Comparison of Gd-DTPA and Gd-BOPTA for studying renal perfusion and filtration

AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease

To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (VRP), renal cortex volume (VRC), renal medulla volume (VRM), the CT values of renal parenchyma (HuRP), the CT values of renal cortex (HuRC), and the CT values of renal medulla (HuRM) (F = 93.476, 144.918, 9.637, 170.533, 216.616, and 94.283; p < 0.001). Pearson correlation analysis revealed significant positive associations between glomerular filtration rate (eGFR) and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = 0.749, 0.818, 0.321, 0.819, 0.820, and 0.747, respectively, all p < 0.001). Similarly, a negative correlation was observed between serum creatinine (Scr) levels and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = - 0.759, - 0.777, - 0.420, - 0.762, - 0.771, and - 0.726, respectively, all p < 0.001). For predicting CKD in males, VRP had an area under the curve (AUC) of 0.726, p < 0.001; VRC, AUC 0.765, p < 0.001; VRM, AUC 0.578, p = 0.018; HuRP, AUC 0.912, p < 0.001; HuRC, AUC 0.952, p < 0.001; and HuRM, AUC 0.772, p < 0.001 in males. In females, VRP had an AUC of 0.813, p < 0.001; VRC, AUC 0.851, p < 0.001; VRM, AUC 0.623, p = 0.060; HuRP, AUC 0.904, p < 0.001; HuRC, AUC 0.934, p < 0.001; and HuRM, AUC 0.840, p < 0.001. The optimal cutoff values for predicting CKD in HuRP are 99.9 Hu for males and 98.4 Hu for females, while in HuRC are 120.1 Hu for males and 111.8 Hu for females. The kidney was effectively segmented by our AI-based 3D nnU-Net model for enhanced renal CT images. In terms of mild kidney injury, the CT values exhibited higher sensitivity compared to kidney volume. The correlation analysis revealed a stronger association between VRC, HuRP, and HuRC with renal function, while the association between VRP and HuRM was weaker, and the association between VRM was the weakest. Particularly, HuRP and HuRC demonstrated significant potential in predicting renal function. For diagnosing CKD, it is recommended to set the threshold values as follows: HuRP < 99.9 Hu and HuRC < 120.1 Hu in males, and HuRP < 98.4 Hu and HuRC < 111.8 Hu in females.

Blood-brain barrier biomarkers

The blood-brain barrier (BBB) is a dynamic interface that regulates the exchange of molecules and cells between the brain parenchyma and the peripheral blood. The BBB is mainly composed of endothelial cells, astrocytes and pericytes. The integrity of this structure is essential for maintaining brain and spinal cord homeostasis and protection from injury or disease. However, in various neurological disorders, such as traumatic brain injury, Alzheimer's disease, and multiple sclerosis, the BBB can become compromised thus allowing passage of molecules and cells in and out of the central nervous system parenchyma. These agents, however, can serve as biomarkers of BBB permeability and neuronal damage, and provide valuable information for diagnosis, prognosis and treatment. Herein, we provide an overview of the BBB and changes due to aging, and summarize current knowledge on biomarkers of BBB disruption and neurodegeneration, including permeability, cellular, molecular and imaging biomarkers. We also discuss the challenges and opportunities for developing a biomarker toolkit that can reliably assess the BBB in physiologic and pathophysiologic states.

Recent advances in medical image processing for the evaluation of chronic kidney disease

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.

Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts

Dynamic contrast-enhanced (DCE) MRI monitors the transit of contrast agents, typically gadolinium chelates, through the intrarenal regions, the renal cortex, the medulla, and the collecting system. In this way, DCE-MRI reveals the renal uptake and excretion of the contrast agent. An optimal DCE-MRI acquisition protocol involves finding a good compromise between whole-kidney coverage (i.e., 3D imaging), spatial and temporal resolution, and contrast resolution. By analyzing the enhancement of the renal tissues as a function of time, one can determine indirect measures of clinically important single-kidney parameters as the renal blood flow, glomerular filtration rate, and intrarenal blood volumes. Gadolinium-containing contrast agents may be nephrotoxic in patients suffering from severe renal dysfunction, but otherwise DCE-MRI is clearly useful for diagnosis of renal functions and for assessing treatment response and posttransplant rejection.Here we introduce the concept of renal DCE-MRI, describe the existing methods, and provide an overview of preclinical DCE-MRI applications to illustrate the utility of this technique to measure renal perfusion and glomerular filtration rate in animal models.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction is complemented by two separate publications describing the experimental procedure and data analysis.

Renal perfusion imaging by MRI

Renal perfusion can be quantitatively assessed by multiple magnetic resonance imaging (MRI) methods, including dynamic contrast enhanced (DCE), arterial spin labeling (ASL), and diffusion-weighted imaging with intravoxel incoherent motion (IVIM) analysis. In this review we summarize the advances in the field of renal-perfusion MRI over the past 5 years. The review starts with a brief introduction of relevant MRI methods, followed by a discussion of recent technical developments. In the main section of the review, we examine the clinical and preclinical applications for three disease populations: chronic kidney disease, renal transplant, and renal tumors. The DCE method has been routinely used for assessing renal tumors but not other renal diseases. As a noncontrast alternative, ASL was extensively explored in both preclinical and clinical applications and showed much promise. Protocol standardization for the methods is desperately needed, and then large-scale clinical trials for the methods can be initiated prior to their broad clinical use. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:369-379.

Quantifying blood-brain barrier leakage in small vessel disease: Review and consensus recommendations

Cerebral small vessel disease (cSVD) comprises pathological processes of the small vessels in the brain that may manifest clinically as stroke, cognitive impairment, dementia, or gait disturbance. It is generally accepted that endothelial dysfunction, including blood-brain barrier (BBB) failure, is pivotal in the pathophysiology. Recent years have seen increasing use of imaging, primarily dynamic contrast-enhanced magnetic resonance imaging, to assess BBB leakage, but there is considerable variability in the approaches and findings reported in the literature. Although dynamic contrast-enhanced magnetic resonance imaging is well established, challenges emerge in cSVD because of the subtle nature of BBB impairment. The purpose of this work, authored by members of the HARNESS Initiative, is to provide an in-depth review and position statement on magnetic resonance imaging measurement of subtle BBB leakage in clinical research studies, with aspects requiring further research identified. We further aim to provide information and consensus recommendations for new investigators wishing to study BBB failure in cSVD and dementia.

Renal Functional MRI and Its Application

Renal function varies according to the nature and stage of diseases. Renal functional magnetic resonance imaging (fMRI), a technique considered superior to the most common method used to estimate the glomerular filtration rate, allows for noninvasive, accurate measurements of renal structures and functions in both animals and humans. It has become increasingly prevalent in research and clinical applications. In recent years, renal fMRI has developed rapidly with progress in MRI hardware and emerging postprocessing algorithms. Function-related imaging markers can be acquired via renal fMRI, encompassing water molecular diffusion, perfusion, and oxygenation. This review focuses on the progression and challenges of the main renal fMRI methods, including dynamic contrast-enhanced MRI, blood oxygen level-dependent MRI, diffusion-weighted imaging, diffusion tensor imaging, arterial spin labeling, fat fraction imaging, and their recent clinical applications.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:863-881.

Dynamic contrast-enhanced magnetic resonance imaging assessment of kidney function and renal masses: single slice versus whole organ/tumor

OBJECTIVES

The aim of this study was to compare single-slice and 3-dimensional (3D) analysis for magnetic resonance renography (plasma flow [FP], plasma volume [VP], and glomerular filtration rate [GFR]) and for dynamic contrast-enhanced magnetic resonance imaging (MRI) of renal tumors (FP, VP, permeability-surface area product), respectively.

