PHANTOM project: development and validation of the pipeline from MRA acquisition to MRA simulations

Abstract

Abstract. The aim of this project is to validate the Vivabrain pipeline with a physical phantom from real MRI acquisition to MRI simulations through image segmentation and computational fluid dynamics (CFD) simulations. For that purpose, we set up three comparison benchmarks. The first benchmark compares dimensions of the reconstructed geometry from real MRI acquisition to the physical phantom dimensions. The second aims to validate the CFD simulations by comparing the outputs of two simulations, one carried out using Feel++ and the other using FreeFem++. The CFD outputs are also compared to MRI flow measurement data. The goal of the last comparison benchmark is to compare the MRI simulations outputs to the numerical fluid simulations.

Computer Simulation of Magnetic Resonance Imaging of the Flow of Fluidized Particles

“…The NMR properties include the equilibrium magnetization, M⃗ 0 , the longitudinal relaxation time, T 1 (also known as spin–lattice relaxation time), and the transverse relaxation time, T 2 (also known as spin–spin relaxation time). It is assumed that the isochromats possess uniform physical properties, such as relaxation times, equilibrium magnetization, and magnetic susceptibility …”

“…All the MR sequence parameters, including RF pulses and gradients, are accounted for by the magnetic field term B⃗ . The expression for this term at time t and position r⃗ is given by B⃗ ( r⃗ , t ) = ( G⃗ false( t false) · r⃗ + Δ ω false( r⃗ , t false) γ ) e⃗ z + B⃗ 1 ( r⃗ , t ) where G⃗ ( t ) are the gradient fields used to encode position in three spatial directions, Δω is the off-resonance term, and B⃗ 1 accounts for the RF field excitation, which is orthogonal to the main field ( B⃗ 1 ⊥ e⃗ z ). , …”

“…If we adopt a Lagrangian approach to build the sample file, static tissues and flowing particles will be treated similarly and there is no need to have different solvers for each. In this approach, the position of each spin over time is recorded ( r⃗ = r⃗ ( t )) and fed to the Bloch equation solver to change the field value seen by the particle: B⃗ ( r⃗ , t ) = ( G⃗ false( t false) · r⃗ false( t false) + Δ ω false( r⃗ , t false) γ ) e⃗ z + B⃗ 1 ( r⃗ , t ) …”

“…All the MR sequence parameters, including RF pulses and gradients, are accounted for by the magnetic field term B ⃗ . 40 The expression for this term at time t and position r⃗ is given by…”

“…Since JEMRIS default settings address only rigid samples, they allow only one trajectory for all the spins. The ability to import steady-state CFD data into JEMRIS was developed first in 2016 by Ancel et al 40 However, here, we are dealing with time-varying DEM data. A Matlab code was therefore written that extracts the position of each particle in the DEM data and puts them in a certain format, including the activation command for each spin, recording the positions at a certain number of time steps, deactivating it, and then moving to the next spin.…”