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Background
Renal blood flow (RBF) can be measured with dynamic contrast enhanced-MRI (DCE-MRI) and arterial spin labeling (ASL). Unfortunately, individual estimates from both methods vary and reference-standard methods are not available. A potential solution is to include a third, arbitrating MRI method in the comparison.
Purpose
To compare RBF es...
Contexts in source publication
Context 1
... acquisition involved 2D cine PC-MRI, a prototypical sequence for 3D renal ASL (pseudo-continuous arterial spin labeling [pCASL]) and 2D DCE-MRI sequences acquired in the same order. An overview of the MRI acquisition parameters is listed in Table 1. PHASE CONTRAST. The renal arteries were depicted using a combination of a coronal survey scan and an axial halfFourier acquisition single-shot turbo spin-echo (HASTE) sequence. ...Context 2
... were recorded using a pseudo-continuous arterial spin labeling (pCASL) sequence with a slice selective labeling pulse. The prototype sequence is implemented by the vendor and acquires label, control images, and a reference proton density weighted (M0) image within the same package (sequence parameters are provided in Table 1). The labeling plane was positioned perpendicular to the abdominal aorta 10 cm above the center of the kidneys. ...Citations
... Since meal consumption is often associated with RBF changes, fasting before the study is commonly used. Low baseline RBF value was observed in some studies [63, 81,82] in which the authors have attributed to overnight fasting, but no empirical study has yet sufficiently addressed this point. While awaiting further insight from future studies on the matter, a preliminary conclusion can be reached from the literature synthesis as to stress the importance of controlling food intake prior to RBF measurement. ...
Objectives
Renal blood flow (RBF) is controlled by a number of physiological factors that can contribute to the variability of its measurement. The purpose of this review is to assess the changes in RBF in response to a wide range of physiological confounders and derive practical recommendations on patient preparation and interpretation of RBF measurements with MRI.
Methods
A comprehensive search was conducted to include articles reporting on physiological variations of renal perfusion, blood and/or plasma flow in healthy humans.
Results
A total of 24 potential confounders were identified from the literature search and categorized into non-modifiable and modifiable factors. The non-modifiable factors include variables related to the demographics of a population (e.g. age, sex, and race) which cannot be manipulated but should be considered when interpreting RBF values between subjects. The modifiable factors include different activities (e.g. food/fluid intake, exercise training and medication use) that can be standardized in the study design. For each of the modifiable factors, evidence-based recommendations are provided to control for them in an RBF-measurement.
Conclusion
Future studies aiming to measure RBF are encouraged to follow a rigorous study design, that takes into account these recommendations for controlling the factors that can influence RBF results.
... The main MRI methods used to evaluate kidney haemodynamics are phase-contrast MRI (PC-MRI), arterial spin labelling (ASL), and dynamic-contrast-enhanced MRI (DCE-MRI) (see Table 1). A recent study compared all three techniques in T2DM patients and concluded that the repeatability of PC-MRI measurements supported its use as a reference method for MRI of RBF [89]. Furthermore, the comparison showed that while DCE-MRI and ASL measurements are unbiased, they showed poor precision relative to PC-MRI [89]. ...
... A recent study compared all three techniques in T2DM patients and concluded that the repeatability of PC-MRI measurements supported its use as a reference method for MRI of RBF [89]. Furthermore, the comparison showed that while DCE-MRI and ASL measurements are unbiased, they showed poor precision relative to PC-MRI [89]. ...
... In DKD patients and healthy volunteers examined 2 weeks apart, RBF had a CV of 7% and an ICC of 0.97 [48]. A CV of 6% was seen in healthy volunteers despite the relatively long interval to complete four repeat scans (4 months on average) [89]. ...
Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.
... Therefore implementation of large, multicenter, and clinically led studies with prospective followup is needed, including evaluation of repeatability studies for assessing the accuracy of multiparametric renal MRI approaches (Claudon et al., 2014;de Boer et al., 2021). More comparative studies are also needed to assess the accuracy of the DCE-MRIderived estimates relative to other methods or techniques, although reference standard methods are not available (Taton et al., 2019;Alhummiany et al., 2022). New Machine Learning and Deep Learning approaches have been recently adopted for several steps related to image acquisition, reconstruction, segmentation, and postprocessing, with potential significant improvements in kidney function quantifications (Klepaczko et al., 2021). ...
