Federica Viola

Aims: Hypertensive pregnancies affect up to 10% of pregnancies globally and are linked to greater cardiovascular risk in offspring. Subclinical cardiac changes have been reported across developmental stages. This study aimed to assess whether children born to hypertensive pregnancies exhibit altered left ventricular (LV) morphology and cardiac flow characteristics. Methods and results: In this cross-sectional study, cardiovascular magnetic resonance (CMR) was performed on a 3.0-Tesla Siemens PRISMA scanner in children aged 6–9 years: 44 offspring of hypertensive pregnancies (50% female) and 56 controls from normotensive pregnancies (37.5% female). Free-breathing real-time cine images were acquired to assess structure and function and to create a computational LV shape atlas. Four-dimensional (4D) Flow CMR was acquired to quantify LV flow component volumes and kinetic energy (KE), enabling assessment of intraventricular flow organization and energetics. Children born to hypertensive and normotensive pregnancies were matched in age (7.49 ± 0.73 and 7.41 ± 1.12 years), but those from hypertensive pregnancies had significantly shorter gestations (median 37.3 weeks [IQR 35.6–39.0] vs. 39.0 weeks [37.7–40.8], P = 0.002). Conventional cardiac measures did not differ between groups. Exploratory shape analysis identified subtle group-level LV differences, including relative prominence of the basal anterior wall, and a more convex LV outflow tract (P < 0.01; AUC = 0.654). LV retained inflow end-diastolic KE was lower in exposed offspring (3.35 [3.03–4.80] vs. 5.70 [4.35–6.80] μJ/mL; P < 0.001). Conclusion: Children born to hypertensive pregnancies show subtle group-level differences in LV morphology and diastolic flow energetics. These findings suggest early differences in cardiovascular phenotype, although their long-term clinical significance remains uncertain and requires longitudinal investigation. Registration: URL: https://www.clinicaltrials.gov; Unique identifiers: NCT05698836. Lay Summary: This study investigated whether children born to mothers with high blood pressure during pregnancy show early differences in heart structure and blood flow.Children exposed to high blood pressure during pregnancy had subtle differences in heart shape and blood flow patterns.The differences identified were small and could only be detected using advanced heart imaging analyses. They were seen when comparing groups of children rather than being useful for identifying individual children, and their long-term importance is not yet known.

Background: Valvular heart disease (VHD) commonly leads to the development of turbulent blood flow. Turbulent kinetic energy (TKE), measured with 4D flow MRI, may be a complement to current metrics for early identification of VHD. Purpose: To investigate TKE as a marker of VHD in relation to flow velocity and cardiovascular geometry. Study Type: Retrospective observational cross‐sectional. Population: Twenty controls and 106 subjects with VHDs, including mitral regurgitation, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and aortic stenosis. Field Strength/Sequences: Four‐dimensional flow MRI using a spoiled gradient‐echo phase‐contrast sequence with asymmetric 4‐point motion encoding at 1.5 or 3 T. Assessment: Time‐resolved segmentations of the left and right ventricles (LV, RV), atria (LA, RA), and aorta were performed. Total and maximum TKE, maximum and average velocity, and diameters were evaluated in each. Correlations between TKE, velocity, and diameter were assessed, along with group differences between VHD subjects and controls. Statistical Tests: Student's t ‐test, Wilcoxon rank–sum test, chi‐squared test, Pearson's correlation, two‐way analysis of covariance. A p value < 0.05 was considered significant. Results: Total and maximum TKE correlated significantly with maximum velocity ( r = 0.45–0.76) and averaged velocity ( r = 0.22–0.44) and less strongly with diameters for aorta, LV, LA, and RV ( r = 0.18–0.37). Compared to controls, total and maximum aortic TKE were significantly higher in aortic stenosis (3.8 vs. 1.6 mJ; 291.7 vs. 133.7 J/m ³ ). Maximum LV TKE was significantly elevated in aortic regurgitation (106.6 vs. 91.8 J/m ³ ). Total TKE was significantly elevated in LA for mitral regurgitation (1.1 vs. 0.6 mJ), in RA for tricuspid regurgitation (1.6 vs. 0.7 mJ), and in RV for pulmonary regurgitation (1.7 vs. 1.0 mJ). Data Conclusion: TKE is elevated in mild VHD. When evaluated alongside velocity as a marker for VHD, TKE may be more discriminative. Consequently, it has potential to be a hemodynamic marker of early VHD conveying complementary information to velocity. Evidence Level: 4. Technical Efficacy: Stage 1.

