High resolution atlasing of the venous brain vasculature from 7T quantitative susceptibility

The vascular organization of the human brain can determine neurological and neurophysiological functions, yet thus far it has not been comprehensively mapped. Aging and diseases such as dementia are known to be associated with changes to the vasculature and normative data could help detect these vascular changes in neuroimaging studies. Furthermore, given the well-known impact of venous vessels on the blood oxygen level dependent (BOLD) signal, information about the common location of veins could help detect biases in existing datasets. In this work, a quantitative atlas of the venous vasculature using quantitative susceptibility maps (QSM) acquired with a 0.6 mm isotropic resolution is presented. The Venous Neuroanatomy (VENAT) atlas was created from 5 repeated 7 Tesla MRI measurements in young and healthy volunteers (n = 20, 10 females, mean age = 25.1 ± 2.5 years) using a two-step registration method on 3D segmentations of the venous vasculature. This cerebral vein atlas includes the average vessel location, diameter (mean: 0.84 ± 0.33 mm) and curvature (0.11 ± 0.05 mm−1) from all participants and provides an in vivo measure of the angio-architectonic organization of the human brain and its variability. This atlas can be used as a basis to understand changes in the vasculature during aging and neurodegeneration, as well as vascular and physiological effects in neuroimaging.

[1]  A. Villringer,et al.  Dural sinus thrombosis: value of venous MR angiography for diagnosis and follow-up. , 1994, AJR. American journal of roentgenology.

[2]  Satoru Miyauchi,et al.  Circulatory basis of fMRI signals: relationship between changes in the hemodynamic parameters and BOLD signal intensity , 2004, NeuroImage.

[3]  Nikos K Logothetis,et al.  Interpreting the BOLD signal. , 2004, Annual review of physiology.

[4]  J. Guralnik,et al.  In Vivo Imaging of Venous Side Cerebral Small-Vessel Disease in Older Adults: An MRI Method at 7T , 2017, American Journal of Neuroradiology.

[5]  H. Duvernoy,et al.  Cortical blood vessels of the human brain , 1981, Brain Research Bulletin.

[6]  M. A. Bell,et al.  Laminar variation in the microvascular architecture of normal human visual cortex (area 17) , 1985, Brain Research.

[7]  A. Kramer,et al.  Exercise, brain, and cognition across the life span. , 2011, Journal of applied physiology.

[8]  Daniel B. Vigneron,et al.  Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases , 2008, NeuroImage.

[9]  Arno Villringer,et al.  Vessel segmentation from quantitative susceptibility maps for local oxygenation venography , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[10]  Stephen C Cunnane,et al.  The morphology of the human cerebrovascular system , 2018, Human brain mapping.

[11]  R. Peters,et al.  Ageing and the brain , 2006, Postgraduate Medical Journal.

[12]  F. Ye,et al.  Increasing striatal iron content associated with normal aging , 1998, Movement disorders : official journal of the Movement Disorder Society.

[13]  Max C. Keuken,et al.  Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI , 2014, NeuroImage.

[14]  J. R. Baker,et al.  The intravascular contribution to fmri signal change: monte carlo modeling and diffusion‐weighted studies in vivo , 1995, Magnetic resonance in medicine.

[15]  Daniel Hershey,et al.  Blood Oxygenation , 1970, Springer US.

[16]  V. Challa,et al.  Venous collagenosis and arteriolar tortuosity in leukoaraiosis , 2002, Journal of the Neurological Sciences.

[17]  A. Villringer,et al.  Heparin treatment in sinus venous thrombosis , 1991, The Lancet.

[18]  Anna Devor,et al.  Quantifying the Microvascular Origin of BOLD-fMRI from First Principles with Two-Photon Microscopy and an Oxygen-Sensitive Nanoprobe , 2015, The Journal of Neuroscience.

[19]  Robert Turner,et al.  How Much Cortex Can a Vein Drain? Downstream Dilution of Activation-Related Cerebral Blood Oxygenation Changes , 2002, NeuroImage.

[20]  Jeff H. Duyn,et al.  High-field MRI of brain cortical substructure based on signal phase , 2007, Proceedings of the National Academy of Sciences.

[21]  Tobias Kober,et al.  MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.

[22]  Yi Wang,et al.  Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker , 2014, Magnetic resonance in medicine.

[23]  T. Miyawaki,et al.  Developmental characteristics of vessel density in the human fetal and infant brains. , 1998, Early human development.

[24]  Ferdinand Schweser,et al.  Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study , 2012, NeuroImage.

[25]  Roel H. R. Deckers,et al.  Quantifying the spatial resolution of the gradient echo and spin echo BOLD response at 3 Tesla , 2005, Magnetic resonance in medicine.

