Aaron A Pruitt1, Ning Jin2, Yingmin Liu3, Orlando P Simonetti1,3,4, Rizwan Ahmad1,3,5. 1. Biomedical Engineering, Ohio State University, Columbus, Ohio. 2. Cardiovascular MR R&D, Siemens Medical Solutions USA, Columbus, Ohio. 3. Dorothy M. Davis Heart and Lung Research Institute, Ohio State University, Columbus, Ohio. 4. Internal Medicine, Ohio State University, Columbus, Ohio. 5. Electrical and Computer Engineering, Ohio State University, Columbus, Ohio.
Abstract
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.
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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