MATERIAL AND METHODS

We prospectively included 22 patients (43 kidneys with 22 suspicious renal lesions) and performed preoperative and postoperative imaging before and after partial nephrectomy, respectively. Of the 22 renal lesions, 15 turned out to be renal cell carcinoma and were included in the tumor analysis, altogether leading to 86 renal and 15 tumor MRI scans, respectively. Dynamic contrast-enhanced MRI was performed with a time-resolved angiography with stochastic trajectories sequence (spatial resolution, 2.6 × 2.6 × 2.6 mm3; temporal resolution, 2.5 seconds) at 3 T (Magnetom Verio; Siemens Healthcare Sector) after injection of 0.05 mmol/kg body weight Gadobutrol (Bayer Healthcare Pharmaceuticals). Analysis was performed using regions of interest encompassing a single central slice and the whole kidney/tumor, respectively. A 2-compartment model yielding FP, VP, GFR, or tumor permeability-surface area product was used for kinetic modelling. Modelling was performed based on relative contrast enhancement to account for coil-related inhomogeneity. Significance in difference, agreement, and goodness of fit of the data to the curve was assessed with paired t tests, Bland-Altman plots, and χ2 test, respectively.

RESULTS

Bland-Altman analysis revealed a good agreement between both types of measurement for kidneys and tumors, respectively. Results between single-slice and whole-kidney regions of interest showed significant differences for Fp (single slice, 256.1 ± 104.1 mL/100 mL/min; whole kidney, 217.2 ± 92.5 mL/100 mL/min; P < 0.01). Regarding VP and GFR, no significant differences were observed. The χ2 test showed a significantly better goodness of fit of the data to the curve for whole kidneys (0.30% ± 0.18%) than for single slices (0.43% ± 0.26%) (P < 0.01). In contrast to renal assessment, tumor analysis showed no significant differences regarding functional parameters and χ test, respectively.

CONCLUSION

In dynamic contrast-enhanced MRI of the kidney, both 3D whole-organ/tumor and single-slice analyses provide roughly comparable values in functional analysis. However, 3D assessment is considerably more precise and should be preferred if available.

Gadofosveset-based biomarker of tissue albumin concentration: Technical validation in vitro and feasibility in vivo

PURPOSE

There is currently no adequate method of mapping physiologic and pathophysiologic tissue albumin concentrations in human subjects. The objective of this study was to devise and evaluate a biomarker of regional albumin concentration using gadofosveset-enhanced MRI.

THEORY AND METHODS

A binding and relaxation model was devised and evaluated in vitro in solutions of albumin at 3.0 Tesla (T) and 4.7T. The method was evaluated in the heart in seven volunteers at 3.0T.

RESULTS

MRI-derived estimates of albumin concentration were in good agreement with true values over the range 0.1-1.0 mM (Pearson correlation coefficients of 0.85 and 0.88 for 3.0T and 4.7T, respectively). The mean calculated albumin concentration in the myocardium for the volunteers was 0.02 mM (range, 0.01-0.03 mM).

CONCLUSION

Accurate estimates of albumin concentration in vitro suggest this may be a viable noninvasive alternative to existing techniques. In the myocardium the MRI-derived estimates of albumin concentration indicate the practical feasibility of the technique but were below expected values. Gadofosveset-enhanced MR relaxometry has potential in providing biomarkers of regional albumin concentration; further evaluation is required before it can be used reliably in vivo.

X-ray phase-contrast tomography of renal ischemia-reperfusion damage

Purpose

The aim of the study was to investigate microstructural changes occurring in unilateral renal ischemia-reperfusion injury in a murine animal model using synchrotron radiation.

Material and Methods

The effects of renal ischemia-reperfusion were investigated in a murine animal model of unilateral ischemia. Kidney samples were harvested on day 18. Grating-Based Phase-Contrast Imaging (GB-PCI) of the paraffin-embedded kidney samples was performed at a Synchrotron Radiation Facility (beam energy of 19 keV). To obtain phase information, a two-grating Talbot interferometer was used applying the phase stepping technique. The imaging system provided an effective pixel size of 7.5 µm. The resulting attenuation and differential phase projections were tomographically reconstructed using filtered back-projection. Semi-automated segmentation and volumetry and correlation to histopathology were performed.

