Key result
A new mathematical and computational model based on Navier-Stokes equations demonstrated the ability to assess the quality of 4D Flow MRI measurements over space and time.
Why the study?
4D Flow MRI is subject to artifacts such as noise and aliasing that are difficult to detect in clinical practice due to its 3D and dynamic structure.
A novel mathematical model based on Navier-Stokes equations can assess the quality of 4D Flow MRI measurements, potentially improving artifact detection in clinical practice.
Answers are grounded in this paper and cite their sources.
New 4D Flow MRI quality model may aid artifact detection; leaves open clinical validation before practice change.
Abstract 4D Flow Magnetic Resonance Imaging (MRI) is the state‐of‐the‐art technique to comprehensively measure the complex spatio‐temporal and multidirectional patterns of blood flow. However, it is subject to artifacts such as noise and aliasing, which due to the 3D and dynamic structure is difficult to detect in clinical practice. In this work, a new mathematical and computational model to determine the quality of 4D Flow MRI is presented. The model is derived by assuming the true velocity satisfies the incompressible Navier–Stokes equations and that can be decomposed by the measurements plus an extra field . Therefore, a non‐linear problem with as unknown arises, which serves as a measure of data quality. A stabilized finite element formulation tailored to this problem is proposed and analyzed. Then, extensive numerical examples—using synthetic 4D Flow MRI data as well as real measurements on experimental phantom and subjects—illustrate the ability to use for assessing the quality of 4D Flow MRI measurements over space and time.
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Garay et al. (2022) studied this question. Mathematical and computational model based on Navier-Stokes equations was evaluated on Quality of 4D Flow MRI measurements over space and time. A new mathematical and computational model based on Navier-Stokes equations demonstrated the ability to assess the quality of 4D Flow MRI measurements over space and time.
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