Advertisement
Neuroradiology| Volume 97, P22-27, May 2023

Download started.

Ok

Cerebral oxygen extraction fraction declines with ventricular enlargement in patients with normal pressure hydrocephalus

Published:February 09, 2023DOI:https://doi.org/10.1016/j.clinimag.2023.02.001

      Highlights

      • Oxygen extraction fraction mapping in normal pressure hydrocephalus patients
      • Oxygen extraction fraction is feasible using multiecho gradient echo MRI.
      • Declined oxygen extraction fraction is correlated with enlarged brain ventricles.
      • Oxygen extraction fraction provides understanding of neurodegeneration.

      Abstract

      Objective

      Normal pressure hydrocephalus (NPH) is a neurodegenerative disease that is potentially reversible by shunt surgery in approximately 60% of patients. Imaging may provide a means to investigate brain tissue viability and oxygen metabolism in NPH patients.

      Methods

      Oxygen extraction fraction (OEF) mapping was generated from 3D multi-echo gradient echo MRI (mGRE) data using QQ-CCTV algorithm and cerebral blood flow (CBF) using 3D arterial spin labeling (ASL) MRI data, thereby calculating the cerebral metabolic rate of oxygen (CMRO2 = CBF × OEF × [H]a) in 16 NPH patients. Regression analyses using cortical gray matter and deep gray matter regions were conducted with age, gender, CSF stroke volume and normalized ventricular volume as independent variables.

      Results

      OEF showed significant negative correlations with normalized brain ventricular volumes in the whole brain (p = 0.004, q = 0.01), cortical gray matter (p = 0.004, q = 0.01), caudate (p = 0.02, q = 0.04), and pallidum (p = 0.03, q = 0.04), but no significant correlation with CSF stroke volume (q > 0.05). There was no significant finding with CBF or CMRO2.

