Perfusion and T2 Relaxation Time as Predictors of Severity and Outcome in Sepsis-Associated Acute Kidney Injury: A Preclinical MRI Study.

BACKGROUND: Preventing sepsis-associated acute kidney injury (S-AKI) can be challenging because it develops rapidly and is often asymptomatic. Probability assessment of disease progression for therapeutic follow-up and outcome are important to intervene and prevent further damage. PURPOSE: To establish a noninvasive multiparametric MRI (mpMRI) tool, including T1 , T2 , and perfusion mapping, for probability assessment of the outcome of S-AKI. STUDY TYPE: Preclinical randomized prospective study. ANIMAL MODEL: One hundred and forty adult female SD rats (65 control and 75 sepsis). FIELD STRENGTH/SEQUENCE: 9.4T; T1 and perfusion map (FAIR-EPI) and T2 map (multiecho RARE). ASSESSMENT: Experiment 1: To identify renal injury in relation to sepsis severity, serum creatinine levels were determined (31 control and 35 sepsis). Experiment 2: Animals underwent mpMRI (T1 , T2 , perfusion) 18 hours postsepsis. A subgroup of animals was immediately sacrificed for histology examination (nine control and seven sepsis). Result of mpMRI in follow-up subgroup (25 control and 33 sepsis) was used to predict survival outcomes at 96 hours. STATISTICAL TESTS: Mann-Whitney U test, Spearman/Pearson correlation (r), P < 0.05 was considered statistically significant. RESULTS: Severely ill septic animals exhibited significantly increased serum creatinine levels compared to controls (70 ± 30 vs. 34 ± 9 µmol/L, P < 0.0001). Cortical perfusion (480 ± 80 vs. 330 ± 140 mL/100 g tissue/min, P < 0.005), and cortical and medullary T2 relaxation time constants were significantly reduced compared to controls (41 ± 4 vs. 37 ± 5 msec in cortex, P < 0.05, 52 ± 7 vs. 45 ± 6 msec in medulla, P < 0.05). The combination of cortical T2 relaxation time constants and perfusion results at 18 hours could predict survival outcomes at 96 hours with high sensitivity (80%) and specificity (73%) (area under curve of ROC = 0.8, Jmax = 0.52). DATA CONCLUSION: This preclinical study suggests combined T2 relaxation time and perfusion mapping as first line diagnostic tool for treatment planning. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

In vivo assessment of anisotropy of apparent magnetic susceptibility in white matter from a single orientation acquisition.

Multiple studies have reported a significant dependence of the effective transverse relaxation rate constant (R2*) and the phase of gradient-echo based (GRE) signal on the orientation of white matter fibres in the human brain. It has also been hypothesized that magnetic susceptibility, as obtained by single-orientation quantitative susceptibility mapping (QSM), exhibits such a dependence. In this study, we investigated this hypothesized relationship in a cohort of healthy volunteers. We show that R2* follows the predicted orientation dependence consistently across white matter regions, whereas the apparent magnetic susceptibility is related differently to fibre orientation across the brain and often in a complex non-monotonic manner. In addition, we explored the effect of fractional anisotropy measured by diffusion-weighted MRI on the strength of the orientation dependence and observed only a limited influence in many regions. However, with careful consideration of such an impact and the limitations imposed by the ill-posed nature of the dipole inversion process, it is possible to study magnetic susceptibility anisotropy in specific brain regions with a single orientation acquisition.

Magnetic susceptibility anisotropy in normal appearing white matter in multiple sclerosis from single-orientation acquisition.

