Quantitative BOLD: The effect of diffusion

Abstract

Purpose: To make the quantitative blood oxygenation level-dependent (qBOLD) method more suitable for clinical application by accounting for proton diffusion and reducing acquisition times. Materials and Methods:Monte Carlo methods are used to simulate the signal from diffusing protons in the presence of a blood vessel network. A diffusive qBOLD model was then constructed using a lookup table of the results. Acquisition times are reduced by parallel imaging and by employing an integrated fieldmapping method, rather than running an additional sequence.Results: The addition of diffusion to the model is shown to have a significant impact on predicted signal formation. This is found to affect all fitted parameters when the model is applied to real data. Parallel imaging and integrated fieldmapping allowed the GESSE (gradient echo sampling of a spin echo) acquisition to be made in less than 10 minutes while maintaining high signal-to-noise ratio (SNR). Conclusion:By incorporating integrated field mapping and parallel imaging techniques, GESSE data were acquired within clinically acceptable acquisition times. These data fit closely to the diffusive qBOLD model, providing more realistic and robust measurements of T 2 and blood oxygenation than the static model.

The physics of functional magnetic resonance imaging (fMRI)

“…For a susceptibility difference of Δχ 0 = 0.264 × 10 −6 [115], a typical hematocrit of 0.44 (normal range is about 0.42–0.52 for adult males and 0.35–0.47 for adult females), and a baseline venous saturation of Y = 0.6, a typical value of δω 0 is ~230 rad s −1 for B 0 = 3 T. In their seminal paper modeling the BOLD effect, Ogawa and co-workers expressed δω 0 as an equivalent frequency ν (in Hz, rather than rad s −1 , so ν = δω 0 /2π), which would be ~37 Hz for this example. This estimate for a field strength of 3 T is consistent with assumptions in two recent models [113, 114], but lower values have been assumed in other models based on either lower assumed values of Hct, Y or Δχ 0 [116, 117]. Because of physiological variability in the hematocrit value in the normal healthy population, this basic parameter could vary in practice by up to 30%, so there is no definitive value that can be assumed to be accurate for all subjects.…”

“…Following the work of Yabonskiy and co-workers, this general approach is usually described under the label qBOLD. Several authors have extended the qBOLD method with sophisticated modeling approaches to dealing with the other factors affecting the signal decay curve (including intravascular effects and diffusion effects) [110, 117, 167, 218]. The full model for the decay curve has become somewhat complex, with a number of parameters describing various physiological effects.…”

Is T2* Enough to Assess Oxygenation? Quantitative Blood Oxygen Level–Dependent Analysis in Brain Tumor

“…Baseline T2* has also been shown to have high sensitivity in the defi nition of tumor hypoxia in human prostate tumor ( 6,7 ). Second, some assumptions of the mathematic model, such as restricted water diffusion or homogeneous vascular geometry, may induce bias in the MR estimates of SO 2 estimates ( 23,26 ). The main source of error in the MR estimation of SO 2 remains the presence of nonvascular magnetic susceptibility sources in the voxel.…”

Imaging Brain Oxygenation with MRI Using Blood Oxygenation Approaches: Methods, Validation, and Clinical Applications

“…He and Yablonskiy 17 refined the model by considering signal from gray matter, white matter, CSF, and blood as well as the effect of water diffusion. 18,19 One interesting remark about these methods is that the CBV and blood oxygen saturation are observed through dephasing effects caused by the presence of deoxyhemoglobin. Therefore, if one assumes fully oxygenated arterial blood, the values that are derived represent only the venous and capillary portions of the vasculature.…”