      Conclusion

      In NPH patients, low OEF in several regions was significantly correlated with large ventricular volumes, indicating decreased tissue oxygen metabolism with increased NPH severity. OEF mapping may provide a functional understanding of neurodegeneration in NPH and may improve monitoring of disease course and treatment outcomes.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Clinical Imaging
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Shprecher D.
        • Schwalb J.
        • Kurlan R.
        Normal pressure hydrocephalus: diagnosis and treatment.
        Curr Neurol Neurosci Rep. 2008; 8 (2008/08/21): 371-376https://doi.org/10.1007/s11910-008-0058-2
        • Miskin N.
        • Patel H.
        • Franceschi A.M.
        Diagnosis of normal-pressure hydrocephalus: use of traditional measures in the era of volumetric MR imaging.
        Radiology. 2017; 285 (2017/05/13): 197-205https://doi.org/10.1148/radiol.2017161216
        • Verrees M.
        • Selman W.R.
        Management of normal pressure hydrocephalus.
        Am Fam Physician. 2004; 70 (2004/10/01): 1071-1078
        • Hebb A.O.
        • Cusimano M.D.
        Idiopathic normal pressure hydrocephalus: a systematic review of diagnosis and outcome.
        Neurosurgery. 2001; 49 (discussion 1184-1166. 2002/02/16): 1166-1184https://doi.org/10.1097/00006123-200111000-00028
        • Bradley Jr., W.G.
        Magnetic resonance imaging of normal pressure hydrocephalus.
        Semin Ultrasound CT MR. 2016; 37 (2016/04/12): 120-128https://doi.org/10.1053/j.sult.2016.01.005
        • Kitagaki H.
        • Mori E.
        • Ishii K.
        CSF spaces in idiopathic normal pressure hydrocephalus: morphology and volumetry.
        AJNR Am J Neuroradiol. 1998; 19 (1998/09/03): 1277-1284
        • Damasceno B.P.
        Neuroimaging in normal pressure hydrocephalus.
        Dement Neuropsychol. 2015; 9 (2015/10/01): 350-355https://doi.org/10.1590/1980-57642015DN94000350
        • Keong N.C.
        • Pena A.
        • Price S.J.
        Diffusion tensor imaging profiles reveal specific neural tract distortion in normal pressure hydrocephalus.
        PLoS One. 2017; 12 (2017/08/18)e0181624https://doi.org/10.1371/journal.pone.0181624
        • Fattahi N.
        • Arani A.
        • Perry A.
        MR elastography demonstrates increased brain stiffness in normal pressure hydrocephalus.
        AJNR Am J Neuroradiol. 2016; 37 (2015/11/07): 462-467https://doi.org/10.3174/ajnr.A4560
        • Virhammar J.
        • Laurell K.
        • Ahlgren A.
        Arterial spin-labeling perfusion MR imaging demonstrates regional CBF decrease in idiopathic normal pressure hydrocephalus.
        AJNR Am J Neuroradiol. 2017; 38 (2017/09/02): 2081-2088https://doi.org/10.3174/ajnr.A5347
        • Ziegelitz D.
        • Arvidsson J.
        • Hellstrom P.
        Pre-and postoperative cerebral blood flow changes in patients with idiopathic normal pressure hydrocephalus measured by computed tomography (CT)-perfusion.
        J Cereb Blood Flow Metab. 2016; 36 (2015/12/15): 1755-1766https://doi.org/10.1177/0271678X15608521
        • Sasaki H.
        • Ishii K.
        • Kono A.K.
        Cerebral perfusion pattern of idiopathic normal pressure hydrocephalus studied by SPECT and statistical brain mapping.
        Ann Nucl Med. 2007; 21 (2007/03/22): 39-45https://doi.org/10.1007/BF03033998
        • Miyamoto J.
        • Imahori Y.
        • Mineura K.
        Cerebral oxygen metabolism in idiopathic-normal pressure hydrocephalus.
        Neurol Res. 2007; 29 (2007/08/25): 830-834https://doi.org/10.1179/016164107X181851
        • Ishikawa M.
        • Kikuchi H.
        • Taki W.
        Regional cerebral blood flow and oxygen metabolism in normal pressure hydrocephalus after subarachnoid hemorrhage.
        Neurol Med Chir (Tokyo). 1989; 29 (1989/05/01): 382-388https://doi.org/10.2176/nmc.29.382
        • Kondziella D.
        • Sonnewald U.
        • Tullberg M.
        Brain metabolism in adult chronic hydrocephalus.
        J Neurochem. 2008; 106 (2008/04/19): 1515-1524https://doi.org/10.1111/j.1471-4159.2008.05422.x
        • de Rochefort L.
        • Liu T.
        • Kressler B.
        Quantitative susceptibility map reconstruction from MR phase data using Bayesian regularization: validation and application to brain imaging.
        Magn Reson Med. 2010; 63 (2009/12/03): 194-206https://doi.org/10.1002/mrm.22187
        • Yablonskiy D.A.
        • Sukstanskii A.L.
        • He X.
        Blood oxygenation level-dependent (BOLD)-based techniques for the quantification of brain hemodynamic and metabolic properties - theoretical models and experimental approaches.
