The Presence of Venous Damage and Microbleeds in Traumatic Brain Injury and the Potential Future Role of Angiographic and Perfusion Magnetic Resonance Imaging

  • E. M. HaackeEmail author
  • Waqar Raza
  • Bo Wu
  • Zhifeng Kou


Imaging plays a key role in the diagnosis and longitudinal follow up of traumatic brain injury (TBI). Among injury pathologies, vascular injury is associated with diffuse axonal injury (DAI) and traumatic axonal injury (TAI). The vascular network is ubiquitous and is an integral part of the tissue structure. In this chapter, we focus on angiographic and venographic-related imaging methods and their role in assessing mild, moderate, and severe TBI. We begin with an introduction to susceptibility weighted imaging (SWI) and magnetic resonance angiography (MRA) and then provide evidence of different types of vascular damage. Examples of TBI-induced microbleeds are presented along with the concept of low-impact medullary vein damage (MVD). This MVD has been seen even for so-called mild TBI cases. Vascular damage can also manifest as a reduction in local perfusion even when no clear macroscopic vessel damage is seen. To further understand the role of vascular abnormalities, we then introduce the different perfusion weighted imaging (PWI) techniques available and their application in TBI. The combination of SWI and PWI should make it possible to differentiate the role of local thrombus versus changes in oxygen saturation in MVD, for example. Since MRA and SWI are able to provide a full description of the brain’s vasculature in 3D, we briefly discuss the presence of finite element modeling in understanding vascular injury. We conclude with recommendations related to the use of perfusion with MRA, SWI, and oxygen saturation measurements to obtain a complete picture of the hemodynamics of the brain.


Traumatic Brain Injury Cerebral Blood Flow Magnetic Resonance Angiography Cerebral Perfusion Pressure Cerebral Blood Volume 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank Sagar Bush for Fig. 4.8, Tang Jin for Fig. 4.7, Meng Li for Fig. 4.5, and Yongquan Ye for Fig. 4.4, and to Yongquan Ye for reviewing the chapter and Liying Zhang for reviewing the section on finite element methods for studying brain trauma.

This work was supported in part by a grant from the Telemedicine and Advanced Technology Research Center (W81XWH-11-1-0493) and the National Institutes of Health, National Heart and Blood Institute (HL62983).


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of RadiologyWayne State UniversityDetroitUSA

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