Dynamic parametric MRI and deep learning: Unveiling renal pathophysiology through accurate kidney size quantification.

Renal pathologies often manifest as alterations in kidney size, providing a valuable avenue for employing dynamic parametric MRI as a means to derive kidney size measurements for the diagnosis, treatment, and monitoring of renal disease. Furthermore, this approach holds significant potential in supporting MRI data-driven preclinical investigations into the intricate mechanisms underlying renal pathophysiology. The integration of deep learning algorithms is crucial in achieving rapid and precise segmentation of the kidney from temporally resolved parametric MRI, facilitating the use of kidney size as a meaningful (pre)clinical biomarker for renal disease. To explore this potential, we employed dynamic parametric T2 mapping of the kidney in rats in conjunction with a custom-tailored deep dilated U-Net (DDU-Net) architecture. The architecture was trained, validated, and tested on manually segmented ground truth kidney data, with benchmarking against an analytical segmentation model and a self-configuring no new U-Net. Subsequently, we applied our approach to in vivo longitudinal MRI data, incorporating interventions that emulate clinically relevant scenarios in rats. Our approach achieved high performance metrics, including a Dice coefficient of 0.98, coefficient of determination of 0.92, and a mean absolute percentage error of 1.1% compared with ground truth. The DDU-Net enabled automated and accurate quantification of acute changes in kidney size, such as aortic occlusion (-8% ± 1%), venous occlusion (5% ± 1%), furosemide administration (2% ± 1%), hypoxemia (-2% ± 1%), and contrast agent-induced acute kidney injury (11% ± 1%). This approach can potentially be instrumental for the development of dynamic parametric MRI-based tools for kidney disorders, offering unparalleled insights into renal pathophysiology.

Comprehensive Analysis of the Spatial Distribution of Gadolinium, Iron, Manganese, and Phosphorus in the Brain of Healthy Rats After High-Dose Administrations of Gadodiamide and Gadobutrol.

OBJECTIVES: After the administration of gadolinium-based contrast agents (GBCAs), residual gadolinium (Gd) has been detected in a few distinct morphological structures of the central nervous system (CNS). However, a systematic, comprehensive, and quantitative analysis of the spatial Gd distribution in the entire brain is not yet available. The first aim of this study is to provide this analysis in healthy rats after administration of high GBCA doses. The second aim is to assess the spatial distributions and possible Gd colocalizations of endogenous iron (Fe), manganese (Mn), and phosphorus (P). In addition, the presence of Gd in proximity to blood vessels was assessed by immunohistochemistry. MATERIALS AND METHODS: Male rats were randomly assigned to 3 groups (n = 3/group): saline (control), gadodiamide (linear GBCA), and gadobutrol (macrocyclic GBCA) with cumulative Gd doses of 14.4 mmol/kg of body mass. Five weeks after the last administration, the brains were collected and cryosectioned. The spatial distributions of Gd, Fe, Mn, and P were analyzed in a total of 130 sections, each covering the brain in 1 of the 3 perpendicular anatomical orientations, using laser ablation coupled with inductively coupled plasma mass spectrometry. Quantitative spatial element maps were generated, and the concentrations of Gd, Fe, and Mn were measured in 31 regions of interest covering various distinct CNS structures. Correlation analyses were performed to test for possible colocalization of Gd, Fe, and Mn. The spatial proximity of Gd and blood vessels was studied using metal-tagged antibodies against von Willebrand factor with laser ablation coupled with inductively coupled plasma mass spectrometry. RESULTS: After administration of linear gadodiamide, high Gd concentrations were measured in many distinct structures of the gray matter. This involved structures previously reported to retain Gd after linear GBCA, such as the deep cerebellar nuclei or the globus pallidus, but also structures that had not been reported so far including the dorsal subiculum, the retrosplenial cortex, the superior olivary complex, and the inferior colliculus. The analysis in all 3 orientations allowed the localization of Gd in specific subregions and layers of certain structures, such as the hippocampus and the primary somatosensory cortex. After macrocyclic gadobutrol, the Gd tissue concentration was significantly lower than after gadodiamide. Correlation analyses of region of interest concentrations of Gd, Fe, and Mn revealed no significant colocalization of Gd with endogenous Fe or Mn in rats exposed to either GBCA. Immunohistochemistry revealed a colocalization of Gd traces with vascular endothelium in the deep cerebellar nuclei after gadobutrol, whereas the majority of Gd was found outside the vasculature after gadodiamide. CONCLUSIONS: In rats exposed to gadodiamide but not in rats exposed to gadobutrol, high Gd concentrations were measured in various distinct CNS structures, and structures not previously reported were identified to contain Gd, including specific subregions and layers with different cytoarchitecture and function. Knowledge of these distinct spatial patterns may pave the way for tailored functional neurological testing. Signs for the localization of the remaining Gd in the vascular endothelium were prominent for gadobutrol but not gadodiamide. The results also indicate that local transmetalation with endogenous Fe or Mn is unlikely to explain the spatial patterns of Gd deposition in the brain, which argues against a general role of these metals in local transmetalation and release of Gd ions in the CNS.

