We proposed a novel method called HARP-I, which enhances the estimation of motion from tagged Magnetic Resonance Imaging (MRI). The harmonic phase of the images is unwrapped and treated as noisy measurements of reference coordinates on a deformed domain, obtaining motion with high accuracy using Radial Basis Functions interpolations. Results were compared against Shortest Path HARP Refinement (SP-HR) and Sine-wave Modeling (SinMod), two harmonic image-based techniques for motion estimation from tagged images. HARP-I showed a favorable similarity with both methods under noise-free conditions, whereas a more robust performance was found in the presence of noise. Cardiac strain was better estimated using HARP-I at almost any motion level, giving strain maps with less artifacts. Additionally, HARP-I showed better temporal consistency as a new method was developed to fix phase jumps between frames. In conclusion, HARP-I showed to be a robust method for the estimation of motion and strain under ideal and non-ideal conditions.

Purpose Three‐dimensional (3D) quantification of circulation using a Finite Elements methodology. Methods We validate our 3D method using an in‐silico arch model, for different mesh resolutions, image resolution and noise levels, and we compared this with a currently used 2D method. Finally, we evaluated the application of our methodology in 4D Flow MRI data of ascending aorta of six healthy volunteers, and six bicuspid aortic valve (BAV) patients, three with right and three with left handed flow, at peak systole. The in‐vivo data was compared using a Mann‐Whitney U‐test between volunteers and patients (right and left handed flow). Results The robustness of our method throughout different image resolutions and noise levels showed subestimation of circulation less than 45 cm2/s in comparison with the 55cm2/s generated by the current 2D method. The circulation (mean ± SD) of the healthy volunteer group was 13.83 ± 28.78 cm2/s, in BAV patients with right‐handed flow 724.37 ± 317.53 cm2/s, and BAV patients with left‐handed flow −480.99 ± 387.29 cm2/s. There were significant differences between healthy volunteers and BAV patients groups (P‐value < .01), and also between BAV patients with a right‐handed or left‐handed helical flow and healthy volunteers (P‐value < .01). Conclusion We propose a novel 3D formulation to estimate the circulation in the thoracic aorta, which can be used to assess the differences between normal and diseased hemodynamic from 4D‐Flow MRI data. This method also can correctly differentiate between the visually seen right‐ and left‐handed helical flow, which suggests that this approach may have high clinical sensitivity, but requires confirmation in longitudinal studies with a large cohort.