Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI

Abstract

In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T, T and T relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T, T and T changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.

Accelerated whole‐brain perfusion imaging using a simultaneous multislice spin‐echo and gradient‐echo sequence with joint virtual coil reconstruction

“…This acquisition demonstrates a tradeoff between higher in‐plane resolution and temporal resolution with shorter TEs and thinner slices. Isotropic resolution whole‐brain data sets can greatly benefit DSC‐MRI by decreasing slice thickness to reduce partial‐volume effects, or by lowering the in‐plane resolution to shorten the TE and improve SNR and arterial input function determination …”

“…While DSC examines perfusion dynamics, DCE examines the leakage of contrast across the blood brain barrier over a period of time after contrast injection, to derive measures such as the transfer coefficient (K trans ) and to assess tumor permeability. Dynamic susceptibility contrast and DCE protocols are typically acquired in 2 separate scans as a result of somewhat competing imaging requirements, with DSC necessitating an acquisition that can provide the transverse relaxation time (T 2 /T 2 *) contrast weighting and DCE requiring T 1 contrast weighting . However, the data from this sequence can be used to decouple the T 2 * and T 1 signal components by extrapolating to TE = 0, providing a T 1 ‐weighted signal time series that can be used to find permeability parameters from DCE analysis.…”

“…Dynamic susceptibility contrast and DCE protocols are typically acquired in 2 separate scans as a result of somewhat competing imaging requirements, with DSC necessitating an acquisition that can provide the transverse relaxation time (T 2 /T 2 *) contrast weighting and DCE requiring T 1 contrast weighting . However, the data from this sequence can be used to decouple the T 2 * and T 1 signal components by extrapolating to TE = 0, providing a T 1 ‐weighted signal time series that can be used to find permeability parameters from DCE analysis. This eliminates 2 separate injections of Gd contrast agent to achieve both perfusion and permeability information.…”

Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)‐MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T

“…Most DSC‐MRI studies have focused on cerebral blood volume (CBV) as a potential diagnostic, prognostic, and therapeutic biomarker; more advanced DSC analysis can provide additional hemodynamic biomarkers, including cerebral blood flow (CBF), mean transit time (MTT), and capillary transit time heterogeneity (CTH). While CBV primarily reflects vascular density, these more advanced biomarkers reflect complementary features such as variations in microvascular flow patterns . While these may be of particular clinical interest across many neurological disorders, they have yet to be widely incorporated into clinical use, in part due to the more advanced postprocessing required and increased sensitivity to noise.…”

“…While CBV primarily reflects vascular density, these more advanced biomarkers reflect complementary features such as variations in microvascular flow patterns. 6 While these may be of particular clinical interest across many neurological disorders, they have yet to be widely incorporated into clinical use, in part due to the more advanced postprocessing required and increased sensitivity to noise. More specifically, these biomarkers rely on calculations of a tissue residue function (ie, the fraction of the contrast remaining in the tissue following bolus injection) and thus require more complex postprocessing, such as a deconvolution or Bayesian modeling.…”