Results

GB-PCI provided good discrimination of the cortex, outer and inner medulla in non-ischemic control kidneys. Post-ischemic kidneys showed a reduced compartmental differentiation, particularly of the outer stripe of the outer medulla, which could not be differentiated from the inner stripe. Compared to the contralateral kidney, after ischemia a volume loss was detected, while the inner medulla mainly retained its volume (ratio 0.94). Post-ischemic kidneys exhibited severe tissue damage as evidenced by tubular atrophy and dilatation, moderate inflammatory infiltration, loss of brush borders and tubular protein cylinders.

Conclusion

In conclusion GB-PCI with synchrotron radiation allows for non-destructive microstructural assessment of parenchymal kidney disease and vessel architecture. If translation to lab-based approaches generates sufficient density resolution, and with a time-optimized image analysis protocol, GB-PCI may ultimately serve as a non-invasive, non-enhanced alternative for imaging of pathological changes of the kidney.

Combined diffusion-weighted, blood oxygen level-dependent, and dynamic contrast-enhanced MRI for characterization and differentiation of renal cell carcinoma

PURPOSE

To investigate a multiparametric magnetic resonance imaging (MRI) approach comprising diffusion-weighted imaging (DWI), blood oxygen-dependent (BOLD), and dynamic contrast-enhanced (DCE) MRI for characterization and differentiation of primary renal cell carcinoma (RCC).

MATERIAL AND METHODS

Fourteen patients with clear-cell carcinoma and four patients with papillary RCC were examined with DWI, BOLD MRI, and DCE MRI at 1.5T. The apparent diffusion coefficient (ADC) was calculated with a monoexponential decay. The spin-dephasing rate R2* was derived from parametric R2* maps. DCE-MRI was analyzed using a two-compartment exchange model allowing separation of perfusion (plasma flow [FP] and plasma volume [VP]), permeability (permeability surface area product [PS]), and extravascular extracellular volume (VE). Statistical analysis was performed with Wilcoxon signed-rank test, Pearson's correlation coefficient, and receiver operating characteristic curve analysis.

RESULTS

Clear-cell RCC showed higher ADC and lower R2* compared to papillary subtypes, but differences were not significant. FP of clear-cell subtypes was significantly higher than in papillary RCC. Perfusion parameters showed moderate but significant inverse correlation with R2*. VE showed moderate inverse correlation with ADC. Fp and Vp showed best sensitivity for histological differentiation.

CONCLUSION

Multiparametric MRI comprising DWI, BOLD, and DCE MRI is feasible for assessment of primary RCC. BOLD moderately correlates to DCE MRI-derived perfusion. ADC shows moderate correlation to the extracellular volume, but does not correlate to tumor oxygenation or perfusion. In this preliminary study DCE-MRI appeared superior to BOLD and DWI for histological differentiation.

The role of functional imaging in the era of targeted therapy of renal cell carcinoma

Antiangiogenic therapies interacting with tumor-specific pathways have been established for targeted therapy of renal cell carcinoma (RCC). However, evaluation of tumor response based on morphologic tumor diameter measurements has limitations, as tumor shrinkage may lag behind pathophysiological response. Functional imaging techniques such as dynamic contrast-enhanced (DCE) ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI), unenhanced diffusion-weighted MRI (DW-MRI), and also metabolic imaging with positron emission tomography (PET) have the ability to assess physiological parameters and to predict and monitor therapy response. Assessment of changes in vascularity, cellularity, oxygenation, and glucose uptake with functional imaging during targeted therapy may correlate with progression-free survival and can predict tumor response or progression. In this review, we explore the potential of functional imaging techniques for assessing the effects of targeted therapy of RCC and as well review the reproducibility and limitations.