To develop and validate an acquisition and processing technique that enables fully self-gated 4D flow imaging with whole-heart coverage in a fixed 5-minute scan. Theory and Methods: The data are acquired continuously using Cartesian sampling and sorted into respiratory and cardiac bins using the self-gating signal. The reconstruction is performed using a recently proposed Bayesian method called ReVEAL4D. ReVEAL4D is validated using data from 8 healthy volunteers and 2 patients and compared with compressed sensing technique, L1-SENSE. Results: Healthy subjects-Compared with 2D phase-contrast MRI (2D-PC), flow quantification from ReVEAL4D shows no significant bias. In contrast, the peak velocity and peak flow rate for L1-SENSE are significantly underestimated. Compared with traditional parallel MRI-based 4D flow imaging, ReVEAL4D demonstrates small but significant biases in net flow and peak flow rate, with no significant bias in peak velocity. All 3 indices are significantly and more markedly underestimated by L1-SENSE. Patients-Flow quantification from ReVEAL4D agrees well with the 2D-PC reference. In contrast, L1-SENSE markedly underestimated peak velocity. Conclusions: The combination of highly accelerated 5-minute Cartesian acquisition, self-gating, and ReVEAL4D enables whole-heart 4D flow imaging with accurate flow quantification.
Purpose Background phase offsets in phase‐contrast MRI are often corrected using polynomial regression; however, correction performance degrades when temporally invariant outliers such as steady flow or spatial wrap‐around artifact are present. We describe and validate an iterative method called automatic rejection of temporally invariant outliers (ARTO), which excludes these outliers from the fitting process. Methods The ARTO method iteratively removes pixels with large polynomial regression errors analyzed by a Gaussian mixture model fitting of the residual distribution. A total of 150 trials of a simulated phantom (75 with wrap‐around artifact) and 125 phase‐contrast MRI cines from 22 healthy subjects (48 with wrap‐around artifact) were used for validation. Background phase offsets were corrected using second‐order weighted regularized least squares (WRLS) with and without ARTO. Flow volumes after WRLS and WRLS+ARTO corrections were compared with the known truth (phantom) and stationary phantom reference (in vivo) using Bland‐Altman analysis. The ratio between the pulmonary flow and the systemic flow was also computed in a subset of 6 subjects. Results In the simulated phantom, compared with WRLS and no correction, correction with WRLS+ARTO produced superior agreement in volumetric flow quantification with the known truth. In vivo, WRLS+ARTO also produced superior agreement with stationary phantom‐corrected volumetric flow compared with WRLS and no correction. In data sets with wrap‐around artifact, WRLS produced significantly larger variance in the pulmonary flow and systemic flow ratio than stationary phantom correction (P = .0008). Conclusion The proposed method provides automatic exclusion of temporally invariant outliers and produces flow quantification results comparable to stationary phantom correction.
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