4D flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6-5.8% and 1.1-3.8% in the phantom data and normal volunteer data, respectively.

Aims: Left ventricular (LV) volumes estimated using three-dimensional echocardiography (3D-echo) have been reported to be smaller than those measured using cardiac magnetic resonance (CMR) imaging, but the underlying causes are not well-understood. We investigated differences in regional LV anatomy derived from these modalities and related subsequent findings to image characteristics.Methods and Results: Seventy participants (18 patients and 52 healthy participants) were imaged with 3D-echo and CMR (<1 h apart). Three-dimensional left ventricular models were constructed at end-diastole (ED) and end-systole (ES) from both modalities using previously validated software, enabling the fusion of CMR with 3D-echo by rigid registration. Regional differences were evaluated as mean surface distances for each of the 17 American Heart Association segments, and by comparing contours superimposed on images from each modality. In comparison to CMR-derived models, 3D-echo models underestimated LV end-diastolic volume (EDV) by −16 ± 22, −1 ± 25, and −18 ± 24 ml across three independent analysis methods. Average surface distance errors were largest in the basal-anterolateral segment (11–15 mm) and smallest in the mid-inferoseptal segment (6 mm). Larger errors were associated with signal dropout in anterior regions and the appearance of trabeculae at the lateral wall.Conclusions: Fusion of CMR and 3D-echo provides insight into the causes of volume underestimation by 3D-echo. Systematic signal dropout and differences in appearances of trabeculae lead to discrepancies in the delineation of LV geometry at anterior and lateral regions. A better understanding of error sources across modalities may improve correlation of clinical indices between 3D-echo and CMR.

Background Volumetric and functional right ventricular (RV) indices such as ejection fraction (EF) and global strains are known independent predictors of adverse cardiovascular events. While cardiac magnetic resonance (CMR) imaging remains the reference standard for volume quantification, echocardiography is more accessible and allows for rapid ventricular assessment. Compared to conventional 2D echocardiography, 3D echocardiography (3DE) enables full volume acquisitions and the ability to circumvent geometric assumptions. Given the complexity of RV geometry and sensitivity to image plane positioning, this advantage offers the potential to obtain more accurate diagnostic measurements. Purpose Tools for RV analysis in 3DE have been less extensively studied compared to those for the left ventricle (LV). We sought to quantify discrepancies in RV indices derived from 3DE and CMR. Methods Transthoracic real-time 3DE and cine CMR imaging were performed in 20 prospectively recruited participants (12 patients with acquired cardiac disease and 8 healthy controls), <1 hour apart. Dynamic 3D biventricular models were constructed semi-automatically from CMR by identifying fiducial landmarks, correcting in-plane breath-hold mis-registrations, and interactively fitting contours to the endocardial and epicardial borders on long- and short-axis slices. For 3DE, right ventricular endocardial models were created by fitting contours on 2D image planes resampled from the 3D volume at end-diastole and end-systole, which were subsequently tracked over one cardiac cycle (Figure 1). RV indices including end-diastolic volume (EDV), end-systolic volume (ESV), EF, global longitudinal strain (GLS), and global circumferential strain (GCS) were calculated from the 3DE- and CMR-derived 3D geometric models and compared. Paired-sample t-tests were performed to identify statistically significant differences (where P<0.05), and intraclass correlation coefficients (ICC) for absolute agreement were computed to assess the reliability for each measurement. Results Differences (mean ± SD) in RV indices between 3DE and CMR, with corresponding ICCs are presented in Table 1. Statistically significant differences in RV EDV, ESV, EF, and GLS were observed, with 3DE consistently underestimating volumes and overestimating function when compared to CMR. Although a statistically significant difference in RV GCS was not observed, a low ICC score indicated poor reliability. Conclusions Volume underestimation in RV indices between 3DE and CMR were found to be larger than those previously reported for the LV, which is likely due to the increased geometric complexity and surface area to volume ratio for the RV. Moreover, 3DE tends to overestimate RV function in terms of EF and GLS, which may impact treatment pathways if used in a clinical setting. Recognising systematic differences between modalities reinforces the need to further develop 3DE technologies for more accurate RV quantification. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand;National Heart Foundation (NHF) of New Zealand

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