        NMR Biomed. 2013; 26 (2012/08/29): 963-986https://doi.org/10.1002/nbm.2839
        • Cho J.
        • Kee Y.
        • Spincemaille P.
        Cerebral metabolic rate of oxygen (CMRO2 ) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level-dependent imaging (qBOLD).
        Magn Reson Med. 2018; 80 (2018/03/09): 1595-1604https://doi.org/10.1002/mrm.27135
        • Cho J.
        • Spincemaille P.
        • Nguyen T.D.
        • et al.
        Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD.
        Magn Reson Med. 2021; https://doi.org/10.1002/mrm.28875
        • Cho J.
        • Zhang S.
        • Kee Y.
        Cluster analysis of time evolution (CAT) for quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent magnitude (qBOLD)-based oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) mapping.
        Magn Reson Med. 2020; 83 (2017/09/02): 844-857https://doi.org/10.1002/mrm.27967
        • Liu T.
        • Spincemaille P.
        • de Rochefort L.
        • et al.
        Unambiguous identification of superparamagnetic iron oxide particles through quantitative susceptibility mapping of the nonlinear response to magnetic fields.
        Magn Reson Imaging. 2010; 2820100804https://doi.org/10.1016/j.mri.2010.06.011
        • Cho J.
        • Zhang J.
        • Spincemaille P.
        • et al.
        QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.
        Magn Reson Med. 2021; (2021/11/01)https://doi.org/10.1002/mrm.29057
        • Cho J.
        • Ma Y.
        • Spincemaille P.
        Cerebral oxygen extraction fraction: comparison of dual-gas challenge calibrated BOLD with CBF and challenge-free gradient echo QSM+qBOLD.
        Magn Reson Med. 2021; 85 (2020/08/13): 953-961https://doi.org/10.1002/mrm.28447
        • Cho J.
        • Lee J.
        • An H.
        Cerebral oxygen extraction fraction (OEF): comparison of challenge-free gradient echo QSM+qBOLD (QQ) with (15)O PET in healthy adults.
        J Cereb Blood Flow Metab. 2021; 41 (2020/11/28): 1658-1668https://doi.org/10.1177/0271678X20973951
        • Wu D.
        • Zhou Y.
        • Cho J.
        The spatiotemporal evolution of MRI-derived oxygen extraction fraction and perfusion in ischemic stroke.
        Front Neurosci. 2021; 15 (2016/04/12)716031https://doi.org/10.3389/fnins.2021.716031
        • Zhang S.
        • Cho J.
        • Nguyen T.D.
        Initial experience of challenge-free MRI-based oxygen extraction fraction mapping of ischemic stroke at various stages: comparison with perfusion and diffusion mapping.
        Front Neurosci. 2020; 14 (2020/10/13)535441https://doi.org/10.3389/fnins.2020.535441
        • Shen N.
        • Zhang S.
        • Cho J.
        Application of cluster analysis of time evolution for magnetic resonance imaging -derived oxygen extraction fraction mapping: a promising strategy for the genetic profile prediction and grading of glioma.
        Front Neurosci. 2021; 15 (1989/05/01)736891https://doi.org/10.3389/fnins.2021.736891
        • Cho J.
        • Nguyen T.D.
        • Huang W.
        • et al.
        Brain oxygen extraction fraction mapping in patients with multiple sclerosis.
        J Cereb Blood Flow Metab. 2021; (2021/09/25. 271678X211048031)https://doi.org/10.1177/0271678X211048031
        • Liu T.
        • Wisnieff C.
        • Lou M.
        Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping.
        Magn Reson Med. 2013; 69 (2012/04/11): 467-476https://doi.org/10.1002/mrm.24272
        • Liu T.
        • Khalidov I.
        • de Rochefort L.
        A novel background field removal method for MRI using projection onto dipole fields (PDF).
        NMR Biomed. 2011; 24 (2017/05/13): 1129-1136https://doi.org/10.1002/nbm.1670
        • Liu Z.
        • Spincemaille P.
        • Yao Y.
        MEDI+0: morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping.
        Magn Reson Med. 2018; 79 (discussion 1184-1166. 2002/02/16): 2795-2803https://doi.org/10.1002/mrm.26946
        • Liu J.
        • Liu T.
        • de Rochefort L.
        Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map.
        Neuroimage. 2012; 59 (2009/12/03): 2560-2568https://doi.org/10.1016/j.neuroimage.2011.08.082
        • Wang Y.
        • Liu T.
        Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker.
        Magn Reson Med. 2015; 73 (2017/09/02): 82-101https://doi.org/10.1002/mrm.25358
        • Dimov A.V.
        • Nguyen T.D.
        • Spincemaille P.
        • et al.
        Global cerebrospinal fluid as a zero-reference regularization for brain quantitative susceptibility mapping.
