Introduction

Microangiopathy is a key factor in the development of diabetic myopathy; it includes components such as capillary network and endothelial cell dysfunction [1], microvascular barrier injury, increased capillary filtration of albumin [2], and abnormal number of microvessels [3]. The associated physiological signs, including alterations in skeletal muscle structure and function, or reduced metabolic capacity, may cause weakness, increase fatigability, and have a negative impact on insulin sensitivity and processing of glycaemic and lipidemic loads [4]. Diabetic animal models also exhibit muscle wasting and reduced muscular endurance and strength [5, 6]. These effects subsequently lead to the initiation and progression of other diabetic complications. Improving microcirculation via modulation of endothelial dysfunction and induction of angiogenesis [7, 8] has been a potential therapeutic target for diabetic myopathy. Hence, non-invasive quantitative evaluation of microvascular permeability and microarchitectural changes is critical for understanding the pathophysiological mechanisms and to evaluate their curative effects on diabetic skeletal muscle lesions.

However, this non-invasive assessment of skeletal muscle has been methodologically limited. Muscle perfusion encompasses both the rate and distribution of blood flow in the tissue [9]. Arterial spin labelling (ASL), which uses blood as an internal tracer, and dynamic susceptibility contrast (DSC)-MRI, which tracks the distribution of a contrast agent bolus by measuring the effective transverse relaxation rate, are effective tools used to calculate the blood flow [10, 11]. Contrastingly, the use of dynamic contrast-enhanced MRI (DCE-MRI) with the application of an external tracer directly reveals the physiological tissue characteristics (e.g., perfusion and capillary permeability); this is based on kinetic parameters and is performed by fitting the pharmacokinetic models to the concentration curves [12]. It has shown accuracy and feasibility in evaluating the microcirculation in low-perfused healthy skeletal muscle tissue [13, 14], and in the detection of damaged microvascular structures in the skeletal muscle following ischaemia-reperfusion (I/R) injury [15]; it also provides an early and sensitive diagnosis for denervated skeletal muscles [16]. However, few studies have employed DCE-MRI to evaluate diabetic muscle permeability and perfusion [17, 18]. Volume transfer constant (Ktrans) represents the transfer rate of contrast agents from vessels to the extravascular extracellular space (EES); it provides comprehensive information on microvascular flow, capillary surface area, and permeability, and is the most commonly used parameter to evaluate microvascular permeability in oncological studies. Studies on DCE-MRI of skeletal muscle also showed effectiveness of Ktrans [15, 16, 19, 20]. Texture analysis (TA) is a potential tool to identify subtle differences in lesion distribution patterns on muscle MRI images [21,22,23]. Texture features extracted from Ktrans maps may be useful for analysing lesion perfusion patterns and intralesional heterogeneity, as well as for tumour grading [24] and differentiation [25].

To date, TA of Ktrans maps obtained from skeletal muscle images has not been reported. Therefore, we aimed to estimate the microvascular permeability and perfusion of skeletal muscle in alloxan-induced diabetic rabbits using quantitative DCE-MRI; we also explored the feasibility of using TA based on Ktrans map to evaluate subtle structural changes in skeletal muscle in the early stages of diabetes.

Materials and methods

Diabetic rabbit model

All experiments were approved by the Animal Ethics Committee of Wuhan University and the Animal Experiment Centre of Renmin Hospital of Wuhan University. Twenty-four young male Japanese big ear rabbits (weighing 2.8–3.1 kg) were adapted to an adjusted diet plan for 1 week. Fasting blood glucose levels of all rabbits were controlled without exceeding 6.0 mmol/L before preparation of the diabetes mellitus (DM) model. Subsequently, the rabbits were randomly divided into control (n = 10) and diabetic (n = 14) groups, with the latter induced with 5% alloxan monohydrate (Sigma-Aldrich Chemical) dissolved in 0.9% sterile saline, and administered intravenously to the marginal ear vein (100 mg/kg). A successful diabetic rabbit model was defined as the one with blood glucose levels ≥ 14 mmol/L (once) or ≥ 11 mmol/L (twice) at 48 h [26]. Blood glucose levels were weekly monitored in all rabbits for the subsequent 4 weeks until the condition tended toward stability; the levels were measured before MRI examination.

