title: Automated Cross-Sectional Measurement Method of Intracranial Dural Venous Sinuses.
abstract: OBJECTIVE
  MRV is an important blood vessel imaging and diagnostic tool for the evaluation of stenosis, occlusions, or aneurysms. However, an accurate image-processing tool for vessel comparison is unavailable. The purpose of this study was to develop and test an automated technique for vessel cross-sectional analysis.
  
  
  METHODS
  An algorithm for vessel cross-sectional analysis was developed that included 7 main steps: 1) image registration, 2) masking, 3) segmentation, 4) skeletonization, 5) cross-sectional planes, 6) clustering, and 7) cross-sectional analysis. Phantom models were used to validate the technique. The method was also tested on a control subject and a patient with idiopathic intracranial hypertension (4 large sinuses tested: right and left transverse sinuses, superior sagittal sinus, and straight sinus). The cross-sectional area and shape measurements were evaluated before and after lumbar puncture in patients with idiopathic intracranial hypertension.
  
  
  RESULTS
  The vessel-analysis algorithm had a high degree of stability with <3% of cross-sections manually corrected. All investigated principal cranial blood sinuses had a significant cross-sectional area increase after lumbar puncture (P ≤ .05). The average triangularity of the transverse sinuses was increased, and the mean circularity of the sinuses was decreased by 6% ± 12% after lumbar puncture. Comparison of phantom and real data showed that all computed errors were <1 voxel unit, which confirmed that the method provided a very accurate solution.
  
  
  CONCLUSIONS
  In this article, we present a novel automated imaging method for cross-sectional vessels analysis. The method can provide an efficient quantitative detection of abnormalities in the dural sinuses.
authors.affiliation: From the Zolotowsky Neuroscience Center (S.L., A.F.), Ben-Gurion University, Beer-Sheva, Israel.
authors.forename: S
authors.identifier: http://orcid.org/0000-0002-0390-4887
authors.initials: S
authors.lastname: Lublinsky
authors.affiliation: Department of Medical Neuroscience (A.F.), Dalhousie University, Halifax, Nova Scotia, Canada.
authors.forename: A
authors.identifier: http://orcid.org/0000-0003-4780-8456
authors.initials: A
authors.lastname: Friedman
authors.affiliation: Ophthalmology Department (A.K., D.Z.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
authors.forename: A
authors.identifier: http://orcid.org/0000-0001-7611-8484
authors.initials: A
authors.lastname: Kesler
authors.affiliation: Ophthalmology Department (A.K., D.Z.), Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
authors.forename: D
authors.identifier: http://orcid.org/0000-0003-1147-444X
authors.initials: D
authors.lastname: Zur
authors.affiliation: Diagnostic Imaging Department (R.A., I.S.), Soroka University Medical Center, Beer-Sheva, Israel.
authors.forename: R
authors.identifier: http://orcid.org/0000-0001-5697-6270
authors.initials: R
authors.lastname: Anconina
authors.affiliation: Diagnostic Imaging Department (R.A., I.S.), Soroka University Medical Center, Beer-Sheva, Israel. shelef@bgu.ac.il.
authors.forename: I
authors.identifier: http://orcid.org/0000-0002-8398-6455
authors.initials: I
authors.lastname: Shelef
mesh_terms: D000465:Algorithms; D003392:Cranial Sinuses / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D059906:Neuroimaging / Q000379:methods*; D011559:Pseudotumor Cerebri / Q000175:diagnosis