Qin Chen1, JingGang Zhang1, WeiQiang Dou2, Jie Chen1, and Wei Xing1
1The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China, 2GE Healthcare, MR Research, Beijing, China
Synopsis
Ischemia
reperfusion injury (IRI) is the main factor that delays the recovery of the renal
function and leads to the failure of treatment. To our knowledge, R2’ mapping
as a quantitative MRI method may be helpful for assessment of IRI in the early
stage. This study compared the R2’ values of outer medulla at different time
points in a rabbit model and found significant differences between IRI group and control group. Meanwhile,
significant correlation was also found between R2’ values and histopathological features. R2’ mapping can evaluate the dynamic changes of the
outer medulla longitudinally.
Introduction
Regulating the duration of renal ischemia is the key
point of treatment for ischemia reperfusion injury (IRI) 1,which can effectively protect the organs and
improve the success rate. Hypoxia plays an important role in the processing of IRI.
R2’ mapping has been reported as an effective method for detecting the changes
of oxygen in vivo, which can accurately reflect the deoxyhemoglobin than Blood
oxygen level-dependent (BOLD) imaging.
Up to date, few studies
about R2’ mapping in the IRI of kidney are reported previously 2, 3. Therefore, the purpose of this study is to explore the feasibility of R2’ mapping in evaluating the dynamic changes
of IRI in early stage. R2’ values were compared among the five time points,
after then the correlation between R2’ values and pathological characteristics was
further analyzed.Materials and Methods
Animals
52 female New Zealand rabbits were randomly divided into two groups: the control (n = 5) group and IRI (n = 47)
group, which were randomly divided into 6 subgroups according to five time
points: pre-IRI (n = 8), IRI-1 (n = 8), IRI-12 (n = 8), IRI-24 (n = 8), IRI-48
(n = 8), and IRI-L (n = 7). The rabbits in the pre-IRI, IRI-1, IRI-12, IRI-24,
and IRI-48 groups underwent MRI only at the specific time point to which they
were randomized, while the rabbits in the IRI-L group and control group
underwent longitudinal MRI across the five time points. The rabbits of the IRI groups underwent the left laparotomy, and the
left renal artery was clamped for 60 minutes, followed by release of the clamp
for reperfusion.
MRI experiments
All experiments were performed at 3.0-T MR scanner
(Discovery silent, GE Healthcare, US)
with sixteen-channel phase array flexible coil. The MRI sequences and parameters were as
follows: (1) Axial T2WI: repetition time (TR)/echo time (TE) =
1811/85 ms, slice thickness (SL) = 4 mm, gap = 1.0 mm, field of view (FOV) = 14
mm×14 mm, matrix size = 256×224, bandwidth (BW) = 31.25 Hz per/pixel, and
scanning time= 112 s; (2) Axial T2 mapping: TR = 962
ms, TE = 5.8-49.5 ms, 4 ms interval, a total of 8 echoes, SL = 4 mm, gap = 1.0
mm, FOV = 14mm×14 mm, matrix size = 128×128, BW = 35.7 Hz per/pixel, and
scanning time= 241 s; (3) Axial T2* mapping: TR = 700
ms; TE 3.9-27.1 ms, 4 ms interval, a total of 8 echoes, SL = 4 mm; gap =
1.0 mm; FOV= 14 cm×14 cm, matrix size = 128×128; BW = 31.25 Hz /pixel, and
scanning time = 104 s.
Data analysis
All data were analyzed using vendor-provided
mono-exponential model for T2 and T2* evaluation software at GE workstation. R2’ maps were generated by using the following
equation: 1/T2’ = 1/T2* - 1/T2, R2’ = 1/T2’ 4, 5. Using the T2-weighted image as reference, the
region of interest (ROI) was manually delineated along the margin of the outer
medulla on the R2’ map, which avoided bleeding, artifacts and the junction of
each zone as possible (Figure 1). Previous studies 6-8 have shown that the effect of ischemia-reperfusion on the outer
medulla was the most obvious. Therefore, we only focused on the outer medulla.
