All-in-one, physics-informed dealiasing method to regularize cardiac 4D flow MRI data.

Cardiac 4D flow MRI provides insight into cardiovascular pathophysiology. Of note, it can identify slow flow regions associated with thrombosis and cardioembolic stroke. However, clinically recommended velocity encodings (VENC) to prevent aliasing in the transvalvular jets yield a poor signal-to-noise ratio (SNR) in slow flow regions. Multi-VENC acquisitions are unfeasible given the current long acquisition times of 4D flow MRI. Low-VENC acquisitions require dealiasing, but existing tools cannot be easily integrated with other regularization steps (e.g., de-noising or velocity divergence removal). We present a single-step algorithm that simultaneously corrects aliasing in low-VENC acquisitions, removes image noise, and imposes physical constraints such as div(v)=0. We formulate a least-squares problem including a dealiasing penalty derived by generalizing existing methods from the meteorology field and additional penalties from physics-informed priors. Algorithm performance is tested on synthetic flows with varying SNR and VENC, and L1 vs. L2 regularizations are compared. The algorithm is tested on cardiac 4D flow MRIs of N=5 human subjects.