Korean Journal of Radiology

  • 제22권3호
  • /
  • Pages.435-441
  • /
  • 2021
  • /
  • 1229-6929(pISSN)
  • /
  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
권호 표지
DOI QR코드

DOI QR Code

Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach

  • Hyun Woo Goo (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • 투고 : 2020.03.31
  • 심사 : 2020.06.22
  • 발행 : 2021.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus. Materials and Methods: Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations. Results: Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm3, 249.0 ± 150.8 cm3, and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948-1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity. Conclusion: The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.

키워드

참고문헌

  1. Naidich TP, Epstein F, Lin JP, Kricheff II, Hochwald GM. Evaluation of pediatric hydrocephalus by computed tomography. Radiology 1976;119:337-345  https://doi.org/10.1148/119.2.337
  2. Yabuuchi H, Kamitani T, Sagiyama K, Yamasaki Y, Matsuura Y, Hino T, et al. Clinical application of radiation dose reduction for head and neck CT. Eur J Radiol 2018;107:209-215  https://doi.org/10.1016/j.ejrad.2018.08.021
  3. Sze RW, Ghioni V, Weinberger E, Seidel KD, Ellenbogen RG. Rapid computed tomography technique to measure ventricular volumes in the child with suspected ventriculoperitoneal shunt failure II: clinical application. J Comput Assist Tomogr 2003;27:668-673  https://doi.org/10.1097/00004728-200309000-00002
  4. Yamin G, Cheecharoen P, Goel G, Sung A, Li CQ, Chang YA, et al. Automated CT registration tool improves sensitivity to change in ventricular volume in patients with shunts and drains. Br J Radiol 2020;93:20190398 
  5. Wilk R, Kluczewska E, Syc B, Bajor G. Normative values for selected linear indices of the intracranial fluid spaces based on CT images of the head in children. Pol J Radiol 2011;76:16-25 
  6. Ragan DK, Cerqua J, Nash T, McKinstry RC, Shimony JS, Jones BV, et al. The accuracy of linear indices of ventricular volume in pediatric hydrocephalus: technical note. J Neurosurg Pediatr 2015;15:547-551  https://doi.org/10.3171/2014.10.PEDS14209
  7. Mardini S, See LC, Lo LJ, Salgado CJ, Chen YR. Intracranial space, brain, and cerebrospinal fluid volume measurements obtained with the aid of three-dimensional computerized tomography in patients with and without Crouzon syndrome. J Neurosurg 2005;103(3 Suppl):238-246 
  8. Liu J, Huang S, Ihar V, Ambrosius W, Lee LC, Nowinski WL. Automatic model-guided segmentation of the human brain ventricular system from CT images. Acad Radiol 2010;17:718-726  https://doi.org/10.1016/j.acra.2010.02.013
  9. Multani JS, Oermann EK, Titano J, Mascitelli J, Nicol K, Feng R, et al. Quantitative computed tomography ventriculography for assessment and monitoring of hydrocephalus: a pilot study and description of method in subarachnoid hemorrhage. World Neurosurg 2017;104:136-141  https://doi.org/10.1016/j.wneu.2017.04.107
  10. Goo HW. Semiautomatic three-dimensional threshold-based cardiac computed tomography ventricular volumetry in repaired tetralogy of fallot: comparison with cardiac magnetic resonance imaging. Korean J Radiol 2019;20:102-113  https://doi.org/10.3348/kjr.2018.0237
  11. Goo HW. Volumetric severity assessment of Ebstein anomaly using three-dimensional cardiac CT: a feasibility study. Cardiovasc Imaging Asia 2019;3:61-67  https://doi.org/10.22468/cvia.2019.00052
  12. Yang DH, Goo HW. Pediatric 16-slice CT protocol: radiation dose and image quality. J Korean Radiol Soc 2008;59:333-347  https://doi.org/10.3348/jkrs.2008.59.5.333
  13. Goo HW. CT radiation dose optimization and estimation: an update for radiologists. Korean J Radiol 2012;13:1-11  https://doi.org/10.3348/kjr.2012.13.1.1
  14. Greess H, Lutze J, Nomayr A, Wolf H, Hothorn T, Kalender WA, et al. Dose reduction in subsecond multislice spiral CT examination of children by online tube current modulation. Eur Radiol 2004;14:995-999  https://doi.org/10.1007/s00330-004-2301-9
  15. Wang J, Duan X, Christner JA, Leng S, Grant KL, McCollough CH. Bismuth shielding, organ-based tube current modulation, and global reduction of tube current for dose reduction to the eye at head CT. Radiology 2012;262:191-198  https://doi.org/10.1148/radiol.11110470
  16. Lee KB, Goo HW. Quantitative image quality and histogram-based evaluations of an iterative reconstruction algorithm at low-to-ultralow radiation dose levels: a phantom study in chest CT. Korean J Radiol 2018;19:119-129  https://doi.org/10.3348/kjr.2018.19.1.119
  17. Cho HH, Lee SM, You SK. Pediatric head computed tomography with advanced modeled iterative reconstruction: focus on image quality and reduction of radiation dose. Pediatr Radiol 2020;50:242-251  https://doi.org/10.1007/s00247-019-04532-z
  18. Huff TJ, Ludwig PE, Salazar D, Cramer JA. Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume. Int J Comput Assist Radiol Surg 2019;14:1923-1932  https://doi.org/10.1007/s11548-019-02038-5
  19. Klimont M, Flieger M, Rzeszutek J, Stachera J, Zakrzewska A, Jon' czyk-Potoczna K. Automated ventricular system segmentation in paediatric patients treated for hydrocephalus using deep learning methods. Biomed Res Int 2019;2019:3059170 
  20. Kamochi H, Sunaga A, Chi D, Asahi R, Nakagawa S, Mori M, et al. Growth curves for intracranial volume in normal Asian children fortify management of craniosynostosis. J Craniomaxillofac Surg 2017;45:1842-1845  https://doi.org/10.1016/j.jcms.2017.08.026
  21. Patel DM, Tubbs RS, Pate G, Johnston JM Jr, Blount JP. Fast-sequence MRI studies for surveillance imaging in pediatric hydrocephalus. J Neurosurg Pediatr 2014;13:440-447  https://doi.org/10.3171/2014.1.PEDS13447
  22. Lee E, Goo HW, Lee JY. Age- and gender-specific estimates of cumulative CT dose over 5 years using real radiation dose tracking data in children. Pediatr Radiol 2015;45:1282-1292 https://doi.org/10.1007/s00247-015-3331-y