MRI examination

All rabbits were anaesthetised with an injection of 3% pentobarbital sodium (40 mg/kg) via the ear marginal vein [27]. This agent provided fast and deep anaesthesia with a long duration (~ 30 min). The rabbits were imaged at the lumbar level of the multifidus muscle in a 3.0-T MR scanner (Discovery MR750, GE Healthcare) with an 8-channel knee coil after anaesthesia at fixed time points (0, 4, 8, 12, and 16 weeks after verification of a successful diabetic rabbit model). The midline of the intervertebral disc at L6/7 level was selected as the scanning canter. The anatomical images were obtained with axial fast spin-echo T1-weighted sequence (repetition time [TR]/echo time [TE] = 400/9.6 ms; slice thickness = 3.0 mm; field of view [FOV]= 12 cm × 12 cm; matrix = 256 × 256; number of excitations [NEX] = 2) and fast spin-echo T2-weighted anatomic images (TR/TE = 2500/121.7 ms; slice thickness = 3.0 mm; FOV = 12 cm × 12 cm; matrix = 256 × 256; NEX = 4).

DCE-MRI was acquired with liver acquisition volume acceleration (LAVA), using the array spatial sensitivity encoding technique. Pre-contrast LAVA series with variable flip angles used for T1 mapping (TR/TE = 3.5/1.6 ms; slice thickness = 3.0 mm; slice number = 12; FOV = 20 cm × 16 cm; matrix = 256 × 256; flip angles = 9° and 12°) were performed first. Subsequently, dynamic contrast-enhanced sequences were acquired using the LAVA series (flip angle = 10°) with continuous scanning for 35 phases. The temporal resolution of each acquisition was 8 s. The contrast agent gadodiamide (Omniscan; GE Healthcare) was injected via an intravenous catheter placed in the ear marginal vein by a contrast media injector (Medrad Spectris Solaris EP Mobile Mount MR Injection System) at a dose of 0.2 mmol/kg and flow rate of 1.0 mL/s after 2-phase scan as baseline; this procedure was followed by 5 mL saline flush.

Data analysis

Primary DCE-MRI data were analysed using a vendor-offered DCE post-processing software (Omni Kinetics, GE Healthcare). First, 3D non-rigid registration of the 35-phase dynamic enhanced images was performed to reduce respiration and motion artefacts. Second, pre-contrast LAVA series with flip angles of 9° and 12° were used to calculate pixel-wise T1 maps to obtain pre-contrast tissue longitudinal relaxation time T1 (T10). This measurement would then be used to convert the signal intensity units into units of contrast agent concentration. Then, the corrected 35-phase enhanced images were imported to the software. A dual-compartment extended Tofts linear model was then selected to fit the rabbit muscle. A circular region of interest (ROI) was placed in the centre of the abdominal aorta to obtain the time-concentration curve as the arterial input function (AIF). Each ROI with an area of approximately 40 mm2 (82 voxels) was placed manually in the ipsilateral multifidus muscle on each image without involvement of subcutaneous fat, fascia, and vertebra. All the ROIs were stacked to a volume of interest. Quantitative permeability parameters including Ktrans, rate constant (Kep), extravascular extracellular space (EES) volume fraction (Ve), and plasma volume fraction (Vp) (mean value) were calculated. Texture features were extracted automatically from the Ktrans map, and the number of bins was set at 50. All relevant parameters of the contralateral multifidus muscle were obtained with the same procedure. Two radiologists with more than 5 years of experience in musculoskeletal MR imaging were blinded to the treatment groups and performed all the measurements mentioned above. The mean values of parameters acquired from both examiners were adopted for analysis.