Background: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time‐consuming process, as there is considerable motion and limited contrast between blood and tissue.Purpose: To develop and evaluate a deep learning‐based segmentation method to automatically segment the cardiac chambers and great thoracic vessels from 4D flow MRI.Study Type: Retrospective.Subjects: A total of 205 subjects, including 40 healthy volunteers and 165 patients with a variety of cardiac disorders were included. Data were randomly divided into training (n = 144), validation (n = 20), and testing (n = 41) sets.Field Strength/Sequence: A 3 T/time‐resolved velocity encoded 3D gradient echo sequence (4D flow MRI).Assessment: A 3D neural network based on the U‐net architecture was trained to segment the four cardiac chambers, aorta, and pulmonary artery. The segmentations generated were compared to manually corrected atlas‐based segmentations. End‐diastolic (ED) and end‐systolic (ES) volumes of the four cardiac chambers were calculated for both segmentations.Statistical tests: Dice score, Hausdorff distance, average surface distance, sensitivity, precision, and miss rate were used to measure segmentation accuracy. Bland–Altman analysis was used to evaluate agreement between volumetric parameters.Results: The following evaluation metrics were computed: mean Dice score (0.908 ± 0.023) (mean ± SD), Hausdorff distance (1.253 ± 0.293 mm), average surface distance (0.466 ± 0.136 mm), sensitivity (0.907 ± 0.032), precision (0.913 ± 0.028), and miss rate (0.093 ± 0.032). Bland–Altman analyses showed good agreement between volumetric parameters for all chambers. Limits of agreement as percentage of mean chamber volume (LoA%), left ventricular: 9.3%, 13.5%, left atrial: 12.4%, 16.9%, right ventricular: 9.9%, 15.6%, and right atrial: 18.7%, 14.4%; for ED and ES, respectively.Data conclusion: The addition of this technique to the 4D flow MRI assessment pipeline could expedite and improve the utility of this type of acquisition in the clinical setting.Evidence Level: 4Technical Efficacy: Stage 1

Background: Although contrast agents would be beneficial, they are seldom used in four‐dimensional (4D) flow magnetic resonance imaging (MRI) due to potential side effects and contraindications.Purpose: To develop and evaluate a deep learning architecture to generate high blood–tissue contrast in noncontrast 4D flow MRI by emulating the use of an external contrast agent.Study Type: Retrospective.Subjects: Of 222 data sets, 141 were used for neural network (NN) training (69 with and 72 without contrast agent). Evaluation was performed on the remaining 81 noncontrast data sets.Field Strength/Sequences: Gradient echo or echo‐planar 4D flow MRI at 1.5 T and 3 T.Assessment: A cyclic generative adversarial NN was trained to perform image translation between noncontrast and contrast data. Evaluation was performed quantitatively using contrast‐to‐noise ratio (CNR), signal‐to‐noise ratio (SNR), structural similarity index (SSIM), mean squared error (MSE) of edges, and Dice coefficient of segmentations. Three observers performed a qualitative assessment of blood–tissue contrast, noise, presence of artifacts, and image structure visualization.Statistical Tests: The Wilcoxon rank‐sum test evaluated statistical significance. Kendall's concordance coefficient assessed interobserver agreement.Results: Contrast in the regions of interest (ROIs) in the NN enhanced images increased by 88%, CNR increased by 63%, and SNR improved by 48% (all P < 0.001). The SSIM was 0.82 ± 0.01, and the MSE of edges was 0.09 ± 0.01 (range [0,1]). Segmentations based on the generated images resulted in a Dice similarity increase of 15.25%. The observers managed to differentiate between contrast MR images and our results; however, they preferred the NN enhanced images in 76.7% of cases. This percentage increased to 93.3% for phase‐contrast MR angiograms created from the NN enhanced data. Visual grading scores were blood–tissue contrast = 4.30 ± 0.74, noise = 3.12 ± 0.98, and presence of artifacts = 3.63 ± 0.76. Image structures within and without the ROIs resulted in scores of 3.42 ± 0.59 and 3.07 ± 0.71, respectively (P < 0.001).Data Conclusion: The proposed approach improves blood–tissue contrast in MR images and could be used to improve data quality, visualization, and postprocessing of cardiovascular 4D flow data.Evidence Level: 3Technical Efficacy: Stage 1

