Takeshi Yokoo1,2, Qing Yuan1, Julien Sénégas3, Andrea J Wiethoff2,4, Ivan Pedrosa1,2. 1. Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA. 2. Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA. 3. Philips Research Laboratories, Hamburg, Germany. 4. Briarcliff Manor, New York, USA.
Abstract
PURPOSE: To compare Rician and non-Rician noise models for quantitative R2 * magnetic resonance imaging (MRI), in a simulation, phantom, and human study. MATERIALS AND METHODS: Synthetic 12-echo spoiled GRE (SGRE) datasets were generated with various R2 * rates (0-2000 sec(-1) ) at a signal-to-noise ratio (SNR) of 50, 20, 10, and 5. Phantoms of different MnCl2 concentrations (0-25 mM) were constructed and imaged using a 12-echo 3D SGRE sequence at 1.5T. Increasing levels of synthetic noise was added to the original data to simulate sequentially lower SNR conditions. Sixteen patients with suspected or known iron overload were imaged using 12-echo 3D SGRE at 1.5T. Various R2 * quantification methods, based on Rician and non-Rician noise models, were compared in the simulation, phantom, and human datasets. RESULTS: Non-Rician R2 * estimates were variably inaccurate in the high R2 * range (>500 sec(-1) ), with SNR-dependent linear goodness-of-fit statistic (R(2) ) of 0.373-0.999. Rician R2 * estimates were accurate even in the high R2 * range, with high R(2) of 0.940-0.999 regardless of SNR. Non-Rician R2 * estimates were variably nonlinear at high MnCl2 concentrations, with SNR-dependent R(2) of 0.345-0.994. Rician R2 * estimates were linear even at high MnCl2 concentrations, with high R(2) of 0.923-0.994 regardless of SNR. Between-method agreement of the R2 * estimates was excellent in patients with low ferritin but poor in patients with high ferritin. Rician R2 * estimates had excellent correlation with ferritin (r = 0.966 P < 0.001). CONCLUSION: Rician R2 * estimates were most consistent in the high R2 * conditions and under varying SNR, and may be more reliable when high iron load is suspected.
PURPOSE: To compare Rician and non-Rician noise models for quantitative R2 * magnetic resonance imaging (MRI), in a simulation, phantom, and human study. MATERIALS AND METHODS: Synthetic 12-echo spoiled GRE (SGRE) datasets were generated with various R2 * rates (0-2000 sec(-1) ) at a signal-to-noise ratio (SNR) of 50, 20, 10, and 5. Phantoms of different MnCl2 concentrations (0-25 mM) were constructed and imaged using a 12-echo 3D SGRE sequence at 1.5T. Increasing levels of synthetic noise was added to the original data to simulate sequentially lower SNR conditions. Sixteen patients with suspected or known iron overload were imaged using 12-echo 3D SGRE at 1.5T. Various R2 * quantification methods, based on Rician and non-Rician noise models, were compared in the simulation, phantom, and human datasets. RESULTS: Non-Rician R2 * estimates were variably inaccurate in the high R2 * range (>500 sec(-1) ), with SNR-dependent linear goodness-of-fit statistic (R(2) ) of 0.373-0.999. Rician R2 * estimates were accurate even in the high R2 * range, with high R(2) of 0.940-0.999 regardless of SNR. Non-Rician R2 * estimates were variably nonlinear at high MnCl2 concentrations, with SNR-dependent R(2) of 0.345-0.994. Rician R2 * estimates were linear even at high MnCl2 concentrations, with high R(2) of 0.923-0.994 regardless of SNR. Between-method agreement of the R2 * estimates was excellent in patients with low ferritin but poor in patients with high ferritin. Rician R2 * estimates had excellent correlation with ferritin (r = 0.966 P < 0.001). CONCLUSION: Rician R2 * estimates were most consistent in the high R2 * conditions and under varying SNR, and may be more reliable when high iron load is suspected.
Authors: Aaryani Tipirneni-Sajja; Ralf B Loeffler; Axel J Krafft; Andrea N Sajewski; Robert J Ogg; Jane S Hankins; Claudia M Hillenbrand Journal: J Magn Reson Imaging Date: 2018-10-25 Impact factor: 4.813
Authors: Marzanna Obrzut; Vitaliy Atamaniuk; Kevin J Glaser; Jun Chen; Richard L Ehman; Bogdan Obrzut; Marian Cholewa; Krzysztof Gutkowski Journal: Sci Rep Date: 2020-10-21 Impact factor: 4.379