Optimizing encoding strategies for 4D Flow MRI of mean and turbulent flow

Abstract

Abstract For 4D Flow MRI of mean and turbulent flow a compromise between spatiotemporal undersampling and velocity encodings needs to be found. Assuming a fixed scan time budget, the impact of trading off spatiotemporal undersampling versus velocity encodings on quantification of velocity and turbulence for aortic 4D Flow MRI was investigated. For this purpose, patient-specific mean and turbulent aortic flow data were generated using computational fluid dynamics which were embedded into the patient-specific background image data to generate synthetic MRI data with corresponding ground truth flow. Cardiac and respiratory motion were included. Using the synthetic MRI data as input, 4D Flow MRI was subsequently simulated with undersampling along pseudo-spiral Golden angle Cartesian trajectories for various velocity encoding schemes. Data were reconstructed using a locally low rank approach to obtain mean and turbulent flow fields to be compared to ground truth. Results show that, for a 15-min scan, velocity magnitudes can be reconstructed with good accuracy relatively independent of the velocity encoding scheme ( $$SSI{M}_{U}=0.938\pm 0.003)$$ S S I M U = 0.938 ± 0.003 ) , good accuracy ( $$SSI{M}_{U}\ge 0.933$$ S S I M U ≥ 0.933 ) and with peak velocity errors limited to 10%. Turbulence maps on the other hand suffer from both lower reconstruction quality ( $$SSI{M}_{TKE}\ge 0.323$$ S S I M TKE ≥ 0.323 ) and larger sensitivity to undersampling, motion and velocity encoding strengths ( $$SSI{M}_{TKE}=0.570\pm 0.110)$$ S S I M TKE = 0.570 ± 0.110 ) when compared to velocity maps. The best compromise to measure unwrapped velocity maps and turbulent kinetic energy given a fixed 15-min scan budget was found to be a 7-point multi- $${V}_{enc}$$ V enc acquisition with a low $${V}_{enc}$$ V enc tuned for best sensitivity to the range of expected intra-voxel standard deviations and a high $${V}_{enc}$$ V enc larger than the expected peak velocity.

Keywords

• 4D Flow MRI
• Computational fluid dynamics
• Aortic stenosis
• Turbulent flow
• Image synthesis