Modified dixon-based renal dynamic contrast-enhanced MRI facilitates automated registration and perfusion analysis

PurposeRenal dynamic contrast-enhanced (DCE) MRI provides information on renal perfusion and filtration. However, clinical implementation is hampered by challenges in postprocessing as a result of misalignment of the kidneys due to respiration. We propose to perform automated image registration using the fat-only images derived from a modified Dixon reconstruction of a dual-echo acquisition because these provide consistent contrast over the dynamic series. MethodsDCE data of 10 hypertensive patients was used. Dual-echo images were acquired at 1.5T with temporal resolution of 3.9 s during contrast agent injection. Dixon fat, water, and in-phase and opposed-phase (OP) images were reconstructed. Postprocessing was automated. Registration was performed both to fat images and OP images for comparison. Perfusion and filtration values were extracted from a two-compartment model fit. ResultsAutomatic registration to fat images performed better than automatic registration to OP images with visible contrast enhancement. Median vertical misalignment of the kidneys was 14mm prior to registration, compared to 3mm and 5mm with registration to fat images and OP images, respectively (P=0.03). Mean perfusion values and MR-based glomerular filtration rates (GFR) were 23364mL/100mL/min and 60 +/- 36mL/minute, respectively, based on fat-registered images. MR-based GFR correlated with creatinine-based GFR (P=0.04) for fat-registered images. For unregistered and OP-registered images, this correlation was not significant. ConclusionAbsence of contrast changes on Dixon fat images improves registration in renal DCE MRI and enables automated postprocessing, resulting in a more accurate estimation of GFR. Magn Reson Med 80:66-76, 2018. (c) 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.