Background: There is an increased interest in quantifying and characterizing epicardial fat which has been linked to various cardiovascular diseases such as coronary artery disease and atrial fibrillation. Recently, three-dimensional single-phase Dixon techniques have been used to depict the heart and to quantify the surrounding fat. The purpose of this study was to investigate the merits of a new high-resolution cine 3D Dixon technique for quantification of epicardial adipose tissue and compare it to single-phase 3D Dixon in patients with cardiovascular disease. Methods: Fifteen patients referred for clinical CMR examination of known or suspected heart disease were scanned on a 1.5 T scanner using single-phase Dixon and cine Dixon. Epicardial fat was segmented by three readers and intra- and inter-observer variability was calculated per slice. Cine Dixon segmentation was performed in the same cardiac phase as single-phase Dixon. Subjective image quality assessment of water and fat images were performed by three readers using a 4-point Likert scale (1 = severe; 2 = significant; 3 = mild; 4 = no blurring of cardiac structures). Results: Intra-observer variability was excellent for cine Dixon images (ICC = 0.96), and higher than single-phase Dixon (ICC = 0.92). Inter-observer variability was good for cine Dixon (ICC = 0.76) and moderate for single-phase Dixon (ICC = 0.63). The intra-observer measurement error (mean ± standard deviation) per slice for cine was − 0.02 ± 0.51 ml (− 0.08 ± 0.4%), and for single-phase 0.39 ± 0.72 ml (0.18 ± 0.41%). Inter-observer measurement error for cine was 0.46 ± 0.98 ml (0.11 ± 0.46%) and for single-phase 0.42 ± 1.53 ml (0.17 ± 0.47%). Visual scoring of the water image yielded median of 2 (interquartile range = [Q3-Q1] 2–2) for cine and median of 3 (interquartile range = 3–2) for single-phase (P < 0.05) while no significant difference was found for the fat images, both techniques yielding a median of 3 and interquartile range of 3–2. Conclusion: Cine Dixon can be used to quantify epicardial fat with lower intra- and inter-observer variability compared to standard single-phase Dixon. The time-resolved information provided by the cine acquisition appears to support the delineation of the epicardial adipose tissue depot.

Background: A reduction in scan time of 4D Flow MRI would facilitate clinical application. A recent study indicates that echo‐planar imaging (EPI) 4D Flow MRI allows for a reduction in scan time and better data quality than the recommended k‐space segmented spoiled gradient echo (SGRE) sequence. It was argued that the poor data quality of SGRE was related to the nonrecommended absence of respiratory motion compensation. However, data quality can also be affected by the background offset compensation.Purpose: To compare the data quality of respiratory motion‐compensated SGRE and EPI 4D Flow MRI and their dependence on background correction (BC) order.Study Type: Retrospective.Subjects: Eighteen healthy subjects (eight female, mean age 32 ± 5 years).Field Strength and Sequence: 1.5 T. [Correction added on July 26, 2019, after first online publication: The preceding field strength was corrected.] SGRE and EPI‐based 4D Flow MRI.Assessment: Data quality was investigated visually and by comparing flows through the cardiac valves and aorta. Measurements were obtained from transvalvular flow and pathline analysis.Statistical Tests: Linear regression and Bland–Altman analysis were used. Wilcoxon test was used for comparison of visual scoring. Student's t‐test was used for comparison of flow volumes.Results: No significant difference was found by visual inspection (P = 0.08). Left ventricular (LV) flows were strongly and very strongly associated with SGRE and EPI, respectively (R² = 0.86–0.94 SGRE; 0.71–0.79 EPI, BC0–4). LV and right ventricular (RV) outflows and LV pathline flows were very strongly associated (R² = 0.93–0.95 SGRE; 0.88–0.91 EPI, R² = 0.91–0.95 SGRE; 0.91–0.93 EPI, BC1–4). EPI LV outflow was lower than the short‐axis‐based stroke volume. EPI RV outflow and proximal descending aortic flow were lower than SGREs.Data Conclusion: Both sequences yielded good internal data consistency when an adequate background correction was applied. Second and first BC order were considered sufficient for transvalvular flow analysis in SGRE and EPI, respectively. Higher BC orders were preferred for particle tracing.Level of Evidence 4Technical Efficacy Stage 1J. Magn. Reson. Imaging 2020;51:885–896.