Purpose In this work, we integrated the pilot tone (PT) navigation system into a reconstruction framework for respiratory and cardiac motion‐resolved 5D flow. We tested the hypotheses that PT would provide equivalent respiratory curves, cardiac triggers, and corresponding flow measurements to a previously established self‐gating (SG) technique while being independent from changes to the acquisition parameters. Methods Fifteen volunteers and 9 patients were scanned with a free‐running 5D flow sequence, with PT integrated. Respiratory curves and cardiac triggers from PT and SG were compared across all subjects. Flow measurements from 5D flow reconstructions using both PT and SG were compared to each other and to a reference electrocardiogram‐gated and respiratory triggered 4D flow acquisition. Radial trajectories with variable readouts per interleave were also tested in 1 subject to compare cardiac trigger quality between PT and SG. Results The correlation between PT and SG respiratory curves were 0.95 ± 0.06 for volunteers and 0.95 ± 0.04 for patients. Heartbeat duration measurements in volunteers and patients showed a bias to electrocardiogram measurements of, respectively, 0.16 ± 64.94 ms and 0.01 ± 39.29 ms for PT versus electrocardiogram and of 0.24 ± 63.68 ms and 0.09 ± 32.79 ms for SG versus electrocardiogram. No significant differences were reported for the flow measurements between 5D flow PT and from 5D flow SG. A decrease in the cardiac triggering quality of SG was observed for increasing readouts per interleave, whereas PT quality remained constant. Conclusion PT has been successfully integrated in 5D flow MRI and has shown equivalent results to the previously described 5D flow SG technique, while being completely acquisition‐independent.

The 2019 Global Burden of Disease (GBD) study estimated that there were approximately 24.2 million people affected worldwide by degenerative mitral regurgitation (MR), resulting in 34,200 deaths. After aortic stenosis, MR is the most prevalent VHD in Europe and the second-most common VHD to pose indications for surgery in western countries. Current ESC and AHA/ACC guidelines for the management of VHD emphasize the importance of an integrative approach for the assessment of MR severity, which is of paramount importance in dictating the timing for surgery. Transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) are the first-line imaging modalities; however, despite the technological advancement, sometimes, the final diagnosis on the degree of the disease may still be challenging. In the last 20 years, CMR has emerged as a robust technique in the assessment of patients with cardiac disease, and, recently, its role is gaining more and more importance in the field of VHD. In fact, CMR is the gold standard in the assessment of cardiac volumes, and it is possible to accurately evaluate the regurgitant volume. The purpose of this review is to outline the current state-of-the-art management of MR by using Cardiac Magnetic Resonance (CMR).

Purpose To validate a respiratory motion correction method called focused navigation (fNAV) for free‐running radial whole‐heart 4D flow MRI. Methods Using fNAV, respiratory signals derived from radial readouts are converted into three orthogonal displacements, which are then used to correct respiratory motion in 4D flow datasets. Hundred 4D flow acquisitions were simulated with non‐rigid respiratory motion and used for validation. The difference between generated and fNAV displacement coefficients was calculated. Vessel area and flow measurements from 4D flow reconstructions with (fNAV) and without (uncorrected) motion correction were compared to the motion‐free ground‐truth. In 25 patients, the same measurements were compared between fNAV 4D flow, 2D flow, navigator‐gated Cartesian 4D flow, and uncorrected 4D flow datasets. Results For simulated data, the average difference between generated and fNAV displacement coefficients was 0.04 ±$$ \pm $$ 0.32 mm and 0.31 ±$$ \pm $$ 0.35 mm in the x and y directions, respectively. In the z direction, this difference was region‐dependent (0.02 ±$$ \pm $$ 0.51 mm up to 5.85 ±$$ \pm $$ 3.41 mm). For all measurements (vessel area, net volume, and peak flow), the average difference from ground truth was higher for uncorrected 4D flow datasets (0.32 ±$$ \pm $$ 0.11 cm2, 11.1 ±$$ \pm $$ 3.5 mL, and 22.3 ±$$ \pm $$ 6.0 mL/s) than for fNAV 4D flow datasets (0.10 ±$$ \pm $$ 0.03 cm2, 2.6 ±$$ \pm $$ 0.7 mL, and 5.1 ±0$$ \pm 0 $$.9 mL/s, p < 0.05). In vivo, average vessel area measurements were 4.92 ±$$ \pm $$ 2.95 cm2, 5.06 ±$$ \pm $$ 2.64 cm2, 4.87 ±$$ \pm $$ 2.57 cm2, 4.87 ±$$ \pm $$ 2.69 cm2, for 2D flow and fNAV, navigator‐gated and uncorrected 4D flow datasets, respectively. In the ascending aorta, all 4D flow datasets except for the fNAV reconstruction had significantly different vessel area measurements from 2D flow. Overall, 2D flow datasets demonstrated the strongest correlation to fNAV 4D flow for both net volume (r2 = 0.92) and peak flow (r2 = 0.94), followed by navigator‐gated 4D flow (r2 = 0.83 and r2 = 0.86, respectively), and uncorrected 4D flow (r2 = 0.69 and r2 = 0.86, respectively). Conclusion fNAV corrected respiratory motion in vitro and in vivo, resulting in fNAV 4D flow measurements that are comparable to those derived from 2D flow and navigator‐gated Cartesian 4D flow datasets, with improvements over those from uncorrected 4D flow.

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