Capability of arterial spin labeling combined with morphological characteristics to distinguish early- and mid-stage chronic kidney disease: a prospective study on 5 T MRI

Introduction

Chronic kidney disease (CKD), as a major global public health issue, requires optimized early diagnosis and management strategies to critically improve patient outcomes (1-3). Early interventions can significantly delay progression to end-stage renal disease and reduce complications such as cardiovascular events (4,5). The Kidney Disease: Improving Global Outcomes (KDIGO) 2024 Clinical Practice Guideline emphasizes that early-stage CKD (G1–G2 stages) should focus on identifying primary etiologies and preserving renal function, whereas mid-stage CKD (G3A–3B stages) prioritizes slowing the decline rate of estimated glomerular filtration rate (eGFR) and managing complications (6). Previous research demonstrated that the standardized application of guideline-recommended therapies (e.g., renin–angiotensin system inhibitors) in mid-stage CKD patients significantly improves annual eGFR decline rates during a two-year follow-up period (7). This further underscores the clinical value of early screening and timely intervention.

Current CKD diagnosis primarily relies on serum creatinine (Scr), urinary albumin-to-creatinine ratio (UACR), and eGFR, supplemented by renal biopsy histopathological assessment. However, Scr levels are significantly influenced by age, sex, and muscle mass, limiting its sensitivity in detecting early renal impairment (6). Although renal biopsy remains the gold standard, its invasive nature introduces risks such as bleeding and infection, and it is unsuitable for dynamic monitoring (8). Consequently, exploring non-invasive renal function assessment methods with high sensitivity represents a pressing clinical need.

Magnetic resonance imaging (MRI) demonstrates unique advantages in assessing CKD morphology and function through its multi-parametric, multi-sequence imaging capabilities (9-11). Currently, the clinical assessment of renal morphology primarily relies on ultrasound, which is susceptible to interference from intestinal gas and surrounding tissues and has strong operator dependence, thus compromising the reproducibility of morphological evaluations (12). In contrast, MRI enables three-dimensional (3D) visualization of renal anatomy with its multi-parametric and multi-planar imaging capabilities. Particularly at 5 T MRI, higher spatial resolution allows for precise identification of subtle structural alterations in the renal parenchyma and heterogeneity in blood flow perfusion (13,14). Pathological studies have confirmed that CKD progression involves glomerulosclerosis, interstitial fibrosis, and microvascular rarefaction, radiologically manifested as renal volume reduction, cortical thinning, and blurring of corticomedullary differentiation (CMD). Francis et al. (9) and Noda et al. (15) have established that renal volume and cortical thickness serve as independent imaging predictors of disease progression. Arterial spin labeling (ASL) technology non-invasively evaluates renal perfusion by quantifying renal blood flow (RBF) (16,17). Multiple studies have confirmed that RBF is significantly lower in CKD patients than in healthy individuals and is significantly positively correlated with eGFR (18,19). Using a 1.5 T scanner, Rossi et al. demonstrated that RBF was significantly higher in healthy volunteers compared to CKD patients (20). Mao et al., utilizing a 3 T system, further demonstrated a significant positive correlation between RBF and eGFR in CKD patients (21).

Notably, while existing studies predominantly utilize 3 T or 1.5 T MRI, the clinical application of 5 T MRI has enhanced the image quality in renal imaging, demonstrating anatomical and functional abdominal image quality comparable to or better than 3 T (13,14). Its superior spatial resolution enables detailed visualization of subtle renal tissue structures, while also significantly improving the signal-to-noise ratio (SNR) and quantitative reproducibility of ASL imaging. However, to our knowledge, no previous study has specifically focused on the application of 5 T MRI for CKD staging. This study innovatively integrates 5 T ASL perfusion imaging with morphological characteristics. It validates the clinical feasibility of ASL technology at 5 T and then applies it to systematically evaluate the ability of a morpho-functional multiparametric model to distinguish early-stage (G1–G2) CKD from mid-stage (G3A–G3B) CKD. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0296/rc).

