Skip to main content
SpringerLink
Log in

Texture analysis of deep medullary veins on susceptibility-weighted imaging in infants: evaluating developmental and ischemic changes

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

Susceptibility-weighted imaging (SWI) can be used to evaluate deep medullary veins (DMVs). This study aimed to apply texture analysis on SWI to evaluate developmental and ischemic changes of DMV in infants.

Methods

A total of 38 infants with normal brain MRI (preterm [n = 12], term-equivalent age [TEA] [n = 18], and term [n = 8]) and seven infants with ischemic injury (preterm [n = 2], TEA [n = 1], and term [n = 4]) were included. Regions of interests were manually drawn to include DMVs. First-order texture parameters including entropy, skewness, and kurtosis were derived from SWI. The parameters were compared between groups according to age and presence of ischemic injury. A regression analysis was performed to correlate postmenstrual age (PMA) and parameters. A ROC analysis was performed to differentiate ischemic infants from normal infants.

Results

Among parameters, entropy showed a significant difference between the age groups (preterm vs. TEA vs. term; 5.395 vs. 4.885 vs. 4.883, p = 0.001). There was a significant positive relationship between PMA and entropy (R square = 0.402, p < 0.001). Skewness was significantly higher in the ischemic group compared with that in the normal group (1.37 vs. 0.70, p = 0.001). The ROC on skewness resulted in an AUC of 0.87 (accuracy, 83.2%) for differentiating infants with ischemic injury.

Conclusion

A texture analysis of DMVs on SWI showed differences according to age and presence of ischemic injury. The texture parameters can potentially be used as quantitative markers for differentiating infants with ischemic injury through DMV changes.

Key Points

• The DMV structure of the infant brain could be quantified on SWI with texture analysis.

• Entropy from texture analysis on SWI increased as infants got older.

• Normal and ischemic injured infants could be differentiated with a cutoff value of 1.025 for skewness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AUC:

Area under the curve

CI:

Confidence interval

DMV:

Deep medullary vein

DTI:

Diffusion tensor imaging

DWI:

Diffusion-weighted imaging

ICC:

Interclass correlation coefficient

MRI:

Magnetic resonance imaging

PMA:

Postmenstrual age

ROC:

Receiver operating characteristic curve

ROI:

Region of interest

SWI:

Susceptibility-weighted imaging

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

TEA:

Term-equivalent age

WM:

White matter

References

  1. Holland BA, Haas DK, Norman D, Brant-Zawadzki M, Newton TH (1986) MRI of normal brain maturation. AJNR Am J Neuroradiol 7:201–208

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Volpe JJ (2009) Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. Lancet Neurol 8:110–124

    PubMed  PubMed Central  Google Scholar 

  3. Li AM, Chau V, Poskitt KJ et al (2009) White matter injury in term newborns with neonatal encephalopathy. Pediatr Res 65:85–89

    PubMed  Google Scholar 

  4. Krageloh-Mann I, Horber V (2007) The role of magnetic resonance imaging in elucidating the pathogenesis of cerebral palsy: a systematic review. Dev Med Child Neurol 49:144–151

    PubMed  Google Scholar 

  5. Haacke EM, Mittal S, Wu Z, Neelavalli J, Cheng YC (2009) Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. AJNR Am J Neuroradiol 30:19–30

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Sehgal V, Delproposto Z, Haacke EM et al (2005) Clinical applications of neuroimaging with susceptibility-weighted imaging. J Magn Reson Imaging 22:439–450

    PubMed  Google Scholar 

  7. Arrigoni F, Parazzini C, Righini A et al (2011) Deep medullary vein involvement in neonates with brain damage: an MR imaging study. AJNR Am J Neuroradiol 32:2030–2036

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Ramenghi LA, Govaert P, Fumagalli M, Bassi L, Mosca F (2009) Neonatal cerebral sinovenous thrombosis. Semin Fetal Neonatal Med 14:278–283

    PubMed  Google Scholar 

  9. Kitamura G, Kido D, Wycliffe N, Jacobson JP, Oyoyo U, Ashwal S (2011) Hypoxic-ischemic injury: utility of susceptibility-weighted imaging. Pediatr Neurol 45:220–224

    PubMed  Google Scholar 

  10. Tong KA, Ashwal S, Obenaus A, Nickerson JP, Kido D, Haacke EM (2008) Susceptibility-weighted MR imaging: a review of clinical applications in children. AJNR Am J Neuroradiol 29:9–17

