Abstract：
:The three-dimensional (3D) visualization of dural venous sinuses (DVS) networks is desired by surgical trainers to create a clear mental picture of the neuroanatomical orientation of the complex cerebral anatomy. Our purpose is to document those identified during routine 3D venography created through 3D models using two-dimensional axial images for teaching and learning neuroanatomy. Anatomical data were segmented and extracted from imaging of the DVS of healthy people. The digital data of the extracted anatomical surfaces was then edited and smoothed, resulting in a set of digital 3D models of the superior sagittal, inferior sagittal, transverse, and sigmoid, rectus sinuses, and internal jugular veins. A combination of 3D printing technology and casting processes led to the creation of realistic neuroanatomical models that include high-fidelity reproductions of the neuroanatomical features of DVS. The life-size DVS training models were provided good detail and representation of the spatial distances. Geometrical details between the neighboring of DVS could be easily manipulated and explored from different angles. A graspable, patient-specific, 3D-printed model of DVS geometry could provide an improved understanding of the complex brain anatomy. These models have various benefits such as the ability to adjust properties, to convert two-dimension images of the patient into three-dimension images, to have different color options, and to be economical. Neuroanatomy experts can model such as the reliability and validity of the designed models, enhance patient satisfaction with improved clinical examination, and demonstrate clinical interventions by simulation; thus, they teach neuroanatomy training with effective teaching styles.
journal_name
J Digit Imagingjournal_title
Journal of digital imagingauthors
Karakas AB,Govsa F,Ozer MA,Eraslan Cdoi
10.1007/s10278-018-0125-4subject
Has Abstractpub_date
2019-04-01 00:00:00pages
314-321issue
2eissn
0897-1889issn
1618-727Xpii
10.1007/s10278-018-0125-4journal_volume
32pub_type
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