Methods

Participants

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Chongqing University Three Gorges Hospital [No. 2024-KS-(149)]. Written informed consent was provided by all participants prior to enrollment. CKD was defined as the presence of kidney damage (blood or urine composition abnormalities, kidney biopsy findings, radiographic abnormalities) or an eGFR lower than 60 mL/min/1.73 m², persisting for 3 or more months. From March to December 2024, 103 patients meeting this definition (69 males and 34 females; mean age, 47.29±15.53 years) were prospectively enrolled at Chongqing University Three Gorges Hospital. The study flowchart is shown in Figure 1. The inclusion criteria were as follows: (I) confirmed CKD diagnosis per KDIGO 2024 guidelines (6) with eGFR ≥30 mL/min/1.73 m²; (II) valid SCr and UACR measurements within 7 days before enrollment; (III) no contraindications for MRI examination; and (IV) good compliance with the examination process. The exclusion criteria were as follows: (I) significant kidney asymmetry (>10% inter-kidney parameter discrepancy); (II) renal neoplasms or cysts (>10 mm diameter); (III) history of renal replacement therapy (dialysis/transplantation); (IV) congenital renal anomalies; (V) decompensated heart failure; and (VI) suboptimal imaging quality for quantitative analysis.

Figure 1 Flow chart of participants. CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; KDIGO, Kidney Disease: Improving Global Outcomes.

The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to calculate eGFR. The participants were categorized into two groups according to KDIGO 2024 staging criteria (6): the early-stage group (G1–G2) with an eGFR of 60–120 mL/min/1.73 m², and the mid-stage group (G3A–G3B) with an eGFR of 30–59 mL/min/1.73 m². Baseline demographic and laboratory parameters are summarized in Table 1.

MRI protocols

MRI examinations were performed using a 5 Tesla (5 T) MRI system (uMR Jupiter, United Imaging Healthcare, Shanghai, China). An 8-channel parallel volumetric transmit coil was used to mitigate dielectric artifacts through independent channel control. A custom-built 24-channel body coil was used for all studies at 5 T using local B1 ± shimming for B1 ± optimization. All patients were required to discontinue medications that may affect kidney hemodynamics, such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), for at least 12 hours prior to the examination, and to abstain from food and water for at least 6 hours. Coronal breath-hold T1-weighted imaging (T1WI) inversion recovery-fast spoiled gradient echo (IR-GRE-FSP) sequence, sagittal breath-hold T2-weighted imaging (T2WI) single-shot fast spin echo (SSFSE) sequence, and transversal free-breathing T2WI fast spin echo (FSE) sequence with fat suppression were employed to exclude organic renal lesions and to evaluate morphological characteristics. Kidney perfusion was measured using a free-breathing, two-dimensional (2D) echo-planar sequence with pulsed ASL and flow-sensitive alternating inversion recovery (PASL_FAIR) scheme. Retrospective registration of the image volumes was performed prior to averaging to reduce the influence of breathing motion on the perfusion imaging. All the relevant scan parameters are summarized in Table 2.

Image analysis

All image analyses were conducted by two radiologists (J.K., with over 20-years’ experience of abdominal MRI; and W.Z., with more than 30-years’ experience of abdominal MRI) blindly and independently.

Renal morphological assessment

Quantitative image analysis

(I) 3D renal dimensions: according to ultrasonography standards (22,23), renal length was measured at the hilum level on T2WI sagittal images (Figure 2A). Thickness and width were assessed at the maximum transverse section and hilum level on T2WI axial images (Figure 2B,2C). All other morphological evaluations were performed on T1WI coronal images, except for the 3D renal dimensions. (II) Cortical thickness: three-point measurements were taken at the upper, middle, and lower poles of the kidney at the hilum level, and the average cortical thickness was calculated (Figure 2D). (III) Cortical signal heterogeneity: large contiguous regions of interest (ROIs) (110–250 pixels) were manually delineated along the cortical margin at the hilum level, avoiding renal sinus and vascular structures (Figure 2E). Heterogeneity index was calculated as: Heterogeneity=(SImax−SImin)/2∗SImean.