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim JH, Sohn CH (2014) Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLoS One 9:e108335

    PubMed  PubMed Central  Google Scholar 

  12. Eliat PA, Olivie D, Saikali S, Carsin B, Saint-Jalmes H, de Certaines JD (2012) Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma? Neurol Res Int 2012:195176

    PubMed  Google Scholar 

  13. Zhang S, Chiang GC, Magge RS et al (2019) Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas. Eur Radiol 29:2751–2759

    PubMed  Google Scholar 

  14. Kuijf HJ, Bouvy WH, Zwanenburg JJ et al (2016) Quantification of deep medullary veins at 7 T brain MRI. Eur Radiol 26:3412–3418

    PubMed  PubMed Central  Google Scholar 

  15. Benninger KL, Maitre NL, Ruess L, Rusin JA (2019) MR imaging scoring system for white matter injury after deep medullary vein thrombosis and infarction in neonates. AJNR Am J Neuroradiol 40:347–352

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Lee SM, Choi YH, You SK et al (2018) Age-related changes in tissue value properties in children: simultaneous quantification of relaxation times and proton density using synthetic magnetic resonance imaging. Invest Radiol 53:236–245

    PubMed  Google Scholar 

  17. Huang BY, Castillo M (2008) Hypoxic-ischemic brain injury: imaging findings from birth to adulthood. Radiographics 28:417–439 quiz 617

    PubMed  Google Scholar 

  18. Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A (2016) Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol 85:824–829

    PubMed  Google Scholar 

  19. Kim HG, Moon WJ, Han J, Choi JW (2017) Quantification of myelin in children using multiparametric quantitative MRI: a pilot study. Neuroradiology 59:1043–1051

    PubMed  Google Scholar 

  20. Dean DC 3rd, O’Muircheartaigh J, Dirks H et al (2014) Modeling healthy male white matter and myelin development: 3 through 60months of age. Neuroimage 84:742–752

    PubMed  Google Scholar 

  21. Huang YP, Okudera T, Fukusumi A et al (1997) Venous architecture of cerebral hemispheric white matter and comments on pathogenesis of medullary venous and other cerebral vascular malformations. Mt Sinai J Med 64:197–206

    CAS  PubMed  Google Scholar 

  22. Okudera T, Huang YP, Fukusumi A, Nakamura Y, Hatazawa J, Uemura K (1999) Micro-angiographical studies of the medullary venous system of the cerebral hemisphere. Neuropathology 19:93–111

    CAS  PubMed  Google Scholar 

  23. Hooshmand I, Rosenbaum AE, Stein RL (1974) Radiographic anatomy of normal cerebral deep medullary veins: criteria for distinguishing them from their abnormal counterparts. Neuroradiology 7:75–84

    CAS  PubMed  Google Scholar 

  24. Friedman DP (1997) Abnormalities of the deep medullary white matter veins: MR imaging findings. AJR Am J Roentgenol 168:1103–1108

    CAS  PubMed  Google Scholar 

  25. Kersbergen KJ, Benders MJ, Groenendaal F et al (2014) Different patterns of punctate white matter lesions in serially scanned preterm infants. PLoS One 9:e108904

    PubMed  PubMed Central  Google Scholar 

  26. Kocak B, Kizilkilic O, Zeynalova A, Korkmazer B, Kocer N, Islak C (2019) Evaluation of sporadic intracranial cavernous malformations for detecting associated developmental venous anomalies: added diagnostic value of C-arm contrast-enhanced cone-beam CT to routine contrast-enhanced MRI. Eur Radiol 29:783–791

    PubMed  Google Scholar 

  27. Takanashi J, Suzuki H, Barkovich AJ et al (2003) Medullary streaks: dilated medullary vessels in chronic ischemia in children. Neurology 61:583–584

    CAS  PubMed  Google Scholar 

  28. Meoded A, Poretti A, Benson JE, Tekes A, Huisman TA (2014) Evaluation of the ischemic penumbra focusing on the venous drainage: the role of susceptibility weighted imaging (SWI) in pediatric ischemic cerebral stroke. J Neuroradiol 41:108–116

    PubMed  Google Scholar 

  29. Young A, Poretti A, Bosemani T, Goel R, Huisman T (2017) Sensitivity of susceptibility-weighted imaging in detecting developmental venous anomalies and associated cavernomas and microhemorrhages in children. Neuroradiology 59:797–802

