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 usin...

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Main Authors: Bruno Paun, Daniel García Leon, Alex Claveria Cabello, Roso Mares Pages, Elena de la Calle Vargas, Paola Contreras Muñoz, Vanessa Venegas Garcia, Joan Castell-Conesa, Mario Marotta Baleriola, Jose Raul Herance Camacho
Format: Article
Language:English
Published: SpringerOpen 2020-06-01
Series:European Radiology Experimental
Subjects:
Online Access:http://link.springer.com/article/10.1186/s41747-020-00163-4
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spelling doaj-f817e27f58714825890110ae38b6696c2020-11-25T03:25:13ZengSpringerOpenEuropean Radiology Experimental2509-92802020-06-01411810.1186/s41747-020-00163-4Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat modelBruno Paun0Daniel García Leon1Alex Claveria Cabello2Roso Mares Pages3Elena de la Calle Vargas4Paola Contreras Muñoz5Vanessa Venegas Garcia6Joan Castell-Conesa7Mario Marotta Baleriola8Jose Raul Herance Camacho9Medical Molecular Imaging Group, Vall d’Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona (UAB)Medical Molecular Imaging Group, Vall d’Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona (UAB)Medical Molecular Imaging Group, Vall d’Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona (UAB)Medical Molecular Imaging Group, Vall d’Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona (UAB)Medical Molecular Imaging Group, Vall d’Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona (UAB)Health & Biomedicine division, Leitat Technological CenterHealth & Biomedicine division, Leitat Technological CenterMedical Molecular Imaging Group, Vall d’Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona (UAB)Health & Biomedicine division, Leitat Technological CenterMedical Molecular Imaging Group, Vall d’Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona (UAB)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 (R 2 = 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.http://link.springer.com/article/10.1186/s41747-020-00163-4Muscle (skeletal)Muscular diseasesRatsTomography (x-ray computed)Wound healing
collection DOAJ
language English
format Article
sources DOAJ
author Bruno Paun
Daniel García Leon
Alex Claveria Cabello
Roso Mares Pages
Elena de la Calle Vargas
Paola Contreras Muñoz
Vanessa Venegas Garcia
Joan Castell-Conesa
Mario Marotta Baleriola
Jose Raul Herance Camacho
spellingShingle Bruno Paun
Daniel García Leon
Alex Claveria Cabello
Roso Mares Pages
Elena de la Calle Vargas
Paola Contreras Muñoz
Vanessa Venegas Garcia
Joan Castell-Conesa
Mario Marotta Baleriola
Jose Raul Herance Camacho
Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model
European Radiology Experimental
Muscle (skeletal)
Muscular diseases
Rats
Tomography (x-ray computed)
Wound healing
author_facet Bruno Paun
Daniel García Leon
Alex Claveria Cabello
Roso Mares Pages
Elena de la Calle Vargas
Paola Contreras Muñoz
Vanessa Venegas Garcia
Joan Castell-Conesa
Mario Marotta Baleriola
Jose Raul Herance Camacho
author_sort Bruno Paun
title Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model
title_short Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model
title_full Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model
title_fullStr Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model
title_full_unstemmed Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model
title_sort modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-ct: a proof-of-concept study in a rat model
publisher SpringerOpen
series European Radiology Experimental
issn 2509-9280
publishDate 2020-06-01
description 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 (R 2 = 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.
topic Muscle (skeletal)
Muscular diseases
Rats
Tomography (x-ray computed)
Wound healing
url http://link.springer.com/article/10.1186/s41747-020-00163-4
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