European Radiology

, Volume 29, Issue 4, pp 2017–2026 | Cite as

Quantitative susceptibility mapping in the human fetus to measure blood oxygenation in the superior sagittal sinus

  • Brijesh Kumar Yadav
  • Sagar Buch
  • Uday Krishnamurthy
  • Pavan Jella
  • Edgar Hernandez-Andrade
  • Anabela Trifan
  • Lami Yeo
  • Sonia S. Hassan
  • E. Mark Haacke
  • Roberto RomeroEmail author
  • Jaladhar NeelavalliEmail author
Magnetic Resonance



To present the feasibility of performing quantitative susceptibility mapping (QSM) in the human fetus to evaluate the oxygenation (SvO2) of cerebral venous blood in vivo.


Susceptibility weighted imaging (SWI) data were acquired from healthy pregnant subjects (n = 21, median = 31.3 weeks, interquartile range = 8.8 weeks). The susceptibility maps were generated from the SWI-phase images using a modified QSM processing pipeline, optimised for fetal applications. The processing pipeline is as follows: (1) mild high-pass filtering followed by quadratic fitting of the phase images to eliminate background phase variations; (2) manual creation of a fetal brain mask that includes the superior sagittal sinus (SSS); (3) inverse filtering of the resultant masked phase images using a truncated k-space approach with geometric constraint. Further, the magnetic susceptibility, χv and corresponding putative SvO2 of the SSS were quantified from the generated susceptibility maps. Systematic error in the measured SvO2 due to the modified pipeline was also studied through simulations.


Simulations showed that the systematic error in SvO2 when using a mask that includes a minimum of 5 voxels around the SSS and five slices remains < 3% for different orientations of the vessel relative to the main magnetic field. The average χv in the SSS quantified across all gestations was 0.42 ± 0.03 ppm. Based on χv, the average putative SvO2 in the SSS across all fetuses was 67% ± 7%, which is in good agreement with published studies.


This in vivo study demonstrates the feasibility of using QSM in the human fetal brain to estimate χv and SvO2.

Key Points

A modified quantitative susceptibility mapping (QSM) processing pipeline is tested and presented for the human fetus.

QSM is feasible in the human fetus for measuring magnetic susceptibility and oxygenation of venous blood in vivo.

Blood magnetic susceptibility values from MR susceptometry and QSM agree with each other in the human fetus.


Magnetic resonance imaging Brain Second trimester 



Magnetic susceptibility


Magnetic susceptibility difference between fully oxygenated and deoxygenated fetal blood


Brain Extraction Tool






Gestational age




Quantitative susceptibility mapping


Venous oxygen saturation


Specific absorption rate


Superior sagittal sinus


Slice thickness



Dr. Romero has contributed to this work as part of his official duties as an employee of the United States Federal Government.


This research was supported, in part, by the Perinatology Research Branch (PRB), Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS); in part, with Federal funds from NICHD/NIH/DHHS under Contract No. HHSN275201300006C; and an STTR grant from the NHLBI number 1R42HL112580- 01A1.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Jaladhar Neelavalli.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

The imaging data used in this manuscript has been used in part previously in an earlier manuscript (Yadav et al [10]), which evaluated fetal blood oxygenation as a function of gestational age in a larger cohort. This manuscript, on the other hand, focuses on demonstrating the applicability of a novel technique in the human fetus for in vivo blood oximetry and compares the results with those obtained using the standard model-dependent method. We find that the results affirm the applicability of both methods for in vivo fetal blood susceptometry and oximetry.


