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A method to correct background phase offset for phase‐contrast MRI in the presence of steady flow and spatial wrap‐around artifact
Author(s) -
Pruitt Aaron A.,
Jin Ning,
Liu Yingmin,
Simonetti Orlando P.,
Ahmad Rizwan
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27572
Subject(s) - imaging phantom , outlier , artifact (error) , mathematics , flow (mathematics) , computer science , artificial intelligence , algorithm , nuclear medicine , medicine , geometry
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.