Texture features extracted from the Ktrans map included histogram parameters (e.g., min, max, median, mean, skewness, and kurtosis) and five commonly used grey level co-occurrence matrix (GLCM) parameters (e.g., energy, entropy, correlation, inverse difference moment [IDM], and contrast). The histogram represents the distribution of pixel intensities in an image [28]. Kurtosis describes the tailedness of the distribution. Skewness measures the asymmetry of the probability distribution. Positive skewness indicates that the distribution is concentrated on the left of the figure. GLCM describes the adjacency relationship between two pixels determined by the spatial distance and direction [29]. Entropy represents the randomness and complexity of intensity values in an image. Energy and IDM reflect the uniformity or homogeneity of intensity value. Correlation measures grey-level linear-dependency between neighbouring resolution units. Contrast calculates the differences in neighbouring pixel values, which indicate the local grey-level variations.

Histological analysis

All rabbits were euthanised with intravenously injected pentobarbital sodium (100 mg/kg) after MRI examination at the 16th week. Samples of the multifidus muscle at L6/7 level of the intervertebral disc were collected, fixed in muscle-specific fixatives, embedded in paraffin, and sliced into 4-μm serial sections perpendicular to the muscle fibres. Sections were stained with haematoxylin-eosin (HE) and labelled with CD31 to measure the muscle fibre mean cross-sectional area (CSA) and capillary-to-fibre (C/F) ratio, respectively. Section images were captured at × 200 magnification using a light microscope (OLYMPUS BX51) with a digital camera; they were then analysed with Image Pro-plus 6.0 software. Four sample fields with approximately 200 muscle fibres were selected from stained sections in each muscle to obtain the CSA [30]. C/F ratio was determined on four separate artefact-free regions of the slice images and calculated as the number of capillaries divided by the number of muscle fibres in these defined areas [31].

Statistical analyses

Normal distribution data are presented as mean ± standard deviation, whereas non-normally distributed data are presented as median (25th–75th percentiles). The two-sample t test (data with a normal distribution and equal homogeneity of variance) or Mann-Whitney U test was used to evaluate differences in CSA, C/F ratio, permeability parameters, and texture parameters between the groups at the same time point. Repeated measures analysis of variance (data with normal distribution and Mauchly’s test of sphericity with Greenhouse-Geisser or Huynh-Feldt correction if necessary) or the Friedman test was applied to assess the differences in these parameters at different time points in the same group, followed by Bonferroni correction for post hoc pairwise comparisons. Pearson correlations were used to estimate the correlations between CSA and C/F ratio and permeability parameters. All statistical analyses were performed with SPSS 17.0 Windows Student Version statistics software (IBM). p< 0.05 was considered statistically significant.

Results

In the diabetic group, two animals failed to maintain a hyperglycaemic state while two others died from poor health condition. The failure and mortality rate were 14.3%, respectively. In the control group, one animal died because of an anaesthetic accident, and another died of unknown causes. These six rabbits were excluded from the experiment. The successfully induced diabetic rabbits exhibited weight loss and tissue wasting over time, as well as decreased activity and reduced flexibility.

Axial T1WI images revealed significantly reduced multifidus muscle and subcutaneous fat areas in the diabetic group at the 16th week relative to these measures in week 0. There were no obvious changes in control group over time (Fig. 1).

Fig. 1
figure 1

Axial T1WI images of the diabetic and control groups. a Subcutaneous fat and multifidus muscles are clearly visible in the diabetic group at week 0, (b) but are significantly reduced at the 16th week. There are no obvious changes in the control group between (c) week 0 and (d) the 16th week

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The control group showed no significant differences in permeability parameters over time (Table 1). In the diabetic group, Ktrans and Kep were significantly higher at the 8th week than the baseline, and they respectively decreased significantly at the 16th and 12th weeks compared to the 8th week. Additionally, Ve increased while Vp decreased from the 8th week. Apart from the baseline and at the 4th week, Ktrans, Kep, and Ve were higher, while the Vp was lower at each time point in the diabetic group than in the control group (Table 2; Figs. 2 and 3). Additionally, DCE-MRI pharmacokinetic parameter maps reflected a higher permeability and poor perfusion of muscles in the diabetic group at the 16th week (Fig. 2).

