Magnetic resonance imaging-based classification of the myodural bridge complex and its influencing factors

被引:1
|
作者
Feng, Xiao [3 ]
Liu, Cong [1 ]
Hu, Dong-Mei [4 ]
Zhang, Jian-Fei [2 ]
Zheng, Nan [2 ]
Chi, Yan-Yan [2 ]
Yu, Sheng-Bo [2 ]
Sui, Hong-Jin [2 ]
Xu, Qiang [1 ,3 ]
机构
[1] 967 Hosp Joint Logist Support Force, Dept Radiol, Dalian 116021, Peoples R China
[2] Dalian Med Univ, Coll Basic Med, Dept Anat, Dalian 116044, Peoples R China
[3] Jinzhou Med Univ, 967 Hosp Joint Logist Support Force, Postgrad Training Base, Dalian 116021, Peoples R China
[4] Dalian Med Univ, Sch Publ Hlth, Dept Hlth Stat, Dalian 116044, Peoples R China
关键词
Myodural bridge complex; Imaging classification; Cervical spine; Degenerative changes; Magnetic resonance imaging; Dura mater; Cerebrospinal fluid; CEREBROSPINAL-FLUID; CHIARI MALFORMATION; CERVICAL-SPINE; BLOOD-FLOW; DYNAMICS; MRI;
D O I
10.1007/s00276-023-03279-5
中图分类号
R602 [外科病理学、解剖学]; R32 [人体形态学];
学科分类号
100101 ;
摘要
Cerebrospinal fluid (CSF) circulation is considered the third circulation of the human body. Recently, some scholars have proposed the myodural bridge (MDB) as a novel power source for CSF flow. Moreover, the suboccipital muscles can exert a driving force on the CSF via the MDB. This hypothesis is directly supported by head rotation and nodding movements, which can affect CSF circulation. The MDB has been validated as a normal structure in humans and mammals. In addition, the fusion of MDB fibers of different origins that act in concert with each other forms the MDB complex (MDBC). The MDBC may be associated with several CSF disorder-related neurological disorders in clinical practice. Therefore, the morphology of the MDBC and its influencing factors must be determined. In this study, T2-weighted imaging sagittal images of the cervical region were analyzed retrospectively in 1085 patients, and magnetic resonance imaging (MRI) typing of the MDBC was performed according to the imaging features of the MDBC in the posterior atlanto-occipital interspace (PAOiS) and posterior atlanto-axial interspace (PAAiS). The effects of age and age-related degenerative changes in the cervical spine on MRI staging of the MDBC were also determined. The results revealed four MRI types of the MDBC: type A (no MDBC hyposignal shadow connected to the dura mater in either the PAOiS or PAAiS), type B (MDBC hyposignal shadow connected to the dura mater in the PAOiS only), type C (MDBC hyposignal shadow connected to the dura mater in the PAAiS only), and type D (MDBC hyposignal shadow connected to the dura mater in both the PAOiS and PAAiS). The influencing factors for the MDBC typing were age (group), degree of intervertebral space stenosis, dorsal osteophytosis, and degenerative changes in the cervical spine (P < 0.05). With increasing age (10-year interval), the incidence of type B MDBC markedly decreased, whereas that of type A MDBC increased considerably. With the deepening of the degree of intervertebral space stenosis, the incidence of type C MDBC increased significantly, whereas that of type A MDBC decreased. In the presence of dorsal osteophytosis, the incidence of type C and D MDBCs significantly decreased, whereas that of type A increased. In the presence of protrusion of the intervertebral disc, the incidence of type B, C, and D MDBCs increased markedly, whereas that of type A MDBC decreased considerably, with cervical degenerative changes combined with spinal canal stenosis. Moreover, the incidence of both type C and D MDBCs increased, whereas that of type A MDBC decreased. Based on the MRI signal characteristics of the dural side of the MDBC, four types of the MDBC were identified. MDBC typing varies dynamically according to population distribution, depending on age and cervical degeneration (degree of intervertebral space stenosis, vertebral dorsal osteophytosis formation, simple protrusion of intervertebral disc, and cervical degeneration changes combined with spinal canal stenosis, except for the degree of protrusion of the intervertebral disc and the degree of spinal canal stenosis); however, it is not influenced by sex.
引用
收藏
页码:125 / 135
页数:11
相关论文
共 50 条
  • [41] Magnetic resonance imaging-based artificial intelligence model in rectal cancer
    Wang, Pei-Pei
    Deng, Chao-Lin
    Wu, Bin
    WORLD JOURNAL OF GASTROENTEROLOGY, 2021, 27 (18) : 2122 - 2130
  • [42] Carotid Plaque Morphological Classification Compared With Biomechanical Cap Stress Implications for a Magnetic Resonance Imaging-Based Assessment
    Gijsen, Frank J. H.
    Nieuwstadt, Harm A.
    Wentzel, Jolanda J.
    Verhagen, Hence J. M.
    van der Lugt, Aad
    van der Steen, Antonius F. W.
    STROKE, 2015, 46 (08) : 2124 - 2128
  • [43] Magnetic resonance imaging-based virtual endoscopy of inner ear pathology
    Schreyer, AG
    Seitz, J
    Strutz, T
    Held, P
    OTOLOGY & NEUROTOLOGY, 2002, 23 (02) : 136 - 140
  • [44] Commissioning of Magnetic Resonance Imaging-Based Tumor Tracking and Beam Control
    Green, O.
    Rankine, L.
    Cai, B.
    Santanam, L.
    Kashani, R.
    Sharma, A.
    Senadheera, L.
    Mahaffey, C.
    Mutic, S.
    MEDICAL PHYSICS, 2015, 42 (06) : 3713 - 3713
  • [45] Magnetic resonance imaging-based spirometry for regional assessment of pulmonary function
    Voorhees, A
    An, J
    Berger, KI
    Goldring, RM
    Chen, Q
    MAGNETIC RESONANCE IN MEDICINE, 2005, 54 (05) : 1146 - 1154
  • [46] Magnetic Resonance Imaging-Based Monitoring of the Accumulation of Polyethylene Terephthalate Nanoplastics
    Bashirova, Narmin
    Butenschoen, Erik
    Poppitz, David
    Gass, Henrik
    Halik, Marcus
    Dentel, Doreen
    Tegenkamp, Christoph
    Matysik, Joerg
    Alia, A.
    MOLECULES, 2024, 29 (18):
  • [47] Magnetic Resonance Imaging-Based Screening Study in a General Population of Adolescents
    Angelini, Paolo
    Cheong, Benjamin Y.
    De Rosen, Veronica V. Lenge
    Lopez, J. Alberto
    Uribe, Carlo
    Masso, Anthony H.
    Ali, Syed W.
    Davis, Barry R.
    Muthupillai, Raja
    Willerson, James T.
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2018, 71 (05) : 579 - 580
  • [48] Magnetic resonance imaging-based deep learning for predicting subtypes of glioma
    Yang, Zhen
    Zhang, Peng
    Ding, Yi
    Deng, Liyi
    Zhang, Tong
    Liu, Yong
    FRONTIERS IN NEUROLOGY, 2025, 16
  • [49] Advances in quantitative magnetic resonance imaging-based biomarkers for Alzheimer disease
    Dickerson, Bradford C.
    ALZHEIMERS RESEARCH & THERAPY, 2010, 2 (04)
  • [50] Magnetic resonance imaging-based artificial intelligence model in rectal cancer
    Pei-Pei Wang
    Chao-Lin Deng
    Bin Wu
    World Journal of Gastroenterology, 2021, 27 (18) : 2122 - 2130