Coil performance evaluation based on electrodynamics : tools for hardware design and validation in magnetic resonance imaging

Parallel MRI techniques allow acceleration of MR imaging beyond traditional speed limits. In parallel MRI, radiofrequency (RF) detector coil arrays are used to perform some degree of spatial encoding which complements traditional encoding using magnetic field gradients. As the acceleration factor increases, coil design becomes critical to the overall image quality. The quality of a design is commonly judged on how it compares with other coil configurations. A procedure to evaluate the absolute performance of RF coil arrays is proposed. Electromagnetic calculations to compute the ultimate intrinsic signal-to-noise ratio (SNR) available for any physically realizable coil array are shown, and coil performance maps are generated based on the ratio of experimentally measured SNR to this ultimate intrinsic SNR. Parallel excitation, which involves independent transmission with multiple RF coils distributed around the body, can be used to improve the homogeneity of RF excitations and minimize the RF energy deposited in tissues - both critical issues for MRI at high magnetic field strength. As its use is explored further, it will be important to investigate the intrinsic constraints of the technique. We studied the trade-off between transmit homogeneity and specific absorption rate (SAR) reduction with respect to main magnetic field strength, object size and acceleration. We introduced the concept of ultimate intrinsic SAR, the theoretical smallest RF energy deposition for a target flip angle distribution, and we calculated the corresponding ideal current patterns. Knowledge of these optimal current patterns will serve as an important guide for future high-field coil designs.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Lattanzi, Riccardo

Advisor dc:contributor.advisor

• Daniel K. Sodickson.

• Harvard University--MIT Division of Health Sciences and Technology.

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/1721.1/45909

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/45909