Quantitative susceptibility mapping (QSM) has been successfully applied to study changes in deep grey matter nuclei as well as in lesional tissue, but its application to white matter has been complicated by the observed orientation dependence of gradient echo signal. The anisotropic susceptibility tensor is thought to be at the origin of this orientation dependence, and magnetic susceptibility anisotropy (MSA) derived from this tensor has been proposed as a marker of the state and integrity of the myelin sheath and may therefore be of particular interest for the study of demyelinating pathologies such as multiple sclerosis (MS). Reconstruction of the susceptibility tensor, however, requires repeated measurements with multiple head orientations, rendering the approach impractical for clinical applications. In this study, we combined single-orientation QSM with fibre orientation information to assess apparent MSA in three white matter tracts, i.e., optic radiation (OR), splenium of the corpus callosum (SCC), and superior longitudinal fascicle (SLF), in two cohorts of 64 healthy controls and 89 MS patients. The apparent MSA showed a significant decrease in optic radiation in the MS cohort compared with healthy controls. It decreased in the MS cohort with increasing lesion load in OR and with disease duration in the splenium. All of this suggests demyelination in normal appearing white matter. However, the apparent MSA observed in the SLF pointed to potential systematic issues that require further exploration to realize the full potential of the presented approach. Despite the limitations of such single-orientation ROI-specific estimation, we believe that our clinically feasible approach to study degenerative changes in WM is worthy of further investigation.

Characterization of microparticles of iron oxide for magnetic resonance imaging.

Microparticles of iron oxide (MPIOs) are increasingly used for contrast generation in magnetic resonance imaging (MRI). In particular, Dynabeads® MyOne™ Tosylactivated MPIOs have enabled sensitive and targeted molecular imaging, e.g., to detect vascular inflammation. For the first time we measured the relaxivities as well as the molar susceptibility χM of these MPIOs at 7 T in agarose gels. They are r1 = 0.69 ± 0.03 s-1/mM, r2 = 220 ± 6 s-1/mM, r2* = 679 ± 14 s-1/mM, and χM = 0.66 ± 0.05 ppm/mM, when expressed with respect to the iron concentration. These material parameters are essential to optimize MRI protocols and progress toward quantitative imaging. To address the heterogeneous nature of the MPIO distributions over the size of a typical MRI voxel, we coupled the MPIOs to a fluorophore to create a bimodal phantom that can be imaged by both Light Sheet microscopy and MRI. In this phantom, the MPIOs produced contrast similar to that found in vivo . The submicron resolution of Light Sheet microscopy images provided a precise measurement of the MPIO spatial distribution in phantoms also imaged by MRI. MPIO aggregates occupying less than one MRI voxel were responsible for alterations in R2* and magnetic susceptibility χ across several MRI voxels. In these cases, the sum of R2* or χ over the affected MRI volume correlated better with the microscopically determined number of MPIOs. These findings were confirmed with simulations performed in the static dephasing regime. The microscopically determined MPIO distribution was also entered directly into the simulation framework, indicating that the bimodal phantom is a useful tool to test theoretical models against experimental measurements.

Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis.

The diagnosis of multiple sclerosis (MS) is usually based on clinical symptoms and signs of damage to the central nervous system, which is assessed using magnetic resonance imaging. The correct interpretation of these data requires excellent clinical expertise and experience. Deep neural networks aim to assist clinicians in identifying MS using imaging data. However, before such networks can be integrated into clinical workflow, it is crucial to understand their classification strategy. In this study, we propose to use a convolutional neural network to identify MS patients in combination with attribution algorithms to investigate the classification decisions. The network was trained using images acquired with susceptibility-weighted imaging (SWI), which is known to be sensitive to the presence of paramagnetic iron components and is routinely applied in imaging protocols for MS patients. Different attribution algorithms were used to the trained network resulting in heatmaps visualizing the contribution of each input voxel to the classification decision. Based on the quantitative image perturbation method, we selected DeepLIFT heatmaps for further investigation. Single-subject analysis revealed veins and adjacent voxels as signs for MS, while the population-based study revealed relevant brain areas common to most subjects in a class. This pattern was found to be stable across different echo times and also for a multi-echo trained network. Intensity analysis of the relevant voxels revealed a group difference, which was found to be primarily based on the T1w magnitude images, which are part of the SWI calculation. This difference was not observed in the phase mask data.