Estimating postoperative skull defect volume from CT images using the ABC method

Furen Xiao, I. Jen Chiang, Thomas Mon Hsian Hsieh, Ke Chun Huang, Yi Hsin Tsai, Jau Min Wong, Hsien Wei Ting, Chun Chih Liao

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Objectives: Surgeons often perform decompressive craniectomy to alleviate a medically-refractory increase of intracranial pressure. The frequency of this type of surgery is on the rise. The goal of this study is to develop a simple formula for clinicians to estimate the volume of the skull defect, based on postoperative computed tomography (CT) studies. Methods: We collected thirty sets of postoperative CT images from patients undergoing craniectomy. We measured the skull defect volume by computer-assisted volumetric analysis (Vm) and our own ABC technique (Vabc). We then compared the volumes measured by these two methods. Results: The Vm ranged from 3.2 to 76.4 mL, with a mean of 38.9 mL. The Vabc ranged from 3.8 to 71.5 mL, with a mean of 38.5 mL. The absolute differences between V abc and Vm ranged from 0.05 to 17.5 mL (mean: 3.8 ± 4.2). There was no statistically significant difference between Vabc and Vm (p = 0.961). The correlation coefficient between V abc and Vm was 0.969. In linear regression analysis, the slope was 1.00086 and the intercept was -0.0035 mL (r2 = 0.939). The residual was 5.7 mL. Conclusion: We confirmed that the ABC technique is a simple and accurate method for estimating skull defect volume, and we recommend routine application of this formula for all decompressive craniectomies.

Original languageEnglish
Pages (from-to)205-210
Number of pages6
JournalClinical Neurology and Neurosurgery
Volume114
Issue number3
DOIs
Publication statusPublished - Apr 2012

Keywords

  • ABC
  • Craniectomy
  • Skull defects
  • Volume measurement

ASJC Scopus subject areas

  • Clinical Neurology
  • Surgery

Fingerprint

Dive into the research topics of 'Estimating postoperative skull defect volume from CT images using the ABC method'. Together they form a unique fingerprint.

Cite this