Diffusion tractography of kidney by high angular resolution diffusion imaging

Diffusion magnetic resonance imaging(MRI) has been utilized to probe the renal microstructures but investigating the three-dimensional(3D) tubular network still presents significant challenges due to the complicated architecture of kidney. This study aims to assess whether high angular resolution diffusion imaging(HARDI) could improve the reconstruction of 3D tubular architectures. Kidneys from both mice and rats were imaged using 3D diffusion-weighted pulse sequences at 9.4 T. Five healthy mouse kidneys were scanned at an isotropic spatial resolution of 40 μm, with a b value of 1500 s/mm2across 46 diffusion encoding directions. The study employed diffusion tensor imaging(DTI) and generalized Q-sampling imaging(GQI) to examine the tubular orientation distributions and tractography, validated by conventional histology. Fractional anisotropy(FA) and mean diffusivity(MD) were quantified and compared among the inner medullar(IM), outer medullar(OM), and cortex(CO) at different angular resolutions. FA values, estimated with 6 diffusion-weighted images(DWIs), were significantly overestimated by 49.9%(p < 0.001) in IM, 179.4%(p < 0.001) in OM, and 225.5%(p < 0.001) in CO, compared to using 46 DWIs. In contrast, MD exhibited less variations to angular resolution variations(3.4% in IM, 4.2% in OM, and 4.6% in CO). Both DTI and GQI at high angular resolution successfully traced renal tubular structures throughout the kidney, with GQI demonstrating superior performance in generating more continuous tracts. Furthermore, disrupted renal tubule structures were observed in a chronic kidney disease(CKD) rat model. HARDI, especially when combined with the GQI approach, holds promise in tracking complicated 3D tubule architectures and may serve as a potent tool for kidney disease research.

Directional Filter, Local Frequency Estimate and Algebraic Inversion of Differential Equation of Psoas Major Magnetic Resonance Elastography

Magnetic resonance elastography (MRE) can visualize the shear wave propagation of in vivo tissues, which can be mapped into viscoelastic properties. No study has measured the biomechanical properties of the PM muscle in vivo using MRE. In this study, we evaluated stiffness values calculated by local frequency estimate (LFE) and algebraic inversion of differential equation (AIDE) in PM-MRE. The PM muscles of 17 healthy male volunteers were scanned in supine position by MRE. The Laplacian-based estimate (LBE) phase wrapped image data were filtered by gaussian-bandpass filter (GBF), and by both directional and GBF. LFE (MREWave) and AIDE wave inversion methods were used to calculate the respective elastograms. The wave interferences were removed by directional filtering, and smooth wave fields were obtained. The stiffness values calculated by LFE of non-DF images were 1.39 ± 0.25 kPa and 1.33 ± 0.22 kPa for right and left PM respectively, whereas for DF images, they were 1.26 ± 0.20 kPa for right PM and 1.18 ± 0.28 kPa for left PM. The stiffness values calculated by AIDE of non-DF images were 0.78 ± 0.10 kPa and 0.78 ± 0.13 kPa for right and left PM respectively, whereas for DF images, they were 0.73 ± 0.12 kPa for right PM and 0.74 ± 0.11 kPa for left PM. There was no statistically significant difference in mean values of stiffness with/without applying directional filter whereas there was a statistically significant difference in mean values of stiffness between LFE and AIDE. Both LFE and AIDE could be used for psoas major MR Elastography.

Phase Unwrapping in Magnetic Resonance Elastography

Phase Unwrapping (PU) is an ill-posed problem in Magnetic Resonance Elastography (MRE). The phase information is not usable until the phases are retrieved by using PU algorithms. In this present study, we attempt to determine the ideal PU method for MRE using both phantom and volunteer psoas major (PM) muscle images. All the MRE experiments were carried out in Philips MRI (Achieva 3.0 T, Best, The Netherlands). A multi-echo gradient-echo MRE pulse sequence was employed and the four PU methods were considered based on their easy user platform. They are namely, Minimum Discontinuity (MD), Laplacian-Based Estimate (LBE), Region Growing (RG) and Dilate-Erode (DE) Propagate. Phantom images were successfully unwrapped by all four methods, whereas MD and LBE could only unwrap PM muscle images properly. RG and DE failed to unwrap the PM muscle images.