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    Determining Subject-Specific Lower-Limb Muscle Architecture Data for Musculoskeletal Models Using Diffusion Tensor Imaging

    Source: Journal of Biomechanical Engineering:;2019:;volume( 141 ):;issue: 006::page 60905
    Author:
    Charles, James P.
    ,
    Moon, Chan-Hong
    ,
    Anderst, William J.
    DOI: 10.1115/1.4040946
    Publisher: American Society of Mechanical Engineers (ASME)
    Abstract: Accurate individualized muscle architecture data are crucial for generating subject-specific musculoskeletal models to investigate movement and dynamic muscle function. Diffusion tensor imaging (DTI) magnetic resonance (MR) imaging has emerged as a promising method of gathering muscle architecture data in vivo; however, its accuracy in estimating parameters such as muscle fiber lengths for creating subject-specific musculoskeletal models has not been tested. Here, we provide a validation of the method of using anatomical magnetic resonance imaging (MRI) and DTI to gather muscle architecture data in vivo by directly comparing those data obtained from MR scans of three human cadaveric lower limbs to those from dissections. DTI was used to measure fiber lengths and pennation angles, while the anatomical images were used to estimate muscle mass, which were used to calculate physiological cross-sectional area (PCSA). The same data were then obtained through dissections, where it was found that on average muscle masses and fiber lengths matched well between the two methods (4% and 1% differences, respectively), while PCSA values had slightly larger differences (6%). Overall, these results suggest that DTI is a promising technique to gather in vivo muscle architecture data, but further refinement and complementary imaging techniques may be needed to realize these goals.
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      Determining Subject-Specific Lower-Limb Muscle Architecture Data for Musculoskeletal Models Using Diffusion Tensor Imaging

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4258754
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    contributor authorCharles, James P.
    contributor authorMoon, Chan-Hong
    contributor authorAnderst, William J.
    date accessioned2019-09-18T09:05:31Z
    date available2019-09-18T09:05:31Z
    date copyright4/22/2019 12:00:00 AM
    date issued2019
    identifier issn0148-0731
    identifier otherbio_141_06_060905
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4258754
    description abstractAccurate individualized muscle architecture data are crucial for generating subject-specific musculoskeletal models to investigate movement and dynamic muscle function. Diffusion tensor imaging (DTI) magnetic resonance (MR) imaging has emerged as a promising method of gathering muscle architecture data in vivo; however, its accuracy in estimating parameters such as muscle fiber lengths for creating subject-specific musculoskeletal models has not been tested. Here, we provide a validation of the method of using anatomical magnetic resonance imaging (MRI) and DTI to gather muscle architecture data in vivo by directly comparing those data obtained from MR scans of three human cadaveric lower limbs to those from dissections. DTI was used to measure fiber lengths and pennation angles, while the anatomical images were used to estimate muscle mass, which were used to calculate physiological cross-sectional area (PCSA). The same data were then obtained through dissections, where it was found that on average muscle masses and fiber lengths matched well between the two methods (4% and 1% differences, respectively), while PCSA values had slightly larger differences (6%). Overall, these results suggest that DTI is a promising technique to gather in vivo muscle architecture data, but further refinement and complementary imaging techniques may be needed to realize these goals.
    publisherAmerican Society of Mechanical Engineers (ASME)
    titleDetermining Subject-Specific Lower-Limb Muscle Architecture Data for Musculoskeletal Models Using Diffusion Tensor Imaging
    typeJournal Paper
    journal volume141
    journal issue6
    journal titleJournal of Biomechanical Engineering
    identifier doi10.1115/1.4040946
    journal fristpage60905
    journal lastpage060905-9
    treeJournal of Biomechanical Engineering:;2019:;volume( 141 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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