Figure 2 Imaging characteristics measurement. Renal length (A), renal thickness (B), renal width (C), cortical thickness (D), cortical signal heterogeneity (E), CMD contrast [good (F), moderate (G)], RBF values (H). CMD, corticomedullary differentiation; RBF, renal blood flow.

Qualitative image analysis

Given the preserved visibility of CMD in early-to-mid stage CKD patients on T1WI, CMD contrast was categorized into two types: good (CMD is maintained and clearly visible in all segments of the kidney); moderate (CMD is present but not distinct in some parts of the kidney) (24) (Figure 2F,2G).

ASL processing and assessment

Raw Digital Imaging and Communications in Medicine (DICOM) datasets were processed using Matlab R2018b (MathWorks, Natick, MA, USA). The post-processing pipeline consisted of the following steps: (I) initial contour delineation: the user manually outlined the left and right kidney boundaries on an averaged display of all 20 control and label images to define a baseline anatomical reference. (II) Rigid motion correction: All individual control and label images were co-registered to this reference mask using an automated rigid-body registration algorithm to correct for respiratory motion. (III) Iterative mask validation: a quality control loop allowed the user to inspect the kidney contour overlaid on each registered image; if respiratory motion caused the kidney to fall outside the mask, the user corrected the mask for the affected image indices, iterating until all 20 masks were confirmed as anatomically accurate. (IV) Signal averaging: the mean signal intensity within each kidney ROI was calculated for the control, label, and proton density (PD)-weighted images (M0) by averaging across the 20 repetitions to enhance the SNR. (V) RBF calculation and export: The mean label, control, and M0 images obtained from the scan were used to calculate RBF voxel-wise using the following formula, yielding a color-coded RBF parametric map (25):

RBF=6000⋅λ⋅(SIcontrol−SIlabel)⋅exp(TIT1,blood)2⋅α⋅TI1⋅SIPD[1]

Based on previous studies and ASL guidelines, the tissue–blood partition coefficient (λ) was set at 0.9 mL/100 g, the labelling efficiency (α) was 0.95, and SI with the respective subscripts represents the signal intensities of the control, label, and PD images. The magnetic resonance (MR) longitudinal relaxation time of arterial blood (T_1,blood) was set at 1.65 s. Inflowing suppression of arterial blood and background suppression were both performed. The post-labeling delay (TI) and the bolus duration (TI_{_1}) were both set at 1,800 ms.

Two ROIs (each 110–250 pixels) covering the entire renal cortex for each kidney were manually drawn on the RBF maps, while avoiding areas with cysts, artifacts, the collecting system, or vascular structures (Figure 2H). The MRI parameter values from both kidneys were averaged for subsequent statistical analysis, as CKD typically affects both kidneys without bias and eGFR reflects the overall function of both kidneys.

Statistical analysis

Statistical analysis was performed using the software SPSS 26.0 (IBM Corp., Armonk, NY, USA) and MedCalc version 23.1 (MedCalc, Ostend, Belgium). Continuous variables were first assessed for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean ± standard deviation (SD) and compared between the two groups using independent samples t-test. Non-normally distributed data were presented as median with interquartile range (IQR) [median (Q1, Q3)] and analyzed with Mann-Whitney U test. Categorical variables were presented as frequencies (%) and compared via Pearson’s χ² test. Positive imaging indicators demonstrating significant intergroup differences (P<0.05) were included in a multivariate logistic regression analysis to identify independent diagnostic markers for CKD. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance of individual characteristics and combined RBF-morphology model, with the area under the curve (AUC) calculated for each. The DeLong test was applied to compare AUC differences among individual characteristics and combined models. Spearman correlation analysis was used to evaluate the correlations between eGFR and imaging characteristics. Intraclass correlation coefficient (ICC) and Cohen’s Kappa were used to assess the interobserver agreement between the two radiologists (a coefficient <0.60 indicated poor agreement, 0.61–0.80 indicated moderate agreement, and >0.80 indicated excellent agreement). A two-tailed P value <0.05 was considered statistically significant.

