Linked Life Data

20339283

Source:http://linkedlifedata.com/resource/pubmed/id/20339283

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-3-26
pubmed:abstractText
A 72-year-old woman presented with a metastatic brain tumor around the pituitary stalk compressing the chiasm anteriorly and upward. After tumor resection by a left pterional approach failed, a three-dimensional (3D) image of the area of interest was reconstructed using the Vecter Vision navigation system and iPlan Cranial planning software version 2.5. Preoperative 3D computed tomography and magnetic resonance imaging data were fused to demonstrate the anatomical relationship between vessels, nerves, and tumor. Clearer demonstration of the optic nerves utilized thin slice axial and sagittal views along the nerves. The 3D reconstructed image demonstrated the spatial relationship of the tumor and surrounding tissue, and suggested the necessity of a right orbitozygomatic approach to create adequate working space for tumor resection. Second surgery according to this preoperative planning was successful. High quality multimodal fusion images in a navigation system has distinct advantages in preoperative assessment of essential structural relationships allowing adequate exposure of certain lesions and surrounding structures in individual patients by defining specific surgical approaches.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1349-8029
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
259-63
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Preoperative surgical approach planning for metastatic pituitary stalk tumor using multimodal fusion imaging in a neuronavigation system--case report.
pubmed:affiliation
Department of Neurosurgery, Kochi Medical School, Kochi University, Nankuku, Kochi, Japan.
pubmed:publicationType
Journal Article, Case Reports