• Prospective

• Experimental

• Performed at one institution


  1. 1.
    Schneider H (2011) Oxygenation of the placental–fetal unit in humans. Respir Physiol Neurobiol 178:51-58Google Scholar
  2. 2.
    Gagnon R (2003) Placental insufficiency and its consequences. Eur J Obstet Gynecol Reprod Biol 110:S99–S107CrossRefGoogle Scholar
  3. 3.
    Maberry MC, Ramin SM, Gilstrap LC 3rd, Leveno KJ, Dax JS (1990) Intrapartum asphyxia in pregnancies complicated by intra-amniotic infection. Obstet Gynecol 76:351–354Google Scholar
  4. 4.
    Escobar J, Teramo K, Stefanovic V et al (2013) Amniotic fluid oxidative and nitrosative stress biomarkers correlate with fetal chronic hypoxia in diabetic pregnancies. Neonatology 103:193–198CrossRefGoogle Scholar
  5. 5.
    Low JA, Galbraith RS, Muir DW, Killen HL, Pater EA, Karchmar EJ (1985) The relationship between perinatal hypoxia and newborn encephalopathy. Am J Obstet Gynecol 152:256–260Google Scholar
  6. 6.
    Gunn AJ, Bennet L (2009) Fetal hypoxia insults and patterns of brain injury: insights from animal models. Clin Perinatol 36:579–593CrossRefGoogle Scholar
  7. 7.
    Hall D (1989) Birth asphyxia and cerebral palsy. BMJ 299(6694):279CrossRefGoogle Scholar
  8. 8.
    Cetin I, Barberis B, Brusati V et al (2011) Lactate detection in the brain of growth-restricted fetuses with magnetic resonance spectroscopy. Am J Obstet Gynecol 205:350.e1–350.e7CrossRefGoogle Scholar
  9. 9.
    Krishnamurthy U, Yadav BK, Jella PK et al (2018) Quantitative flow imaging in human umbilical vessels in utero using nongated 2d phase contrast MRI. J Magn Reson Imaging 48:283–289CrossRefGoogle Scholar
  10. 10.
    Yadav BK, Krishnamurthy U, Buch S et al (2018) Imaging putative foetal cerebral blood oxygenation using susceptibility weighted imaging (SWI). Eur Radiol (28):1884–1890Google Scholar
  11. 11.
    Zhu MY, Milligan N, Keating S et al (2016) The hemodynamics of late-onset intrauterine growth restriction by MRI. Am J Obstet Gynecol 214:367.e1–367.e17CrossRefGoogle Scholar
  12. 12.
    Neelavalli J, Jella PK, Krishnamurthy U et al (2014) Measuring venous blood oxygenation in fetal brain using susceptibility-weighted imaging. J Magn Reson Imaging 39:998–1006CrossRefGoogle Scholar
  13. 13.
    Stuber M, Botnar RM, Fischer SE et al (2002) Preliminary report on in vivo coronary MRA at 3 Tesla in humans. Magn Reson Med 48:425–429CrossRefGoogle Scholar
  14. 14.
    Neelavalli J, Mody S, Yeo L et al (2014) MR venography of the fetal brain using susceptibility weighted imaging. J Magn Reson Imaging 40:949–957CrossRefGoogle Scholar
  15. 15.
    Haacke EM, Liu S, Buch S, Zheng W, Wu D, Ye Y (2015) Quantitative susceptibility mapping: current status and future directions. Magn Reson Med 33(1):–25Google Scholar
  16. 16.
    Haacke EM, Tang J, Neelavalli J, Cheng YC (2010) Susceptibility mapping as a means to visualize veins and quantify oxygen saturation. J Magn Reson Imaging 32:663-676Google Scholar
  17. 17.
    Liu T, Surapaneni K, Lou M, Cheng L, Spincemaille P, Wang Y (2012) Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping. Radiology 262:269–278CrossRefGoogle Scholar
  18. 18.
    Deistung A, Schweser F, Reichenbach JR (2017) Overview of quantitative susceptibility mapping. NMR Biomed 30(4):e3569CrossRefGoogle Scholar
  19. 19.
    Liu C, Wei H, Gong NJ, Cronin M, Dibb R, Decker K (2015) Quantitative susceptibility mapping: contrast mechanisms and clinical applications. Tomography 1(1):3CrossRefGoogle Scholar
  20. 20.
    Liu C, Li W, Tong KA, Yeom KW, Kuzminski S (2015) Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 42:23–41CrossRefGoogle Scholar
  21. 21.
    Carpenter KL, Li W, Wei H et al (2016) Magnetic susceptibility of brain iron is associated with childhood spatial IQ. Neuroimage 132:167–174CrossRefGoogle Scholar
  22. 22.
    Liu J, Xia S, Hanks R et al (2016) Susceptibility weighted imaging and mapping of micro-hemorrhages and major deep veins after traumatic brain injury. J Neurotrauma 33:10–21CrossRefGoogle Scholar
  23. 23.
    Haacke EM, Reichenbach JR (2014) Susceptibility weighted imaging in MRI: basic concepts and clinical applications. Wiley-Blackwell, HobokenGoogle Scholar
  24. 24.
    Weisskoff RM, Kiihne S (1992) MRI susceptometry: image-based measurement of absolute susceptibility of MR contrast agents and human blood. Magn Reson Med 24:375–383CrossRefGoogle Scholar
  25. 25.
    Tang J, Liu S, Neelavalli J, Cheng YC, Buch S, Haacke EM (2013) Improving susceptibility mapping using a threshold-based K-space/image domain iterative reconstruction approach. Magn Reson Med 69:1396–1407CrossRefGoogle Scholar
  26. 26.
