Fast simulation of E1, B1 and Specific Absorption Rate for 7T MRI with the use of graphical processors

Specific Absorption Rate (SAR) is a dominant constraint in high field MR, and has been a topic of much recent interest with developments of parallel transmission systems (pTx). While real-time estimates of local SAR over large volumes as well as SAR-constrained pTx RF design are highly desirable goals, it is both difficult to control and computationally demanding. Steady advances in graphics cards for game developers have enabled dramatic speedups in computationally heavy tasks for computer graphics, and some of this functionality is applicable for faster numerical SAR simulation compared to general CPUs. In this study, we present the use of Compute Unified Device Architecture (CUDA) enabled graphics cards in Finite Difference Time Domain (FDTD) simulations for SAR computation. We show that using this framework can speed up computation by at least an order of magnitude compared to regular CPU computation. This will allow us to estimate SAR, B1, and E1 fields quickly for instances where SAR estimation for parallel transmission imaging of individual subjects is necessary, or for optimizing coil designs based on these estimates. A fast FDTD computation would also significantly speed up iterative optimizations of coil design over a geometric parameter space. A description is provided of how FDTD with Uniaxial Perfect Matching Layer (UPML) boundary conditions was coded on GPUs using the NVIDIA CUDA framework. FDTD equations were CUDA optimized by use of two kernel functions, one for the E field update equations and another for the B field update equations. FDTD simulations were compared to an analytical validation case of a dielectric sphere under a current loop. In addition, a description is provided of how SAR computation was parallelized for the CUDA framework.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Kini, Lohith Ganesh

Advisor dc:contributor.advisor

• Elfar Adalsteinsson.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/61304

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/61304