Measurement of murine kidney functional biomarkers using DCE-MRI: A multi-slice TRICKS technique and semi-automated...

No prior work had resolved the limitations of slow, single-slice imaging for assessing whole-kidney function in small animal models. Current protocols often struggle to balance high temporal resolution with the spatial coverage required for comprehensive physiological mapping. It was already known that dynamic contrast-enhanced magnetic resonance imaging provides valuable insights into renal perfusion and filtration. However, traditional acquisition schemes frequently result in prolonged scan times that hinder high-throughput experimental workflows. This gap motivated the development of more efficient sampling strategies to capture rapid physiological changes. That uncertainty drove the need for a multi-slice approach capable of maintaining diagnostic accuracy. Prior research has shown that time-resolved imaging of contrast kinetics can accelerate data acquisition in various clinical settings. No previous studies had fully integrated these advanced sampling schemes with automated processing to streamline murine renal assessments.

The aim of this study is to present a rapid multi-slice measurement method for assessing kidney function in mice. Researchers sought to overcome the constraints of traditional imaging by utilizing time-resolved sampling and automated processing. This work addresses the need for a more efficient way to perform comprehensive renal evaluations in small animal models. The motivation stems from the requirement to balance high temporal resolution with the spatial coverage necessary for accurate physiological mapping. By implementing a specialized sampling scheme, the authors intended to improve the throughput of dynamic contrast-enhanced magnetic resonance imaging. The project also focused on developing a semi-automated algorithm to simplify the complex task of image analysis. This effort was driven by the goal of reducing the manual labor associated with extracting functional parameters from large datasets. Ultimately, the study provides a validated framework for high-speed, accurate assessment of renal health in experimental research settings.

The research team implemented a multi-slice sampling design to replace traditional single-slice acquisition protocols for renal imaging. They utilized a specialized time-resolved sequence to increase the speed of data collection during contrast administration. An automated software pipeline was constructed using basic image processing routines and machine learning algorithms to handle the resulting datasets. The investigators tested the reliability of this new approach by comparing it against a validated single-slice standard in healthy subjects. To demonstrate practical utility, the authors performed experiments on mice subjected to either sham procedures or the induction of unilateral renal artery stenosis. Renal functional metrics were subsequently derived using a bi-compartment mathematical model applied to the processed images. The entire workflow was optimized to ensure that image analysis could be completed within a fifteen-minute window per animal. This review approach focuses on the technical integration of hardware acceleration and computational efficiency for small animal diagnostics.

The multi-slice sampling scheme achieved an acceleration factor of 2.7, allowing for the acquisition of eight axial slices at a temporal resolution of 1.23 seconds per scan. Key findings from the literature show that the new technique produced renal perfusion and glomerular filtration rate values comparable to the established single-slice method. The automated processing pipeline successfully reduced the analysis time to under fifteen minutes for both kidneys. Model-fitted parameters effectively differentiated control kidneys from those with induced stenosis. Specifically, renal perfusion was measured at 706.5 milliliters per 100 grams per minute in controls, compared to 375.9 in stenotic kidneys. Blood flow values were significantly lower in the stenotic group, recorded at 0.7 milliliters per minute versus 1.6 in controls. Glomerular filtration rate also showed a marked difference, with 58.0 microliters per minute in stenotic mice versus 142.9 in healthy controls. These results confirm the capability of the system to provide rapid, comprehensive functional assessments in murine models.

The authors demonstrate that their multi-slice acquisition strategy provides a robust alternative to conventional single-slice imaging for renal assessment. Synthesis and implications suggest that this approach significantly enhances the efficiency of longitudinal studies in small animal models. The researchers propose that the integration of automated processing software reduces the manual burden of data analysis to under fifteen minutes. Their findings indicate that the model-fitted parameters reliably distinguish between healthy and stenotic renal tissue in mice. The study confirms that the acceleration factor achieved allows for comprehensive coverage without sacrificing the precision of physiological measurements. These results imply that the described workflow is suitable for large-scale investigations requiring rapid functional readouts. The authors conclude that the combination of advanced sampling and machine learning facilitates a more thorough evaluation of kidney health. This work provides a practical framework for future research aiming to improve the throughput of renal functional imaging.