On the optimization of imaging parameters for magnetic resonance imaging thermometry using magnetic microparticles

Magnetic Resonance Imaging thermometry is an extremely useful technique which allows one to determine, noninvasively, the temperature deep in the tissue in two or three dimensions. Many methods of MR thermometry have been developed, including those that rely on the intrinsic MR properties of tissue and those which depend on the addition of contrast agents injected into the tissue to create temperature dependent MR images. One such method is to introduce magnetic particles whose magnetization's temperature dependence influences the MR properties of the surrounding tissue and obtain temperature from calibrated intensity changes of T-2* weighted MR images. One limitation of this method is the temperature resolution which is determined by the rate of change of the magnetization with temperature. One can change the MR response either through varying the particles properties or finding the MR scan parameters which maximize the image contrast due to T-2* weighting of images. In this work we calculate the MR signal strength, using known values of T-1 and T-2* relaxation times for agarose gel phantoms with embedded magnetic particles, and compared this with the temperature dependent intensity of experimental MR images. We seek to optimize the change in signal intensity with temperature by varying the selectable MR scanner parameters: echo time, repetition time, and flip angle. Based on comparison with experimental data we find that the change in signal with temperature can be significantly increased (by as much as 100%) through the appropriate choice of MR scan parameters. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Magnetic Resonance Imaging thermometry is a useful technique for noninvasively determining tissue temperature, with methods including the use of magnetic particles to influence MR properties. By optimizing MR scan parameters, the change in signal intensity with temperature can be significantly increased based on comparison with experimental data.