Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model

Abstract

Background: Skeletal muscle injury characterisation during healing supports trauma prognosis. Given the potential interest of computed tomography (CT) in muscle diseases and lack of in vivo CT methodology to image skeletal muscle wound healing, we tracked skeletal muscle injury recovery using in vivo micro-CT in a rat model to obtain a predictive model.

Methods: Skeletal muscle injury was performed in 23 rats. Twenty animals were sorted into five groups to image lesion recovery at 2, 4, 7, 10, or 14 days after injury using contrast-enhanced micro-CT. Injury volumes were quantified using a semiautomatic image processing, and these values were used to build a prediction model. The remaining 3 rats were imaged at all monitoring time points as validation. Predictions were compared with Bland-Altman analysis.

Results: Optimal contrast agent dose was found to be 20 mL/kg injected at 400 μL/min. Injury volumes showed a decreasing tendency from day 0 (32.3 ± 12.0mm³, mean ± standard deviation) to day 2, 4, 7, 10, and 14 after injury (19.6 ± 12.6, 11.0 ± 6.7, 8.2 ± 7.7, 5.7 ± 3.9, and 4.5 ± 4.8 mm³, respectively). Groups with single monitoring time point did not yield significant differences with the validation group lesions. Further exponential model training with single follow-up data (R² = 0.968) to predict injury recovery in the validation cohort gave a predictions root mean squared error of 6.8 ± 5.4 mm³. Further prediction analysis yielded a bias of 2.327.

Conclusion: Contrast-enhanced CT allowed in vivo tracking of skeletal muscle injury recovery in rat.

Keywords: Muscle (skeletal); Muscular diseases; Rats; Tomography (x-ray computed); Wound healing.

Fig. 1

Contrast-to-noise ratio quantification for several infusion rates. Tested contrast injection rates from 100 to 500 μL/min (yellow), with an overall administration time of 20 min. The red dashed line defines the applied cutoff criterion to determine the optimal infusion rate. CNR Contrast-to-noise ratio

Fig. 2

Contrast-enhanced micro-computed tomography images and segmented injuries from one representative example of the validation cohort. a First row shows short-axis images of the lesion in similar location during all follow-up time points. Images were reconstructed using a filtered back-projection approach with a Ram-Lak filter. Second row images superimpose the obtained segmentation mask (blue) of the injury on the first row images. b Three-dimensional volumetric rendering of the segmented lesions (white) during all follow-up time points

Fig. 3

In vivo three-dimensional skeletal muscle injury volume quantification up to 14 days after injury. This quantification includes both single follow-up (n = 20) and validation (n = 3) cohorts

Fig. 4

Bland-Altman analysis of the predicted injury volumes in the validation cohort. Predictions were compared to the injury volumes during all follow-up time points. Red dotted line corresponds to the bias, with corresponding lower and upper limits of agreement at 95% represented with green dotted lines