Continuous diffusion spectrum computation for diffusion-weighted magnetic resonance imaging of the kidney tubule system.

BACKGROUND: The use of rigid multi-exponential models (with a priori predefined numbers of components) is common practice for diffusion-weighted MRI (DWI) analysis of the kidney. This approach may not accurately reflect renal microstructure, as the data are forced to conform to the a priori assumptions of simplified models. This work examines the feasibility of less constrained, data-driven non-negative least squares (NNLS) continuum modelling for DWI of the kidney tubule system in simulations that include emulations of pathophysiological conditions. METHODS: Non-linear least squares (LS) fitting was used as reference for the simulations. For performance assessment, a threshold of 5% or 10% for the mean absolute percentage error (MAPE) of NNLS and LS results was used. As ground truth, a tri-exponential model using defined volume fractions and diffusion coefficients for each renal compartment (tubule system: Dtubules , ftubules ; renal tissue: Dtissue , ftissue ; renal blood: Dblood , fblood ;) was applied. The impact of: (I) signal-to-noise ratio (SNR) =40-1,000, (II) number of b-values (n=10-50), (III) diffusion weighting (b-rangesmall =0-800 up to b-rangelarge =0-2,180 s/mm2), and (IV) fixation of the diffusion coefficients Dtissue and Dblood was examined. NNLS was evaluated for baseline and pathophysiological conditions, namely increased tubular volume fraction (ITV) and renal fibrosis (10%: grade I, mild) and 30% (grade II, moderate). RESULTS: NNLS showed the same high degree of reliability as the non-linear LS. MAPE of the tubular volume fraction (ftubules ) decreased with increasing SNR. Increasing the number of b-values was beneficial for ftubules precision. Using the b-rangelarge led to a decrease in MAPE ftubules compared to b-rangesmall. The use of a medium b-value range of b=0-1,380 s/mm2 improved ftubules precision, and further bmax increases beyond this range yielded diminishing improvements. Fixing Dblood and Dtissue significantly reduced MAPE ftubules and provided near perfect distinction between baseline and ITV conditions. Without constraining the number of renal compartments in advance, NNLS was able to detect the (fourth) fibrotic compartment, to differentiate it from the other three diffusion components, and to distinguish between 10% vs. 30% fibrosis. CONCLUSIONS: This work demonstrates the feasibility of NNLS modelling for DWI of the kidney tubule system and shows its potential for examining diffusion compartments associated with renal pathophysiology including ITV fraction and different degrees of fibrosis.

Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings.

AIM: Kidney diseases constitute a major health challenge, which requires noninvasive imaging to complement conventional approaches to diagnosis and monitoring. Several renal pathologies are associated with changes in kidney size, offering an opportunity for magnetic resonance imaging (MRI) biomarkers of disease. This work uses dynamic MRI and an automated bean-shaped model (ABSM) for longitudinal quantification of pathophysiologically relevant changes in kidney size. METHODS: A geometry-based ABSM was developed for kidney size measurements in rats using parametric MRI (T2 , T2 * mapping). The ABSM approach was applied to longitudinal renal size quantification using occlusion of the (a) suprarenal aorta or (b) the renal vein, (c) increase in renal pelvis and intratubular pressure and (d) injection of an X-ray contrast medium into the thoracic aorta to induce pathophysiologically relevant changes in kidney size. RESULTS: The ABSM yielded renal size measurements with accuracy and precision equivalent to the manual segmentation, with >70-fold time savings. The automated method could detect a ~7% reduction (aortic occlusion) and a ~5%, a ~2% and a ~6% increase in kidney size (venous occlusion, pelvis and intratubular pressure increase and injection of X-ray contrast medium, respectively). These measurements were not affected by reduced image quality following administration of ferumoxytol. CONCLUSION: Dynamic MRI in conjunction with renal segmentation using an ABSM supports longitudinal quantification of changes in kidney size in pathophysiologically relevant experimental setups mimicking realistic clinical scenarios. This can potentially be instrumental for developing MRI-based diagnostic tools for various kidney disorders and for gaining new insight into mechanisms of renal pathophysiology.