Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model

Abstract

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexibl representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplifie treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed ∼200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure.

Rapid T1 quantification from high resolution 3D data with model‐based reconstruction

“…Numerical T 1 and (pseudo) proton density M 0 brain phantoms were used as ground truth for the simulation studies, which are part of the MRiLab toolbox for MATLAB (The Mathworks, Natick, Massachusetts). T 1 and M 0 values were chosen to agree with in vivo brain structures for 3T.…”

“…Numerical T 1 and (pseudo) proton density M 0 brain phantoms were used as ground truth for the simulation studies, which are part of the MRiLab toolbox 46 for MATLAB…”

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

“…Although possible from a memory point of view, 3D reconstructions will take too long for practical purposes with the current reconstruction setup. The main bottleneck in the reconstructions is formed by the partial derivative computations needed to solve Equation (19). Further research is aimed at performing these computations on GPU architectures, 51,52 reducing the computational effort through algorithmic improvements 53 and through the use of surrogate models.…”

“…Note that Algorithm 2 only requires the allocation of two vectors of length , which is feasible on modern computing architectures for both 2D and 3D acquisitions. The computation of can then be parallelized using computing cores by following the procedure outlined in Algorithm 3 (see also Stöcker et al and Liu et al).…”

A virtual scanner for teaching fundamental magnetic resonance in biomedical engineering

“…Several educational MRI software packages are already available, and examples focused on MRI technology are briefly reviewed here. A number of programs [2,6,[14][15][16][17][18][19] allow the user to choose sequences and imaging parameters and to generate corresponding synthetic MR images. Such programs are useful in later stages of MRI education [13], but they do not give new students insight into the basic physics, the system components, or their interconnection.…”