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In vivo muscle morphology comparison in post-stroke survivors using ultrasonography and diffusion tensor imaging
KTH, School of Engineering Sciences (SCI), Mechanics.
KTH, School of Engineering Sciences (SCI), Mechanics.
Karolinska Univ Hosp, Dept Med Radiat Phys & Nucl Med, Stockholm, Sweden.;Karolinska Inst, Dept Clin Sci Intervent & Technol, Stockholm, Sweden..
Karolinska Inst, Danderyd Hosp, Dept Clin Sci, Div Rehabil Med, Stockholm, Sweden..
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 11836Article in journal (Refereed) Published
Abstract [en]

Skeletal muscle architecture significantly influences the performance capacity of a muscle. A DTI-based method has been recently considered as a new reference standard to validate measurement of muscle structure in vivo. This study sought to quantify muscle architecture parameters such as fascicle length (FL), pennation angle (PA) and muscle thickness (t(m)) in post-stroke patients using diffusion tensor imaging (DTI) and to quantitatively compare the differences with 2D ultrasonography (US) and DTI. Muscle fascicles were reconstructed to examine the anatomy of the medial gastrocnemius, posterior soleus and tibialis anterior in seven stroke survivors using US- and DTI-based techniques, respectively. By aligning the US and DTI coordinate system, DTI reconstructed muscle fascicles at the same scanning plane of the US data can be identified. The architecture parameters estimated based on two imaging modalities were further compared. Significant differences were observed for PA and t m between two methods. Although mean FL was not significantly different, there were considerable intra-individual differences in FL and PA. On the individual level, parameters measured by US agreed poorly with those from DTI in both deep and superficial muscles. The significant differences in muscle parameters we observed suggested that the DTI-based method seems to be a better method to quantify muscle architecture parameters which can provide important information for treatment planning and to personalize a computational muscle model.

Place, publisher, year, edition, pages
Nature Publishing Group, 2019. Vol. 9, article id 11836
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Physiology
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URN: urn:nbn:se:kth:diva-257437DOI: 10.1038/s41598-019-47968-xISI: 000480678100035PubMedID: 31413264Scopus ID: 2-s2.0-85070752028OAI: oai:DiVA.org:kth-257437DiVA, id: diva2:1347149
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QC 20190830

Available from: 2019-08-30 Created: 2019-08-30 Last updated: 2019-09-05Bibliographically approved

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