate the ostium of right coronary artery of anomalous origin from the left coronary sinus (AORL) with an interarterial course throughout the cardiac cycle on CT and analyze the clinical significance of the ostial findings. MATERIALS AND METHODS: From January 2011 to December 2015, 68 patients (41 male, 57.3 ± 12.1 years) with AORL with an interarterial course and retrospective cardiac CT data were included. AORL was classified as high or low ostial location based on the pulmonary annulus in the diastolic and systolic phases on cardiac CT. In addition, the height, width, height/width ratio, area, and angle of the ostium were measured in both cardiac phases. After cardiac CT, patients were followed until December 31, 2020 for major adverse cardiac events (MACE). Clinical and CT characteristics associated with MACE were explored using Cox regression analysis. RESULTS: During a median follow-up period of 2071 days (interquartile range, 1180.5-2747.3 days), 13 patients experienced MACE (19.1%, 13/68). Seven (10.3%, 7/68) had the ostial location change from high in the diastolic phase to low in the systolic phase. In the univariable analysis, younger age (hazard ratio [HR] = 0.918, p < 0.001), high ostial location (HR = 4.008, p = 0.036), larger height/width ratio (HR = 5.621, p = 0.049), and smaller ostial angle (HR = 0.846, p = 0.048) in the systolic phase were significant predictors of MACE. In multivariable cox regression analysis, younger age (adjusted HR = 0.917, p = 0.002) and high ostial location in the systolic phase (adjusted HR = 4.345, p = 0.026) were independent predictors of MACE. CONCLUSION: The ostial location of AORL with an interarterial course can change during the cardiac cycle, and high ostial location in the systolic phase was an independent predictor of MACE.

Radiomics Feature Analysis Using Native T1 Mapping for Discriminating Between Cardiac Tumors and Thrombi.

RATIONALE AND OBJECTIVES: Accurate differential diagnosis is essential because cardiac tumors and thrombi have different prognoses and therapeutic approaches. Native T1 map provides an objective T1 time quantifications of cardiac mass without the need for a contrast agent. We examined the diagnostic performance of radiomics features for differentiating cardiac tumors from thrombi using cardiac magnetic resonance imaging T1 mapping technique compared to that of late gadolinium enhancement (LGE) imaging. MATERIALS AND METHODS: This retrospective study included 22 cardiac tumors and 21 thrombi of 41 patients who underwent cardiac magnetic resonance imaging from December 2013 to May 2018. Fifty-six radiomics features were extracted from native T1 images. The least absolute shrinkage and selection operator method was used for feature selection and rad score extraction. The diagnostic performance of the rad score was compared to that of the native T1 value (mean T1) and LGE ratio. RESULTS: The area under the receiver operating characteristic curve of the rad score was higher than that of the mean T1 and LGE ratio (0.98 vs. 0.86 vs. 0.82, p = 0.001). With the optimal cut-off value, the rad score showed sensitivity, specificity, and accuracy of 95.4%, 95.2%, and 95.2%, respectively. Combination of the rad score and mean T1 showed a significantly higher diagnostic performance than mean T1 (p = 0.019) or LGE ratio (p = 0.022). CONCLUSION: The rad score derived from native T1 maps can differentiate thrombi from tumors better than the mean T1 or LGE ratio. This is valuable for determining a treatment strategy for cardiac lesions in patients who cannot tolerate contrast agents.

Synthetic Extracellular Volume Fraction Derived Using Virtual Unenhanced Attenuation of Blood on Contrast-Enhanced Cardiac Dual-Energy CT in Nonischemic Cardiomyopathy.

BACKGROUND. Current methods for calculating the myocardial extracellular volume (ECV) fraction require blood sampling to determine the serum hematocrit. Synthetic hematocrit and thus synthetic ECV may be derived using unenhanced attenuation of blood. By use of virtual unenhanced (VUE) attenuation of blood, contrast-enhanced dual-energy CT (DECT) may allow synthetic ECV calculations without unenhanced acquisition. OBJECTIVE. The purpose of this study was to compare synthetic ECV calculated using synthetic hematocrit derived from VUE images and conventional ECV calculated using serum hematocrit, both of which were obtained by contrast-enhanced DECT, with ECV derived from MRI used as the reference standard. METHODS. This retrospective study included 51 patients (26 men and 25 women; mean age, 59.9 ± 15.6 [SD] years) with nonischemic cardiomyopathy who, as part of an earlier prospective investigation, underwent equilibrium phase contrast-enhanced cardiac DECT and cardiac MRI and had serum hematocrit measured within 6 hours of both tests. A separate retrospective sample of 198 patients who underwent contrast-enhanced thoracic DECT performed on the same day for suspected pulmonary embolism and serum hematocrit measurement was identified to derive a synthetic hematocrit formula using VUE attenuation of blood by linear regression analysis. In the primary sample, two radiologists independently used DECT iodine maps to obtain the conventional ECV using serum hematocrit and the synthetic ECV using synthetic hematocrit based on the independently derived formula. The concordance correlation coefficient (CCC) was computed between conventional ECV and synthetic ECV from DECT. Conventional ECV and synthetic ECV from DECT were compared with the ECV derived from MRI in Bland-Altman analyses. RESULTS. In the independent sample, the linear regression formula for synthetic hematocrit was as follows: synthetic hematocrit = 0.85 × (VUE attenuation of blood) - 5.40. In the primary sample, the conventional ECV and synthetic ECV from DECT showed excellent agreement (CCC, 0.95). Bland-Altman analysis showed a small bias of -0.44% (95% limits of agreement, -5.10% to 4.22%) between MRI-derived ECV and conventional ECV from DECT as well as a small bias of -0.78% (95% limits of agreement, -5.25% to 3.69%) between MRI-derived ECV and synthetic ECV from DECT. CONCLUSION. Synthetic ECV and conventional ECV derived from DECT show excel lent agreement and a comparable association with ECV derived from cardiac MRI. CLINICAL IMPACT. Synthetic hematocrit from VUE attenuation of blood may allow myocardial tissue characterization on DECT without the inconvenience of blood sampling.