Results

Patient characteristics

Table 1 summarizes the clinical characteristics of the enrolled CKD patients. A total of 103 patients were included, with 66 (64.08%) classified as early-stage CKD and 37 (35.92%) as mid-stage CKD. The etiologies were as follows: glomerulonephritis in 45 patients, diabetic kidney disease in 7, hypertensive nephropathy in 13, other causes in 15, and unknown etiology in 23. Significant differences were observed between the two groups in SCr and eGFR (both P<0.001), whereas UACR did not differ significantly (P=0.60).

Interobserver agreement of imaging characteristics

Quantitative measurements demonstrated excellent interobserver agreement (Table 3): renal length [ICC =0.938, 95% confidence interval (CI): 0.874–0.970], width (ICC =0.915, 95% CI: 0.829–0.959), thickness (ICC =0.906, 95% CI: 0.813–0.954), cortical thickness (ICC =0.931, 95% CI: 0.862–0.967), cortical signal heterogeneity (ICC =0.893, 95% CI: 0.788–0.948), and RBF values (ICC =0.905, 95% CI: 0.812–0.954). Qualitative assessment of CMD contrast also showed excellent agreement (Kappa =0.870).

Comparison of imaging characteristics between early- and mid-stage CKD groups

The renal length, width, cortical thickness, cortical signal heterogeneity, CMD contrast, and RBF values showed statistically significant differences between groups. Table 4 shows that renal length decreased significantly in mid-stage CKD (t=2.62, P<0.001), with concomitant reductions in renal width (Z=−2.61, P<0.001) and cortical thickness (Z=−3.69, P<0.001). Cortical signal heterogeneity increased significantly (Z=−4.55, P<0.001), with significant degradation of CMD contrast (χ²=25.97, P<0.001). RBF values were markedly lower in mid-stage patients (Z=−5.07, P<0.001).

Multivariate logistic regression analysis identified the independent predictors for the diagnosis of early-stage CKD

The results of the multivariate logistic regression analysis are shown in Table 5. Renal length (B=−0.133, P=0.004), CMD contrast (B=2.351, P=0.010), cortical signal heterogeneity (B=0.093, P=0.022), and RBF values (B=−0.038, P=0.002) were identified as independent predictors for distinguishing between early and mid-stage CKD.

Correlations between eGFR and the independent predictors

A positive correlation was observed between renal length (r=0.191, P=0.054), CMD contrast (r=0.484, P<0.001), and RBF values (r=0.519, P<0.001), whereas eGFR was significantly negatively correlated with cortical signal heterogeneity (r=−0.339, P<0.001).

Diagnostic efficacy for early-stage CKD

The results of the ROC curve analysis are presented in Table 6. The AUC values for renal length, CMD contrast, cortical signal heterogeneity, RBF values, and the combined model were 0.629 (95% CI: 0.528–0.722), 0.707 (95% CI: 0.609–0.793), 0.771 (95% CI: 0.678–0.848), 0.802 (95% CI: 0.712–0.874), and 0.916 (95% CI: 0.844–0.961), respectively. Compared to renal length (Z=4.828, P<0.001), cortical signal heterogeneity (Z=3.270, P=0.001), CMD contrast (Z=5.202, P<0.001), and RBF values alone (Z=2.778, P=0.005), the combined RBF-morphology model demonstrated the highest diagnostic efficacy, with an AUC of 0.916 (95% CI: 0.844–0.961), a specificity of 87.88%, and a sensitivity of 81.08% at the optimal cut-off value of 44.0% (Figure 3).

Figure 3 ROC curve analyses were conducted to evaluate the diagnostic performance of individual characteristics and combined RBF-morphology models. CMD, corticomedullary differentiation; RBF, renal blood flow; ROC, receiver operating characteristic.

