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 5T. 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. Students 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 (R-2 = 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 (R-2 = 0.93-0.95 SGRE; 0.88-0.91 EPI, R-2 = 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. Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2019.

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 4

Technical Efficacy Stage 1

Background: Diastolic left ventricular (LV) dysfunction is a powerful contributor to the symptoms and prognosis of patients with heart failure. In patients with depressed LV systolic function, the E/A ratio, the ratio between the peak early (E) and the peak late (A) transmitral flow velocity, is the first step to defining the grade of diastolic dysfunction. Doppler echocardiography (echo) is the preferred imaging technique for diastolic function assessment, while cardiovascular magnetic resonance (CMR) is less established as a method. Previous four-dimensional (4D) Flow -based studies have looked at the E/A ratio proximal to the mitral valve, requiring manual interaction. In this study, we compare an automated, deep learning -based and two semi -automated approaches for 4D Flow CMR-based E/A ratio assessment to conventional, gold -standard echo -based methods. Methods: Ninety-seven subjects with chronic ischemic heart disease underwent a cardiac echo followed by CMR investigation. 4D Flow -based E/A ratio values were computed using three different approaches; two semi -automated, assessing the E/A ratio by measuring the inflow velocity (MVvel) and the inflow volume (MVflow) at the mitral valve plane, and one fully automated, creating a full LV segmentation using a deep learning -based method with which the E/A ratio could be assessed without constraint to the mitral plane (LVvel). Results: MVvel, MVflow, and LVvel E/A ratios were strongly associated with echocardiographically derived E/A ratio (R 2 = 0.60, 0.58, 0.72). LVvel peak E and A showed moderate association to Echo peak E and A, while MVvel values were weakly associated. MVvel and MVflow EA ratios were very strongly associated with LVvel (R 2 = 0.84, 0.86). MVvel peak E was moderately associated with LVvel, while peak A showed a strong association (R 2 = 0.26, 0.57). Conclusion: Peak E, peak A, and E/A ratio are integral to the assessment of diastolic dysfunction and may expand the utility of CMR studies in patients with cardiovascular disease. While underestimation of absolute peak E and A velocities was noted, the E/A ratio measured with all three 4D Flow methods was strongly associated with the gold standard Doppler echocardiography. The automatic, deep learning -based method performed best, with the most favorable runtime of similar to 40 seconds. As both semi -automatic methods associated very strongly to LVvel, they could be employed as an alternative for estimation of E/A ratio.