id:"swepub:oai:DiVA.org:umu-197981"
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- Ali, HazratUmeå universitet,Radiofysik,Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan (författare)
Modelling intra-muscular contraction dynamics using in silico to in vivo domain translation
- Artikel/kapitelEngelska2022
Förlag, utgivningsår, omfång ...
- 2022-07-08
- BioMed Central (BMC),2022
- electronicrdacarrier
Nummerbeteckningar
- LIBRIS-ID:oai:DiVA.org:umu-197981
- https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-197981URI
- https://doi.org/10.1186/s12938-022-01016-4DOI
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- Språk:engelska
- Sammanfattning på:engelska
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- Background: Advances in sports medicine, rehabilitation applications and diagnostics of neuromuscular disorders are based on the analysis of skeletal muscle contractions. Recently, medical imaging techniques have transformed the study of muscle contractions, by allowing identifcation of individual motor units’ activity, within the whole studied muscle. However, appropriate image-based simulation models, which would assist the continued development of these new imaging methods are missing. This is mainly due to a lack of models that describe the complex interaction between tissues within a muscle and its surroundings, e.g., muscle fbres, fascia, vasculature, bone, skin, and subcutaneous fat. Herein, we propose a new approach to overcome this limitation.Methods: In this work, we propose to use deep learning to model the authentic intramuscular skeletal muscle contraction pattern using domain-to-domain translation between in silico (simulated) and in vivo (experimental) image sequences of skeletal muscle contraction dynamics. For this purpose, the 3D cycle generative adversarial network (cycleGAN) models were evaluated on several hyperparameter settings and modifcations. The results show that there were large diferences between the spatial features of in silico and in vivo data, and that a model could be trained to generate authentic spatio-temporal features similar to those obtained from in vivo experimental data. In addition, we used diference maps between input and output of the trained model generator to study the translated characteristics of in vivo data.Results: This work provides a model to generate authentic intra-muscular skeletal muscle contraction dynamics that could be used to gain further and much needed physiological and pathological insights and assess and overcome limitations within the newly developed research feld of neuromuscular imaging.
Ämnesord och genrebeteckningar
- NATURVETENSKAP Data- och informationsvetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences hsv//eng
- MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Radiologi och bildbehandling hsv//swe
- MEDICAL AND HEALTH SCIENCES Clinical Medicine Radiology, Nuclear Medicine and Medical Imaging hsv//eng
- TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
- ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
- Domain adaptation
- Noise adaptation
- Generative adversarial network
- Neural networks
- Skeletal muscle
- Ultrasound
- Plane wave
- High frame rate imaging
- Fascia
- Simulation model
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Umander, JohannesUmeå universitet,Radiofysik(Swepub:umu)joum0001 (författare)
- Rohlén, RobinUmeå universitet,Radiofysik(Swepub:umu)roro0009 (författare)
- Röhrle, OliverStuttgart Center for Simulation Technology (SC SimTech), University of Stuttgart, Stuttgart, Germany; Institute for Modelling and Simulation of Biomechanical Systems, Chair for Computational Biophysics and Biorobotics, University of Stuttgart, Stuttgart, Germany (författare)
- Grönlund, ChristerUmeå universitet,Radiofysik(Swepub:umu)chgr0009 (författare)
- Umeå universitetRadiofysik (creator_code:org_t)
Sammanhörande titlar
- Ingår i:Biomedical engineering online: BioMed Central (BMC)21:11475-925X
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