Table 1 Repeated measure of permeability and texture parameters in diabetic and control groups
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Table 2 Permeability parameters in the diabetic and control groups at each time point
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Fig. 2
figure 2

Post-contrast axial T1WI images and pharmacokinetic maps in diabetic and control groups at the 16th week. a shows post-contrast axial T1WI images of the multifidus muscle of the diabetic group. The red area indicates an ROI on the right side. b Ktrans, (c) Kep, (d) Ve, and (e) Vp maps are shown, with values of 2.096 min−1, 1.807 min−1, 0.848, and 0.133, respectively. f shows post-contrast axial T1WI images of the multifidus muscle of the control group. The red area indicates an ROI on the right side. g Ktrans, (h) Kep, (i) Ve, and (j) Vp maps are shown, with values of 1.045 min−1, 1.282 min−1, 0.381, and 0.639, respectively. A zoom of the multifidus muscle is shown in the bottom left corner of each pharmacokinetic map

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Fig. 3
figure 3

Permeability parameters of the diabetic and control groups. All parameters were significantly different between the diabetic group (solid line) and control group (dotted line) at the 8th, 12th, and 16th weeks (#p < 0.05). Ktrans and Kep were increased at the 8th week and subsequently exhibited a decreasing trend. Ve was increased whereas Vp was decreased at the 8th week, after which they were stable in the diabetic group (*p < 0.05)

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All the texture parameters in the control group were not significantly different (Table 1). In the diabetic group, the skewness began to decrease significantly at the 4th week and increased significantly at the 16th week. From the 8th week, median Ktrans, mean Ktrans, and entropy increased significantly, whereas IDM decreased. The energy also decreased while the contrast increased significantly in the diabetic group, only at the 8th week (Tables 3 and 4).

Table 3 Ktrans histogram parameters in the diabetic and control groups at each time point
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Table 4 Ktrans grey level co-occurrence matrix parameters in the diabetic and control groups at each time point
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Cross-sections of HE-stained multifidus muscles revealed muscle fibre atrophy and widened interstitial spaces between fibres. CD31-Stained muscle fibres also exhibited sparse microvessel distribution in the diabetic group at the 16th week (Fig. 4). CSA and C/F were decreased in the diabetic group (3332.823 ± 532.908 μm2, 0.580 ± 0.060) compared to those in the control group (5841.718 ± 306.501 μm2, 1.035 ± 0.072) (t = − 11.802 and − 14.650, respectively; p < 0.001) (Fig. 5). CSA was negatively correlated with Ktrans and Ve, and positively correlated with Vp (r = − 0.821, − 0.823, and 0.826, respectively, p < 0.05). Furthermore, C/F was negatively correlated with Ktrans, Kep, and Ve, and positively correlated with Vp (r = − 0.854, − 0.503, − 0.768, and 0.908, respectively; p < 0.05).