    de Rochefort L, Brown R, Prince MR, Wang Y (2008) Quantitative MR susceptibility mapping using piece-wise constant regularized inversion of the magnetic field. Magn Reson Med 60:1003–1009CrossRefGoogle Scholar
  27. 27.
    Wharton S, Schäfer A, Bowtell R (2010) Susceptibility mapping in the human brain using threshold-based k-space division. Magn Reson Med 63:1292–1304CrossRefGoogle Scholar
  28. 28.
    Schweser F, Deistung A, Lehr BW, Reichenbach JR (2010) Differentiation between diamagnetic and paramagnetic cerebral lesions based on magnetic susceptibility mapping. Med Phys 37:5165–5178CrossRefGoogle Scholar
  29. 29.
    Neelavalli J, Cheng YC, Haacke M (2005) Method for susceptibility calculation in multiple source object distribution with arbitrary susceptibilities: a preliminary report. No. 2333, Proceedings of the International Society for Magnetic Resonance in Medicine, Miami Beach Convention Center, Miami, 7–13 May 2005Google Scholar
  30. 30.
    Spees WM, Yablonskiy DA, Oswood MC, Ackerman JJ (2001) Water proton MR properties of human blood at 1.5 Tesla: magnetic susceptibility, T1, T2, T* 2, and non-Lorentzian signal behavior. Magn Reson Med 45:533–542CrossRefGoogle Scholar
  31. 31.
    Jain V, Abdulmalik O, Propert KJ, Wehrli FW (2012) Investigating the magnetic susceptibility properties of fresh human blood for noninvasive oxygen saturation quantification. Magn Reson Med 68:863–867CrossRefGoogle Scholar
  32. 32.
    Boulot P, Cattaneo A, Taib J et al (1993) Hematologic values of fetal blood obtained by means of cordocentesis. Fetal Ther 8:309–316CrossRefGoogle Scholar
  33. 33.
    Chua S, Yeong S, Razvi K, Arulkumaran S (1997) Fetal oxygen saturation during labour. Br J Obstet Gynaecol 104:1080–1083CrossRefGoogle Scholar
  34. 34.
    Dildy GA, Thorp JA, Yeast JD, Clark SL (1996) The relationship between oxygen saturation and pH in umbilical blood: implications for intrapartum fetal oxygen saturation monitoring. Am J Obstet Gynecol 175(3 Pt 1):682–687CrossRefGoogle Scholar
  35. 35.
    Portnoy S, Milligan N, Seed M, Sled JG, Macgowan CK (2018) Human umbilical cord blood relaxation times and susceptibility at 3 T. Magn Reson Med 79:3194–3206CrossRefGoogle Scholar
  36. 36.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155CrossRefGoogle Scholar
  37. 37.
    Pooh RK, Kurjak A (2010) Fetal brain vascularity visualized by conventional 2D and 3D power Doppler technology. DSJUOG 4:249–258CrossRefGoogle Scholar
  38. 38.
    Soothill PW, Nicolaides KH, Rodeck CH, Campbell S (1986) Effect of gestational age on fetal and intervillous blood gas and acid-base values in human pregnancy. Fetal Ther 1:168–175CrossRefGoogle Scholar
  39. 39.
    Nicolaides KH, Economides DL, Soothill PW (1989) Blood gases, pH, and lactate in appropriate-and small-for-gestational-age fetuses. Am J Obstet Gynecol 161:996–1001Google Scholar
  40. 40.
    Schröter B, Chaoui R, Glatzel E, Bollmann R (1997) [Normal value curves for intrauterine fetal blood gas and acid-base parameters in the 2nd and 3rd trimester]. Gynakol Geburtshilfliche Rundsch 37:130–135Google Scholar
  41. 41.
    Veille JC, Hanson R, Tatum K (1993) Longitudinal quantitation of middle cerebral artery blood flow in normal human fetuses. Am J Obstet Gynecol 169:1393–1398CrossRefGoogle Scholar
  42. 42.
    Habas PA, Kim K, Corbett-Detig JM et al (2010) A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation. Neuroimage 53:460–470CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Brijesh Kumar Yadav
    • 1
    • 2
  • Sagar Buch
    • 3
  • Uday Krishnamurthy
    • 1
    • 2
  • Pavan Jella
    • 1
  • Edgar Hernandez-Andrade
    • 4
    • 5
  • Anabela Trifan
    • 1
  • Lami Yeo
    • 4
    • 5
  • Sonia S. Hassan
    • 4
    • 5
    • 6
  • E. Mark Haacke
    • 1
    • 2
  • Roberto Romero
    • 4
    • 7
    • 8
    • 9
    Email author
  • Jaladhar Neelavalli
    • 1
    • 10
    Email author
  1. 1.Department of RadiologyWayne State University School of MedicineDetroitUSA
  2. 2.Department of Biomedical EngineeringDetroitUSA
  3. 3.The MRI Institute for Biomedical ResearchWaterlooCanada
  4. 4.Perinatology Research Branch, NICHD/NIH/DHHSBethesda, Maryland and DetroitUSA
  5. 5.Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitUSA
  6. 6.Department of PhysiologyWayne State University School of MedicineDetroitUSA
  7. 7.Department of Obstetrics and GynecologyUniversity of MichiganAnn ArborUSA
  8. 8.Department of Epidemiology and BiostatisticsMichigan State UniversityEast LansingUSA
  9. 9.Center for Molecular Medicine and GeneticsWayne State UniversityDetroitUSA
  10. 10.Philips Innovation Campus, Philips India Ltd.BengaluruIndia

Personalised recommendations