        J Neuroimaging. 2022; 3220210904https://doi.org/10.1111/jon.12923
        • Zhang J.
        • Zhou D.
        • Nguyen T.D.
        Cerebral metabolic rate of oxygen (CMRO2 ) mapping with hyperventilation challenge using quantitative susceptibility mapping (QSM).
        Magn Reson Med. 2017; 77 (discussion 1184-1166. 2002/02/16): 1762-1773https://doi.org/10.1002/mrm.26253
        • Jenkinson M.
        • Bannister P.
        • Brady M.
        • et al.
        Improved optimization for the robust and accurate linear registration and motion correction of brain images.
        Neuroimage. 2002; 17: 825-841https://doi.org/10.1006/nimg.2002.1132
        • Fischl B.
        • Salat D.H.
        • Busa E.
        Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.
        Neuron. 2002; 33 (2002/02/08): 341-355https://doi.org/10.1016/s0896-6273(02)00569-x
        • Benjamini Y.
        • Hochberg Y.
        Controlling the false discovery rate - a practical and powerful approach to multiple testing.
        J R Stat Soc B. 1995; 57: 289-300https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
        • Factora R.
        • Luciano M.
        Normal pressure hydrocephalus: diagnosis and new approaches to treatment.
        Clin Geriatr Med. 2006; 22 (2006/07/25): 645-657https://doi.org/10.1016/j.cger.2006.05.001
        • Fan A.P.
        • An H.
        • Moradi F.
        Quantification of brain oxygen extraction and metabolism with [(15)O]-gas PET: a technical review in the era of PET/MRI.
        Neuroimage. 2020; 220 (2020/07/08)117136https://doi.org/10.1016/j.neuroimage.2020.117136
        • Leenders K.L.
        • Perani D.
        • Lammertsma A.A.
        Cerebral blood flow, blood volume and oxygen utilization. Normal values and effect of age.
        Brain. 1990; 113 (2020/08/13): 27-47https://doi.org/10.1093/brain/113.1.27
        • Owler B.K.
        • Momjian S.
        • Czosnyka Z.
        Normal pressure hydrocephalus and cerebral blood flow: a PET study of baseline values.
        J Cereb Blood Flow Metab. 2004; 24 (2003/12/23): 17-23https://doi.org/10.1097/01.WCB.0000093326.88757.49
        • Villablanca J.R.
        Why do we have a caudate nucleus?.
        Acta Neurobiol Exp (Wars). 2010; 70 (2020/08/13): 95-105
        • Sakakibara R.
        • Tateno F.
        • Nagao T.
        Bladder function of patients with Parkinson's disease.
        Int J Urol. 2014; 21 (2014/02/28): 638-646https://doi.org/10.1111/iju.12421
        • Fowler C.J.
        • Griffiths D.
        • de Groat W.C.
        The neural control of micturition.
        Nat. Rev. Neurosci. 2008; 9 (2008/05/21): 453-466https://doi.org/10.1038/nrn2401
        • Alsop D.C.
        • Detre J.A.
        • Golay X.
        • et al.
        Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia.
        Magn Reson Med. 2015; 73: 102-116https://doi.org/10.1002/mrm.25197
        • Zhou L.
        • Zhang Q.
        • Spincemaille P.
        Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network.
        Magn Reson Med. 2021; 85 (2020/11/20): 2247-2262https://doi.org/10.1002/mrm.28584
        • Bambach S.
        • Smith M.
        • Morris P.P.
        • et al.
        Arterial spin labeling applications in pediatric and adult neurologic disorders.
        J Magn Reson Imaging. 2022; 5520201211https://doi.org/10.1002/jmri.27438
        • Scollato A.
        • Caini S.
        • Angelini L.
        Aqueductal CSF stroke volume measurements may drive management of shunted idiopathic normal pressure hydrocephalus patients.
        Sci Rep. 2021; 11 (2021/03/31): 7095https://doi.org/10.1038/s41598-021-86350-8
        • Scollato A.
        • Tenenbaum R.
        • Bahl G.
        Changes in aqueductal CSF stroke volume and progression of symptoms in patients with unshunted idiopathic normal pressure hydrocephalus.
        AJNR Am J Neuroradiol. 2008; 29 (2007/10/11): 192-197https://doi.org/10.3174/ajnr.A0785
        • Wang Y.
        • Ehman R.L.
        Retrospective adaptive motion correction for navigator-gated 3D coronary MR angiography.
        J Magn Reson Imaging. 2000; 11: 208-214https://doi.org/10.1002/(sici)1522-2586(200002)11:2<208::aid-jmri20>3.0.co;2-9
        • Wang Y.
        • Grist T.M.
        • Korosec F.R.
        • et al.
        Respiratory blur in 3D coronary MR imaging.
        Magn Reson Med. 1995; 33: 541-548https://doi.org/10.1002/mrm.1910330413
        • Wang Z.
        • Zhang Y.
        • Hu F.
        • et al.
        Pathogenesis and pathophysiology of idiopathic normal pressure hydrocephalus.
        CNS Neurosci Ther. 2020; 2620201126https://doi.org/10.1111/cns.13526