1. M. Fathi, A. Bakhshinejad, A. Baghaie, D. Saloner, R. Sacho,
Denoising and Spatial Resolution Enhancement of 4D-Flow MRI Using POD and Lasso Regularization
M. Fathi, A. Bakhshinejad, A. Baghaie, D. Saloner, R. Sacho,
V. Rayz, R. D'souza

2. Introduction 4D-Flow MRI technique The issues of 4D-Flow MRI
Non-invasive imaging of human cardio-vascular system
In-vivo volumetric time-resolved three dimensional blood flow velocity measurements
4D means 3 spatial dimensions + time
The issues of 4D-Flow MRI
Velocity aliasing
Phase offsets
Random acquisition noise
Low spatial and temporal resolution

3. Input Data 4D-Flow MRI Data Vascular geometry In-vivo Noisy
Eddy current effect
Vascular geometry
Contrast-Enhanced MRA
Time-of-Flight MRA

4. Running CFD Using ANSYS Fluent Creating patient-specific geometry
Estimating the boundary conditions
Rigid wall assumption
No-slip boundary condition at walls
About 10 snapshots per 4D-Flow time step

5. POD Basis Vectors Using SVD Method
Find the basis vectors in CFD mesh resolution
Downsample the basis vectors into 4D-Flow resolution

6. LASSO Regularization Using OWL-QN algorithm
Finding the sparsest solution in the POD basis
β controls the sparseness of c

7. Reconstruction Reconstructing all snapshots In CFD mesh resolution
In 4D-Flow resolution

8. Summary
This method is capable of reconstructing data to any arbitrary high resolution (depending on the resolution of the CFD mesh) and reconstruct many flow details.
It can potentially improve the ability to accurately derive relevant secondary hemodynamic parameters such as
wall shear stresses, and
pressure gradients
at much higher resolution than 4D-Flow resolution.