High performance MRI simulations of motion on multi-GPU systems

Abstract

BackgroundMRI physics simulators have been developed in the past for optimizing imaging protocols and for training purposes. However, these simulators have only addressed motion within a limited scope. The purpose of this study was the incorporation of realistic motion, such as cardiac motion, respiratory motion and flow, within MRI simulations in a high performance multi-GPU environment.MethodsThree different motion models were introduced in the Magnetic Resonance Imaging SIMULator (MRISIMUL) of this study: cardiac motion, respiratory motion and flow. Simulation of a simple Gradient Echo pulse sequence and a CINE pulse sequence on the corresponding anatomical model was performed. Myocardial tagging was also investigated. In pulse sequence design, software crushers were introduced to accommodate the long execution times in order to avoid spurious echoes formation.The displacement of the anatomical model isochromats was calculated within the Graphics Processing Unit (GPU) kernel for every timestep of the pulse sequence. Experiments that would allow simulation of custom anatomical and motion models were also performed. Last, simulations of motion with MRISIMUL on single-node and multi-node multi-GPU systems were examined.ResultsGradient Echo and CINE images of the three motion models were produced and motion-related artifacts were demonstrated. The temporal evolution of the contractility of the heart was presented through the application of myocardial tagging. Better simulation performance and image quality were presented through the introduction of software crushers without the need to further increase the computational load and GPU resources. Last, MRISIMUL demonstrated an almost linear scalable performance with the increasing number of available GPU cards, in both single-node and multi-node multi-GPU computer systems.ConclusionsMRISIMUL is the first MR physics simulator to have implemented motion with a 3D large computational load on a single computer multi-GPU configuration. The incorporation of realistic motion models, such as cardiac motion, respiratory motion and flow may benefit the design and optimization of existing or new MR pulse sequences, protocols and algorithms, which examine motion related MR applications.

Flow MRI simulation in complex 3D geometries: Application to the cerebral venous network

“…Contrary to biological tissues, which have a virtually continuous distribution of spins, a discrete distribution of isochromats can lead to constructive magnetization summation inside a voxel and to artificial spin refocusing . Some simulators work around this problem by estimating intravoxel dephasing from gradient magnitudes, by calculating additional points, by controlling decay with a specific variable, by calculating intravoxel magnetization gradients or by nulling the transverse magnetization artificially when a spoiling gradient is applied (however, this last solution can avoid some realistic spin refocusing from one TR to the next). In the present framework, increasing the number of particles per voxel is the most natural way to circumvent the problem, although this method substantially increases computation times.…”

“…Both POSSUM and JEMRIS can take into account rigid body motion and diffusion. A proprietary software application, MRISIMUL, is the only platform for now that has been used to simulate laminar Poiseuille flow based on a Lagrangian approach …”

“…A proprietary software application, MRISIMUL, is the only platform for now that has been used to simulate laminar Poiseuille flow based on a Lagrangian approach. 13 None of these platforms have yet proposed results covering realistic 3D vessels and complex CFD flow data. Leveraging an existing platform, we have developed and evaluated a complete framework for flow MRI simulation able to deal with any pulse sequence and complex flow pattern.…”

Virtual clinical trials in medical imaging: a review

“…MRISIMUL was developed in cardiac MRI and includes extensions to account for cardiac, respiratory, and blood flow motion. 171 However, this remains a Bloch solver useful for producing realistic cine MRI but not to incorporate MR diffusion terms. Recent work by Xanthis and Aletras 184 repackaged MRISIMUL as a simulation as a service system on a GPU cloud, thus providing the required scalability for large-scale in silico trials.…”

Review of Journal of Cardiovascular Magnetic Resonance 2015

“…Recent advances reported in the journal have included improved flow mapping, [1–3] real-time imaging, [4] T1 mapping, [5–7] simulations, [8, 9] perfusion, [10–12] coronary imaging, [13] diffusion tensor imaging, [14–16] feature tracking, [17–20] and strain assessment. [21, 22] The full breadth of new developments is described in the papers in the section.…”