Statistical analysis
All statistical analyses
were performed in SPSS version 22. The R2’ values were measured and evaluated
with repeated measurement analysis of variance. The
Spearman correlation coefficient (ρ) was calculated to assess the correlation
between R2’ values with histopathological features. P < 0.05 was
considered threshold of statistical significance.Results
Representative
histopathological and MR images for five time points are shown in Figure 2. The R2’ values in the pre-IRI,
IRI-1, IRI-12, IRI-24, and IRI-48 groups were 30.60 ± 8.71, 18.35 ± 5.04, 30.16 ± 2.66, 17.81 ± 4.99, and 21.83 ± 2.48 (respectively, P = 0.001). The R2’ values in IRI-L group at 1, 12, 24, and 48
hours after IRI were significantly lower than those in control group (all P < 0.05;
Figure 3). In IRI-L
group, the R2’ values decreased and reached the bottom
at 1 hour after IRI, which increased from 1 hour to 12 hours after IRI, then fell
down from 12 hours to 24 hours and 48 hours after IRI. The score of tubular
epithelial edema decreased gradually with the development of ischemia
reperfusion injury, while tubular epithelial necrosis, interstitial
inflammation and cast increased. In addition, the R2’ values were negatively
correlated with tubular epithelial edema (ρ = - 0.568, P = 0.001;
Figure 3). Discussion
In this study, we mainly demonstrated
that the R2’ value in IRI
group decreased after IRI and reached the lowest point at 1 hour after
modeling, and then increased. Pathological results showed that the renal
tubular epithelial edema was most obvious at 1 hour after IRI, and then gradually alleviated. The result is consistent
with the previous study 8, 9.
This study indicated that the
R2’ value of the renal outer medulla was negatively correlated with tubular
epithelial edema.Conclusion
R2’ mapping can quantitatively evaluate the dynamic
changes of the outer medulla of renal after IRI.Acknowledgements
No acknowledgement found.References
[1] Woo D C, Kim N, Lee D W, et al. Assessing
Renal Ischemia/Reperfusion Injury in Mice Using Time-Dependent BOLD and DTI at
9.4 T. Applied Magnetic Resonance, 2015; 46(6): 709-722.
[2] Seiler A, Deichmann R, Pfeilschifter W, et al.
T2’-Imaging to Assess Cerebral Oxygen Extraction Fraction in Carotid Occlusive
Disease: Influence of Cerebral Autoregulation and Cerebral Blood Volume.
PLoS One, 2016;11(8): e0161408.
[3] Wagner M, Magerkurth J, Volz S, et al. T2′‐ and PASL‐based perfusion mapping at 3 Tesla: influence of
oxygen‐ventilation on cerebral autoregulation. J Magn
Reson Imaging, 2012; 36(6): 1347-1352.
[4] Ghassaban K, Liu S, Jiang C, et al. Quantifying
iron content in magnetic resonance imaging. Neuroimage, 2019; 187(77-92).
[5] Wang Y, Zhang R, Zhang B, et al. Simultaneous
R2, R2' and R2* measurement of skeletal muscle in a rabbit model of unilateral
artery embolization. Magn Reson Imaging, 2019; 61(149-157).
[6] Bonventre J V, Yang L. Cellular pathophysiology
of ischemic acute kidney injury. J Clin Invest, 2011; 121(11): 4210-4221.
[7] Munshi R, Hsu C, Himmelfarb J. Advances in
understanding ischemic acute kidney injury. BMC Med, 2011; 9(1): 11.
[8] Pan L, Chen J, Xing W, et al. Magnetic
resonance imaging evaluation of renal ischaemia-reperfusion injury in a rabbit
model. Exp Physiol, 2017; 102(8): 1000-1006.
[9] Pan L, Chen J, Zha T, et al. Evaluation of
renal ischemia-reperfusion injury by magnetic resonance imaging texture
analysis: An experimental study. Magn Reson Med, 2021; 85(1): 346-356.