    PubMed  Google Scholar 

  30. Messina SA, Poretti A, Tekes A, Robertson C, Johnston MV, Huisman TA (2014) Early predictive value of susceptibility weighted imaging (SWI) in pediatric hypoxic-ischemic injury. J Neuroimaging 24:528–530

    PubMed  Google Scholar 

  31. Iwasaki H, Fujita Y, Hara M (2015) Susceptibility-weighted imaging in acute-stage pediatric convulsive disorders. Pediatr Int 57:922–929

    PubMed  Google Scholar 

  32. Polan RM, Poretti A, Huisman TA, Bosemani T (2015) Susceptibility-weighted imaging in pediatric arterial ischemic stroke: a valuable alternative for the noninvasive evaluation of altered cerebral hemodynamics. AJNR Am J Neuroradiol 36:783–788

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Dai Y, Dong S, Zhu M, Wu D, Zhong Y (2014) Visualizing cerebral veins in fetal brain using susceptibility-weighted MRI. Clin Radiol 69:e392–e397

    CAS  PubMed  Google Scholar 

  34. Nakamura Y, Okudera T, Hashimoto T (1994) Vascular architecture in white matter of neonates: its relationship to periventricular leukomalacia. J Neuropathol Exp Neurol 53:582–589

    CAS  PubMed  Google Scholar 

  35. Takashima S, Tanaka K (1978) Development of cerebrovascular architecture and its relationship to periventricular leukomalacia. Arch Neurol 35:11–16

    CAS  PubMed  Google Scholar 

  36. Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–406

    PubMed  PubMed Central  Google Scholar 

  37. Koo TK, Li MY (2016) A guideline of selecting and reporting Intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163

    PubMed  PubMed Central  Google Scholar 

  38. Materka A, Strzelecki M (2015) On the effect of image brightness and contrast nonuniformity on statistical texture parameters. Found Comput Decis Sci 40:163–185

    Google Scholar 

  39. Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341

    PubMed  PubMed Central  Google Scholar 

  40. Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE (2015) IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42:1341–1353

    PubMed  PubMed Central  Google Scholar 

  41. Orlhac F, Soussan M, Maisonobe JA, Garcia CA, Vanderlinden B, Buvat I (2014) Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med 55:414–422

    CAS  PubMed  Google Scholar 

  42. Frood R, Palkhi E, Barnfield M, Prestwich R, Vaidyanathan S, Scarsbrook A (2018) Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer? Eur Radiol 28:5010–5018

    PubMed  Google Scholar 

  43. Makanyanga J, Ganeshan B, Rodriguez-Justo M et al (2017) MRI texture analysis (MRTA) of T2-weighted images in Crohn's disease may provide information on histological and MRI disease activity in patients undergoing ileal resection. Eur Radiol 27:589–597

    PubMed  Google Scholar 

  44. De Cecco CN, Ganeshan B, Ciolina M et al (2015) Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol 50:239–245

    PubMed  Google Scholar 

  45. Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W (2017) Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol 90:20160665

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors thank Hyesun Ko of the Ajou University Hospital for data-gathering.

Funding

This work was partly funded by the National Research Foundation of Korea (NRF-2017R1D1A1B03034768).

Author information

Author notes

  1. Hyun Gi Kim and Jin Wook Choi contributed equally to the study and share first authorship.

Authors and Affiliations

  1. Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

    Hyun Gi Kim

  2. Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, Suwon, Republic of Korea

    Hyun Gi Kim, Jin Wook Choi & Miran Han

  3. Department of Pediatrics, Ajou University School of Medicine, Ajou University Medical Center, Suwon, Republic of Korea

    Jang Hoon Lee

  4. Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hye Sun Lee

Authors
  1. Hyun Gi Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Wook Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miran Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jang Hoon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hye Sun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hyun Gi Kim.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Hyun Gi Kim.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise (Hye Sun Lee, Ph.D.).

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Cross-sectional study

• Performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 25 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, H.G., Choi, J.W., Han, M. et al. Texture analysis of deep medullary veins on susceptibility-weighted imaging in infants: evaluating developmental and ischemic changes. Eur Radiol 30, 2594–2603 (2020). https://doi.org/10.1007/s00330-019-06618-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-019-06618-6

Keywords

Search

Navigation