The influence of cardiac activity on the viscoelastic properties of intracranial tissue is one of the mechanisms through which brain–heart interactions take place, and is implicated in cerebrovascular disease. Cerebrovascular disease risk is not fully explained by current risk factors, including arterial compliance. Cerebrovascular compliance is currently estimated indirectly through Doppler sonography and magnetic resonance imaging (MRI) measures of blood velocity changes. In order to meet the need for novel cerebrovascular disease risk factors, we aimed to design and validate an MRI indicator of cerebrovascular compliance based on direct endogenous measures of blood volume changes. We implemented a fast non-gated two-dimensional MRI pulse sequence based on echo-planar imaging (EPI) with ultra-short repetition time (approx. 30–50 ms), which stepped through slices every approximately 20 s. We constrained the solution of the Bloch equations for spins moving faster than a critical speed to produce an endogenous contrast primarily dependent on spin volume changes, and an approximately sixfold signal gain compared with Ernst angle acquisitions achieved by the use of a 90° flip angle. Using cardiac and respiratory peaks detected on physiological recordings, average cardiac and respiratory MRI pulse waveforms in several brain compartments were obtained at 7 Tesla, and used to derive a compliance indicator, the pulsatility volume index (pVI). The pVI, evaluated in larger cerebral arteries, displayed significant variation within and across vessels. Multi-echo EPI showed the presence of significant pulsatility effects in both S0 and signals, compatible with blood volume changes. Lastly, the pVI dynamically varied during breath-holding compared with normal breathing, as expected for a compliance indicator. In summary, we characterized and performed an initial validation of a novel MRI indicator of cerebrovascular compliance, which might prove useful to investigate brain–heart interactions in cerebrovascular disease and other disorders.
One contribution of 16 to a theme issue ‘Uncovering brain–heart information through advanced signal and image processing’.
- Accepted October 26, 2015.
- © 2016 The Author(s)
Published by the Royal Society. All rights reserved.