Fig. 4
figure 4

Transverse histologic sections of multifidus muscle fibres. The muscle fibres (white arrow) demonstrated atrophy in the (a) diabetic group compared to that in the (b) control group (haematoxylin-eosin stain; original magnification, ×200). Microvessels (black arrow) among the muscle fibres were sparser in the (c) diabetic group than in the (d) control group (CD31 stain; original magnification, ×200)

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Fig. 5
figure 5

a Mean cross-sectional area (CSA) and (b) capillary-to-fibre ratio (C/F) of multifidus muscle fibres in diabetic and control groups. CSA and C/F were decreased in the diabetic group compared to those in the control group (#p < 0.05)

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Discussion

To our best knowledge, this is the first study to estimate the microcirculation and structural changes in skeletal muscle in alloxan-induced diabetic rabbits using permeability parameters of DCE-MRI and texture features extracted from Ktrans maps. We observed that microvascular permeability, CSA, and C/F ratio of the multifidus muscle were altered in DM. The permeability parameters were correlated with CSA and C/F ratio. Ktrans map-based texture parameters reflected these microarchitectural changes in diabetic skeletal muscles.

The multifidus muscle was selected for two reasons. First, it is a distinguishable anatomical landmark and pathologic specimen. Second, it plays a crucial role in maintaining the stability of the lumbosacral spine, which is closely related to low back pain, denervation, and spinal deformities [32]. Microvascular endothelium barrier dysfunction is a classic feature of DM in the early stage due to glucose toxicity and inflammatory responses. Metabolic toxicity may cause skeletal muscle pericyte dysfunction and loss, which in turn diminishes its supportive–protective effect on capillary endothelial cells before capillary loss. These reactions may result in ultrastructural remodelling in microvascular beds, and consequent plasma leakage in various tissues, including skeletal muscle [33, 34]. Thus, microvascular permeability is altered in diabetic muscles. Our study revealed increased Ktrans and Kep at the 8th week in the diabetic group. Ganesh et al [15] have attributed the increased Ktrans of skeletal muscle to the loss of endothelial integrity in an I/R injury model. Likewise, our study’s findings suggest hyperpermeability and reflect endothelial barrier injury in the early stage of diabetes. We also observed a decreasing trend of Ktrans and Kep in the diabetic group. These effects may be related to abnormal perfusion. Decreased perfusion in diabetic muscles has been supported by evidence from studies of adequate imaging techniques and other methods, such as decreased initial area under the signal intensity-time curve value over the first 60 s (IAUC60) on DCE-MRI [18], reduced perfusion ratio on laser Doppler perfusion imaging [35], reduced blood flow on ASL [10], and reduced video intensity on contrast-enhanced ultrasound (CEU) [36] in diabetic animals and patients. Wang et al [17] demonstrated that diabetic patients experienced severe ischaemic damage displayed significantly lower Ktrans values in the tibialis anterior muscles. In this condition, reduced perfusion may have induced a decrease in Ktrans. Conversely, our study focused on animal models of DM in the early stages; hence, the inconsistency may be attributed to differences in disease duration.

Vp represents the fractional volume of plasma per unit volume of tissue [12]. To this end, the reduced Vp values in our study may reflect decreased perfusion in the diabetic group, consistent with previous studies. Decreased C/F ratio signifies sparse capillaries between the diabetic muscle fibres. This factor may explain the reduction in Vp, considering the strong positive correlation between factors. Our study revealed increased Ve in the diabetic group from the 8th week, suggesting an expanded EES; this corresponded to the enlarged interstitial spaces between diabetic muscle fibres as shown on HE staining. Additionally, decreased CSA of diabetic muscles was observed, which is consistent with previous findings [37]. Our study also revealed a strong negative correlation between CSA and Ve, demonstrating that muscle fibre atrophy may induce an increase in Ve as EES expands. Therefore, Ve may be useful for assessing muscle atrophy.

In this study, median Ktrans was significantly increased in the diabetic group at the 8th week; as with mean Ktrans, this suggested greater permeability. Skewness has recently been investigated in several studies. Shukla-Dave et al [38] reported that skewness of Ktrans was a strong predictor of progression-free survival and overall survival in head and neck squamous cell carcinoma patients with stage IV nodal disease. Just [39] also demonstrated that hypoxic and necrotic regions increased asymmetric distributions (skewness) of Ktrans and Kep in DCE-MRI, indicating poor perfusion within tumours. In our study, both groups showed positive skewness of Ktrans values; however, a lower skewness was observed in the diabetic group from the 4th week, although this group had relatively poor perfusion. A possible explanation is that there were no areas of obvious cystoid variation and necrosis within diabetic skeletal muscle in our study, and the distribution of Ktrans tended to higher intensity values than those of the control group, as permeability increased. This also indicates that skewness of Ktrans may have advantages for estimating the permeability of diabetic muscles at an early stage.