... Only limited studies using renal PC-MRI in diabetic patients are available. In a renal blood flow validation study in 25 patients with type 2 diabetes (36% female), a good agreement between ASL, delayed contrast enhancement (DCE), and PC RBF was observed on average, but not in individual patients [32]. Of interest, PC-MRI showed a significantly smaller reproducibility error than ASL [32]. ...
... In a renal blood flow validation study in 25 patients with type 2 diabetes (36% female), a good agreement between ASL, delayed contrast enhancement (DCE), and PC RBF was observed on average, but not in individual patients [32]. Of interest, PC-MRI showed a significantly smaller reproducibility error than ASL [32]. PC-MRI has mainly been used in patients with suspected or confirmed renal artery stenosis, and fewer studies have focused on CKD or DKD. ...
... Indeed, the filtration fraction of CKD patients was lower, and the R2* values did not differ between the CKD patients and the controls. In a randomised, double-blind, placebo-controlled, crossover trial in adults with type 1 diabetes and albuminuria, a single 50 mg dose of the SGLT2 inhibitor dapagliflozin and placebo in random order, separated by a two-week washout period, did not change renal perfusion or blood flow, but improved renal oxygenation [32,34]. ...
Diabetic kidney disease (DKD) is a major public health problem and its incidence is rising. The disease course is unpredictable with classic biomarkers, and the search for new tools to predict adverse renal outcomes is ongoing. Renal magnetic resonance imaging (MRI) now enables the quantification of metabolic and microscopic properties of the kidneys such as single-kidney, cortical and medullary blood flow, and renal tissue oxygenation and fibrosis, without the use of contrast media. A rapidly increasing number of studies show that these techniques can identify early kidney damage in patients with DKD, and possibly predict renal outcome. This review provides an overview of the currently most frequently used techniques, a summary of the results of some recent studies, and our view on their potential applications, as well as the hurdles to be overcome for the integration of these techniques into the clinical care of patients with DKD.
Renal magnetic resonance imaging (MRI) nowadays offers multimodal imaging to derive functional imaging parameters giving information on volume and volume changes, microstructure, perfusion, and permeability and metabolism. Imaging these parameters with modern scanner hardware comes along with a huge amount of image data ranging from hundreds to several thousands of images per exam and patient. Therefore, manual processing of the abovementioned parameters becomes infeasible and demands for automated image processing. Furthermore, automation of the extraction of renal imaging parameters also removes possible bias due to reader dependencies. There are several promising approaches reported in the literature that tackle the three tasks described in this chapter: segmentation, registration, and modeling. A bottleneck toward clinical implementation is often the lack of available open source software, evaluation on large datasets, and consensus on workflows.
Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end‐stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T 1 ‐ or T 2 ‐weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision‐making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break‐through are discussed including biological validation of the MRI findings, large‐scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI.
Level of Evidence
1
Technical Efficacy
Stage 2
Renal diseases pose a significant socio‐economic burden on healthcare systems. The development of better diagnostics and prognostics is well‐recognized as a key strategy to resolve these challenges. Central to these developments are MRI biomarkers, due to their potential for monitoring of early pathophysiological changes, renal disease progression or treatment effects. The surge in renal MRI involves major cross‐domain initiatives, large clinical studies, and educational programs. In parallel with these translational efforts, the need for greater (patho)physiological specificity remains, to enable engagement with clinical nephrologists and increase the associated health impact. The ISMRM 2022 Member Initiated Symposium (MIS) on renal MRI spotlighted this issue with the goal of inspiring more solutions from the ISMRM community. This work is a summary of the MIS presentations devoted to: 1) educating imaging scientists and clinicians on renal (patho)physiology and demands from clinical nephrologists, 2) elucidating the connection of MRI parameters with renal physiology, 3) presenting the current state of leading MR surrogates in assessing renal structure and functions as well as their next generation of innovation, and 4) describing the potential of these imaging markers for providing clinically meaningful renal characterization to guide or supplement clinical decision making. We hope to continue momentum of recent years and introduce new entrants to the development process, connecting (patho)physiology with (bio)physics, and conceiving new clinical applications. We envision this process to benefit from cross‐disciplinary collaboration and analogous efforts in other body organs, but also to maximally leverage the unique opportunities of renal physiology.
Level of Evidence
1
Technical Efficacy Stage
2