[26]  Gary F. Egan,et al.  Combining images and anatomical knowledge to improve automated vein segmentation in MRI , 2017, NeuroImage.

[27]  Lawrence L. Wald,et al.  Fast quantitative susceptibility mapping with L1‐regularization and automatic parameter selection , 2013, Magnetic resonance in medicine.

[28]  Robert Turner,et al.  Toward in vivo histology: A comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2 ⁎-imaging at ultra-high magnetic field strength , 2013, NeuroImage.

[29]  工藤與亮,et al.  Quantitative Susceptibility Mapping , 2014 .

[30]  Pascal Spincemaille,et al.  Reproducibility of quantitative susceptibility mapping in the brain at two field strengths from two vendors , 2015, Journal of magnetic resonance imaging : JMRI.

[31]  Arvind P. Pathak,et al.  Three-Dimensional Imaging of the Mouse Neurovasculature with Magnetic Resonance Microscopy , 2011, PloS one.

[32]  Jan Modersitzki,et al.  FLIRT: A Flexible Image Registration Toolbox , 2003, WBIR.

[33]  Yu-Chung N. Cheng,et al.  Susceptibility weighted imaging (SWI) , 2004, Zeitschrift fur medizinische Physik.

[34]  Roland N. Boubela,et al.  fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions , 2015, Scientific Reports.

[35]  Meenakshi Rajeev,et al.  Regional Variations , 2021, Political Populism.

[36]  Kaleem Siddiqi,et al.  Flux driven automatic centerline extraction , 2005, Medical Image Anal..

[37]  Xiaoliang Wang,et al.  Geometric properties estimation from discrete curves using discrete derivatives , 2011, Comput. Graph..

[38]  Alberto Gatti,et al.  The role of iron and copper molecules in the neuronal vulnerability of locus coeruleus and substantia nigra during aging. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Richard Arwed Pfeifer,et al.  Die Angioarchitektonik der Großhirnrinde , 1927 .

[40]  D Purves,et al.  Specialized vascularization of the primate visual cortex , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[41]  Arthur F Kramer,et al.  Exercise, cognition, and the aging brain. , 2006, Journal of applied physiology.

[42]  Essa Yacoub,et al.  High-Field fMRI for Human Applications: An Overview of Spatial Resolution and Signal Specificity , 2011, The open neuroimaging journal.

[43]  Robert Turner,et al.  Myelin and iron concentration in the human brain: A quantitative study of MRI contrast , 2014, NeuroImage.

[44]  Himanshu Bhat,et al.  Quantitative oxygenation venography from MRI phase , 2014, Magnetic resonance in medicine.

[45]  MCh S. S. Baldawa MS,et al.  Susceptibility-Weighted Imaging , 2012 .

[46]  A. Villringer,et al.  DIAGNOSIS OF SUPERIOR SAGITTAL SINUS THROMBOSIS BY THREE-DIMENSIONAL MAGNETIC RESONANCE FLOW IMAGING , 1989, The Lancet.

[47]  Hans J. Johnson,et al.  Advanced Normalization Tools (ANTs) , 2020 .

[48]  A G Webb,et al.  Practical improvements in the design of high permittivity pads for dielectric shimming in neuroimaging at 7T. , 2016, Journal of magnetic resonance.

[49]  A. Haase,et al.  FLASH imaging: rapid NMR imaging using low flip-angle pulses. 1986. , 1986, Journal of magnetic resonance.

[50]  W. Brown,et al.  Review: Cerebral microvascular pathology in ageing and neurodegeneration , 2011, Neuropathology and applied neurobiology.

[51]  Ferdinand Schweser,et al.  SHARP edges: Recovering cortical phase contrast through harmonic extension , 2015, Magnetic resonance in medicine.

[52]  Karl Rohr,et al.  A New 3D Parametric Intensity Model for Accurate Segmentation and Quantification of Human Vessels , 2004, MICCAI.

[53]  Maxime Descoteaux,et al.  Regional variations in vascular density correlate with resting‐state and task‐evoked blood oxygen level‐dependent signal amplitude , 2014, Human brain mapping.

[54]  P. Scheltens,et al.  A New Rating Scale for Age-Related White Matter Changes Applicable to MRI and CT , 2001, Stroke.

[55]  Peter R Luijten,et al.  Blood oxygenation level‐dependent (BOLD) total and extravascular signal changes and ΔR2* in human visual cortex at 1.5, 3.0 and 7.0 T , 2011, NMR in biomedicine.

[56]  A. Towbin The Syndrome of Latent Cerebral Venous Thrombosis: Its Frequency and Relation to Age and Congestive Heart Failure , 1973, Stroke.

[57]  Phillip G. D. Ward,et al.  Combining images and anatomical knowledge to improve automated vein segmentation in MRI , 2017 .