Energy (angular second moment, ASM), entropy, correlation, IDM, and contrast are frequently used for evaluating tumour heterogeneity; they have also been applied in the evaluation of skeletal muscles. A longitudinal study demonstrated that the shortened walking distance of patients with lumbar spinal stenosis was associated with entropy based on axial T2W TSE images of the paraspinal muscle [22]. Watanabe et al [40] observed decreased IDM and ASM in sonograms of aging quadriceps femoris muscle. Mahmoud-Ghoneim et al [41] reported that ASM and entropy could discriminate muscle conditions of normal, atrophy, and regeneration in rats, which were related to abnormal modifications in the mean CSA of type 2 fibres. In another study, contrast and entropy based on T2WI could identify different texture distribution of leg muscle groups in various mouse models of muscular dystrophies; this was related to degenerating and regenerating muscle cells, inflammatory infiltrates, and differential distribution of fibrosis [21]. These GLCM parameters demonstrate a potential value in identifying subtle changes in skeletal muscle microstructure, and associations with pathology increase the robustness of these observations.

Our study revealed higher entropy and lower IDM in the diabetic group from the 8th week, indicating increased complexity and irregularity of Ktrans map texture. This may be explained by histological findings. Amin et al [42] observed markedly increased interstitial connective tissue around blood vessels and muscle fibres, as well as ultrastructural modifications such as disrupted sarcomere organisation, swollen mitochondria, and lipid droplet infiltration in diabetic skeletal muscles. Our results revealed sparsely distributed microvessels and atrophic muscle fibres with enlarged interstitial spaces. The axial T1WI images in our study also showed muscular atrophy. These factors may have contributed to uneven distribution of contrast medium in diabetic muscles, resulting in increased complexity and local variability in image texture. Contrast significantly increased while energy decreased in the diabetic group only at the 8th week, reflecting differences in grey levels of adjacent pixels and higher inhomogeneity of Ktrans map at this time point. These findings suggested that entropy and IDM might be more sensitive than other GLCM parameters for detecting altered microarchitecture in diabetic muscles.

There are several limitations to this study. First, the progression of diabetes-induced animals was only observed for 16 weeks; therefore, understanding complete temporal variations in permeability of diabetic muscles requires further study. Second, our study did not investigate clinical signs of myopathy and microvascular pathology. More pathological markers of myopathy, such as blood flow/perfusion obtained by DSC or ASL MRI, MR spectroscopy, and functional parameters (e.g., force and fatigability), are needed to discern subtle changes in skeletal muscle features and investigate the confounding factors of microvascular permeability and perfusion. Third, the low temporal resolution (8 s) may lead to overestimations of the Vp value. In addition, the sum of Vp and Ve in few rabbits showed values slightly greater than 1, which might be due to a computational bias, when a pharmacokinetic model based on a database of humans was used on rabbits. A multi-compartment pharmacokinetic model might be a more suitable pharmacokinetic model for evaluating the multifidus muscle of rabbits. Despite this, differences between the diabetic and control groups indicate that Vp and Ve are promising parameters with which to estimate the blood supply in the skeletal muscle. Finally, a small sample size was used. Definitive conclusions on texture features of diabetic skeletal muscle should be confirmed by using larger samples.

In conclusion, quantitative DCE-MRI can be used to evaluate microvascular permeability and perfusion of diabetic skeletal muscles; TA based on Ktrans maps can identify microarchitectural modifications in diabetic skeletal muscle at early stages.