Discussion

This study utilized 5 T MRI to achieve imaging of subtle renal tissue structures and combined it with ASL perfusion imaging to differentiate early- from mid-stage CKD from both morphological and functional perspectives. The results demonstrate that renal length, CMD contrast, cortical signal heterogeneity, and RBF values are independent factors for differentiating CKD stages. With declining eGFR, these parameters progressively decrease. The combined RBF-morphology model achieved an AUC of 0.916 for differentiating early- from mid-stage CKD, significantly outperforming single-characteristic assessments (P<0.05), demonstrating superior diagnostic value. These findings suggest that perfusion and morphological biomarkers derived from 5 T MRI may serve as complementary imaging indicators for assessing renal structural and functional alterations in CKD, and could help to propel the clinical translation of 5 T MRI.

Morphological assessment

In this study, renal length decreased significantly in mid-stage CKD, with concomitant reductions in renal width and cortical thickness. During CKD progression, pathological changes including glomerulosclerosis, tubular atrophy, and interstitial fibrosis lead to progressive renal parenchymal loss, manifesting as cortical thinning and medullary fibrosis, ultimately causing a 3D reduction (9,10). Renal length, as a key morphological indicator, is commonly used to characterize kidney size (26). Beland et al. demonstrated a significant correlation between renal length and eGFR (P=0.003), suggesting its potential as a renal functional biomarker (27). At the early-stage, despite the initial structural damage, compensatory mechanisms such as glomerular hyperfiltration and tubular solute load regulation in the residual nephrons help to maintain relatively stable renal function. This compensatory effect causes morphological changes (renal length reduction) to precede functional decline (eGFR reduction).

Our study revealed that cortical signal heterogeneity increased significantly, with significant degradation of CMD contrast. Mid-stage CKD patients frequently exhibit characteristic “mosaic-like” signal heterogeneity on T1WI, attributable to spatial heterogeneity of renal injury. In early-stage diseases, fibrotic foci demonstrate prolonged T1 relaxation times (hypointensity); meanwhile, adjacent edematous areas also show prolonged T1 relaxation times (hypointensity) (19). However, normal renal cortex exhibits relatively shorter T1 values, appearing hyperintense compared to fibrotic or edematous tissue. This spatial intermingling between pathology and normal tissue results in T1WI signal heterogeneity. Inflammation-driven edema in renal impairment increases cortical free water and lengthens T1, attenuating cortical signal and CMD (15,19,28), while fibrosis further prolongs T1 and deepens CMD loss by restricting diffusion (19,29-31). Marotti et al. (29) and Kettritz et al. (12) emphasized that a reduction or complete loss of CMD contrast is a sensitive but non-specific indicator. In contrast to these findings, our study demonstrated a relatively high specificity (95.45%) for CMD contrast. This discrepancy may be attributed to the subjective assessment of CMD, introducing potential observer variability, and selection bias within the study population.

Functional assessment

ASL is a non-invasive imaging technique that provides a reliable method for quantifying RBF (32-34). Under normal physiological conditions, RBF accounts for 20–25% of cardiac output and exhibits regional distribution differences. Over 90% of blood flow is distributed to the renal cortex with its high metabolic demand, whereas only a small portion reaches the medulla (33,35,36). This anatomical distribution results in the cortex exhibiting significantly high-perfusion characteristics. During the progression of CKD, renal hemodynamic disturbances lead to progressive perfusion deficits in the peritubular capillary network and microvascular loss, ultimately resulting in a significant reduction in cortical blood flow (37,38). This study found that RBF is an independent predictor for diagnosing early- and mid-stage CKD [odds ratio (OR) =0.963, 95% CI: 0.940–0.986] and shows a significantly positive correlation with eGFR (r=0.519, P<0.001). These findings are consistent with those reported by Mao et al. (21), which were confirmed through ROC curve analysis that RBF values exhibited good discriminative ability for early renal injury (AUC =0.827, 95% CI: 0.687–0.922) and showed a moderate correlation with eGFR (r=0.586, P<0.05), suggesting a close association between the degree of renal ischemia and renal functional decompensation. Compared to traditional eGFR assessments based on Scr, ASL directly quantifies microcirculatory perfusion in the renal cortex. This approach not only overcomes the limitations of creatinine metabolism, which is influenced by muscle mass and diet, but also provides a direct visualization of the dynamic progression from compensatory hyperperfusion to decompensated hypoperfusion in nephrons (25,39).

