This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Introduction: Lipids create errors in temperature estimation with the proton resonant frequency (PRF) shift method. Normal liver tissue can contain more than 6% lipid while diseased liver may consist of lipid signal on the order of 20% or more. Fat suppression is commonly used, but increases imaging time and is difficult at low field strengths. In this study, we investigate a simple method to substantially reduce these errors by refocusing several gradient echoes and combining the corresponding temperature maps. This echo combination technique eliminates the need for fat suppression in tissues such as the liver. Methods and Discussion: Simulation of temperature errors were made as a function of echo time (TE) and fat percentage, and an echo combination scheme was developed. To verify the simulation results, experiments were performed in pure water, pure fat and a homogeneous mixture of approximately 80% water and 20% fat that were heated to 90 C and imaged during cooling to room temperature. For the echo combination, data was acquired at three echo times (TE1/TE2/TE3= 14.3, 21.4, 28.6 ms) corresponding to 2?, 3?, and 4? phase angles between fat and water. All echoes were acquired in a single refocused gradient echo acquisition. Additional imaging parameters were TR = 60 ms, flip angle = 60, FOV = 16 cm, BW = 15.6 kHz, slice thickness = 8 mm, matrix size 128 128. For comparison, temperature maps of a GRE sequence were acquired with the same imaging parameters but with a single echo at TE = 25 ms and BW = 6.9 kHz. Phase drift was measured in a reference phantom at room temperature and corrected in all temperature maps. In addition, temperature in the test tubes was measured with a fiber optic temperature sensor. SNR, temperature uncertainty, and temperature errors were compared for the different acquisitions.
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