Special issue on magnetic resonance imaging biomarkers of renal disease

Imaging Markers Derived From MRI-Based Automated Kidney Segmentation

BACKGROUND

Population-wide research on potential new imaging biomarkers of the kidney depends on accurate automated segmentation of the kidney and its compartments (cortex, medulla, and sinus).

METHODS

We developed a robust deep-learning framework for kidney (sub-)segmentation based on a hierarchical, three-dimensional convolutional neural network (CNN) that was optimized for multiscale problems of combined localization and segmentation. We applied the CNN to abdominal magnetic resonance images from the population-based German National Cohort (NAKO) study.

RESULTS

There was good to excellent agreement between the model predictions and manual segmentations. The median values for the body-surface normalized total kidney, cortex, medulla, and sinus volumes of 9934 persons were 158, 115, 43, and 24 mL/m2. Distributions of these markers are provided both for the overall study population and for a subgroup of persons without kidney disease or any associated conditions. Multivariable adjusted regression analyses revealed that diabetes, male sex, and a higher estimated glomerular filtration rate (eGFR) are important predictors of higher total and cortical volumes. Each increase of eGFR by one unit (i.e., 1 mL/min per 1.73 m2 body surface area) was associated with a 0.98 mL/m2 increase in total kidney volume, and this association was significant. Volumes were lower in persons with eGFR-defined chronic kidney disease.

CONCLUSION

The extraction of image-based biomarkers through CNN-based renal sub-segmentation using data from a population-based study yields reliable results, forming a solid foundation for future investigations.

Noninvasive Assessment of Fibrosis Following Ischemia/Reperfusion Injury in Rodents Utilizing Na Magnetic Resonance Imaging

Fibrosis is often heterogeneously distributed, and classical biopsies do not reflect this. Noninvasive methods for renal fibrosis have been developed to follow chronic kidney diseases (CKD) and to monitor anti-fibrotic therapy. In this study, we combined two approaches to assess fibrosis regression following renal ischemia-reperfusion injury (IRI): magnetic resonance imaging (MRI) and noninvasive extracellular matrix (ECM) biomarkers. MRI was used to evaluate fibrosis in bilateral IRI in rats after reperfusion at 7, 14, and 21 days. This was performed with ¹HT₁ and T₂* mapping, dynamic contrast-enhanced (DCE)-MRI, and chemical shift imaging (CSI)-²³Na. The degradation of laminin gamma-1 chain (LG1M) and type III collagen (C3M) was measured in urine and plasma. Fibrosis was analyzed in tissue using fibronectin (FN) and alpha-smooth muscle actin (α-SMA) using quantitative polymerase chain reaction qPCR and western blotting. We found increased fibrosis 7 days after reperfusion, which dropped to sham levels after 21 days. Single kidney glomerular filtration rate (skGFR), perfusion (DCE-MRI), and total ²³Na kidney content correlated positively with fibrotic markers FN and α-SMA as well as noninvasive LG1M and C3M. We showed that novel MRI protocols and ECM markers could track fibrogenic development. This could give rise to a multi-parametric practice to diagnose and assess fibrosis whilst treating kidney disease without using invasive methods.