The combined RBF-morphology model assessment

Morphological changes and RBF values in CKD can reflect the pathological progression of the disease and are closely related to eGFR. Currently, the clinical assessment of renal morphology primarily relies on ultrasound, which is susceptible to interference from intestinal gas and surrounding tissues and is strongly operator-dependent, thus compromising the reproducibility of morphological evaluations (23,26). In contrast, MRI with its multi-parametric and multi-planar imaging capabilities, enables 3D visualization of renal anatomy. Notably, 5 T MRI provides significantly superior SNR and contrast-to-noise ratio (CNR) compared to 3 T, allowing clearer delineation of fine anatomical details and more sensitive detection of subtle lesions (13,40). Consistent with this, Zheng et al. (13) demonstrated that 5 T MRI yields morphological and functional renal image quality comparable or superior to that of 3 T, with the enhanced SNR and CNR improving intra-lesional structural visualization and pathological diagnostic accuracy. Furthermore, the increase in SNR and the longer T1 relaxation time of blood at higher field strengths may improve ASL imaging, potentially allowing for a shorter scan time, higher spatial resolution, and increased sensitivity to low perfusion levels (41-43). Supporting this, Tong et al. (44) showed that, compared to 3 T, 5 T yielded a 20–50% increase in SNR and a 60–70% increase in CNR, with excellent repeatability and reproducibility of RBF values (ICC =0.855–0.973). Capitalizing on these advantages, our study employed 5 T MRI for renal morphological and ASL perfusion imaging. ROC curve analysis confirmed that the combined RBF-morphology model achieved an AUC of 0.916 (95% CI: 0.844–0.961) for distinguishing between early- and mid-stage CKD, significantly outperforming any single-characteristic assessment (P<0.05). These findings indicate that the RBF-morphology combination has good diagnostic value for differentiating early-stage from mid-stage CKD, and may serve as a complementary imaging indicator for assessing renal structural and functional alterations in CKD.

Despite providing valuable insights, this study has several limitations that warrant consideration. First, ROIs were manually delineated, which may have introduced subjective bias and affected the accuracy of the results. Future studies could employ deep learning-based automatic ROI segmentation algorithms to improve measurement reproducibility. Second, the lack of patient oxygenation level assessment limits deeper pathophysiological analysis of the hemodynamic disturbances. Third, the single-center design and small sample size may restrict the clinical applicability of the conclusions. Future research should expand the sample size and conduct multi-center studies to enhance the reliability of the findings. Fourth, CKD staging in this study was based on eGFR rather than directly measured glomerular filtration rate (GFR), which may introduce classification bias due to inherent inaccuracies of estimation equations. Future studies incorporating measured GFR could help validate the imaging biomarkers against a more definitive reference standard.

Conclusions

The combination of ASL and morphological characteristics achieves significantly better diagnostic performance between early- and mid-stage CKD than any single imaging characteristic, suggesting that 5 T MRI derived perfusion and morphological biomarkers may serve as complementary imaging indicators for assessing renal structural and functional alterations in CKD.

Acknowledgments

We would especially like to thank Dr. Zhichao Feng for his valuable suggestions on the revision of this manuscript.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0296/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0296/dss

Funding: This work was supported by the Key Medical Discipline of Chongqing Municipality (No. zdxk202116), Science-Health Joint Medical Scientific Research Project of Chongqing (No. 2026MSXM078), and the Medical Research Project jointly funded by the Science and Technology Bureau and Health Commission of Wanzhou District, Chongqing, China (No. wzwjw-kw2024015).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0296/coif). S.X. and L.Z. are currently employed by United Imaging Healthcare. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Chongqing University Three Gorges Hospital [No. 2024-KS-(149)]. Written informed consent was provided by all participants prior to enrollment.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)