Dependence of the MR signal on the magnetic susceptibility of blood studied with models based on real microvascular networks

Abstract

Purpose The primary goal of this study was to estimate the value of β, the exponent in the power law relating changes of the transverse relaxation rate and intra‐extravascular local magnetic susceptibility differences as ΔnormalR2∗∝(Δχ)β. The secondary objective was to evaluate any differences that might exist in the value of β obtained using a deoxyhemoglobin‐weighted Δχ distribution versus a constant Δχ distribution assumed in earlier computations. The third objective was to estimate the value of β that is relevant for methods based on susceptibility contrast agents with a concentration of Δχ higher than that used for BOLD fMRI calculations. Methods Our recently developed model of real microvascular anatomical networks is used to extend the original simplified Monte‐Carlo simulations to compute β from the first principles. Results Our results show that β=1 for most BOLD fMRI measurements of real vascular networks, as opposed to earlier predictions of β=1.5 using uniform Δχ distributions. For perfusion or fMRI methods based on contrast agents, which generate larger values for Δχ, β=1 for normalB0≤9.4 T, whereas at 14 T β can drop below 1 and the variation across subjects is large, indicating that a lower concentration of contrast agent with a lower value of Δχ is desired for experiments at high B0. Conclusion These results improve our understanding of the relationship between R2* and the underlying microvascular properties. The findings will help to infer the cerebral metabolic rate of oxygen and cerebral blood volume from BOLD and perfusion MRI, respectively.

Microstructural plasticity in nociceptive pathways after spinal cord injury

“…13 Inflammatory processes leading to microglial activation and neurodegenerative processes may result in changes in R2*, but also to changes in metabolic demand affecting the concentration of deoxyhaemoglobin in certain brain areas also influences R2*. 33 However, although there is a complex interplay between myelin, iron accumulation and R2* values in brain pathology, remote spinal cord and brain changes following SCI are likely related to anterograde and retrograde degeneration and subsequent changes in diaschitic regions. Communication between the prefrontal cortical areas and the PAG has been recently shown in functional MRI studies.…”

Impaired cerebral vascular and metabolic responses to parametric N-back tasks in subjective cognitive decline

“…be ¼ 1.5, simulated with 2% capillary and 2% venule volume fractions, 34 was selected for this study. 24,26,27 A lower b ¼ 0.8$1 has also been suggested [35][36][37] and may have minor effects on the results. 24,34,36 For each subject, M, the BOLD calibration parameter, can be calculated by solving equation (1) with the dCBV, dCBF and dBOLD measured during the CO 2 challenge under the assumption that the CMRO 2 does not change between normocapnic and hypercapnic conditions (i.e., dCMRO 2 ¼ 0).…”

Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

“…Existing tools for the measurement of cerebral vascular dynamics rely on functional imaging tech-niques, for example, functional magnetic resonance imaging (fMRI), positive emission tomography (PET), and optical imaging [1,3]. Importantly, mathematical models have been proposed for these neuroimaging methods, which provide valuable insight into the relation between the measured signals, and the underlying physiological parameters, such as cerebral blood flow, oxygen consumption, and rate of metabolism [4][5][6][7]. These mathematical models often require a topological representation of the blood vessels as a graph of spatially distributed nodes, connected via edges [5,7].…”

“…Importantly, mathematical models have been proposed for these neuroimaging methods, which provide valuable insight into the relation between the measured signals, and the underlying physiological parameters, such as cerebral blood flow, oxygen consumption, and rate of metabolism [4][5][6][7]. These mathematical models often require a topological representation of the blood vessels as a graph of spatially distributed nodes, connected via edges [5,7]. These vascular graphs are usually estimated from two-photon microscopy (2PM) angiograms of the mouse brain [5], and segmentation of blood vessels is generally the first step in this process [8].…”