Diffusion Magnetic Resonance Imaging (dMRI) is an imaging technique with exquisite sensitivity to the microstructural properties of heterogeneous media. The conventionally adopted acquisition schemes involving single pulsed field gradients encode the random motion of water molecules into the NMR signal, however typically conflating the effects of different sources contributing to the water motion. Time-varying magnetic field gradients have recently been considered for disentangling such effects during the data encoding phase, opening to the possibility of adding specificity to the recovered information about the medium’s microstructure. Such data is typically represented via a diffusion tensor distribution (DTD) model, thus assuming the existence of several non-exchanging compartments in each of which diffusion is unrestricted. In this work, we consider a model that takes confinement into account and possesses a diffusion time-dependence closer to that of restricted diffusion, to replace the free diffusion assumption in multidimensional diffusion MRI methods. We first demonstrate how the confinement tensor model captures the relevant signal modulations impressed by water diffusing in both free and closed spaces, for data simulated with a clinically feasible protocol involving time-varying magnetic field gradients. Then, we provide the basis for incorporating this model into two multidimensional dMRI methods, and attempt to recover a confinement tensor distribution (CTD) on a human brain dataset.
Diffusion Magnetic Resonance Imaging (dMRI) is an imaging technique with exquisite sensitivity to the microstructural properties of heterogeneous media. The conventionally adopted acquisition schemes involving single pulsed field gradients encode the random motion of water molecules into the NMR signal, however typically conflating the effects of different sources contributing to the water motion. Time-varying magnetic field gradients have recently been considered for disentangling such effects during the data encoding phase, opening to the possibility of adding specificity to the recovered information about the medium's microstructure. Such data is typically represented via a diffusion tensor distribution (DTD) model, thus assuming the existence of several non-exchanging compartments in each of which diffusion is unrestricted. In this work, we consider a model that takes confinement into account and possesses a diffusion time-dependence closer to that of restricted diffusion, to replace the free diffusion assumption in multidimensional diffusion MRI methods. We first demonstrate how the confinement tensor model captures the relevant signal modulations impressed by water diffusing in both free and closed spaces, for data simulated with a clinically feasible protocol involving time-varying magnetic field gradients. Then, we provide the basis for incorporating this model into two multidimensional dMRI methods, and attempt to recover a confinement tensor distribution (CTD) on a human brain dataset.
The diffusion propagator fully characterizes the diffusion process, which is highly sensitive to the confining boundaries and the structure within enclosed pores. While magnetic resonance has extensively been used to observe various features of the diffusion process, its full characterization has been elusive. Here, we address this challenge by employing a special sequence of magnetic field gradient pulses for measuring the diffusion propagator, which allows for “listening to the drum,” mapping structural dispersity, and determining not only the pore’s shape but also diffusive dynamics within it.
Q-space trajectory imaging (QTI) characterizes microstructures through the statistical moments of the diffusion tensor distribution. A constrained estimation framework named QTI+ was recently proposed to achieve mathematically and physically acceptable estimates of these moments. Here we consider expanding QTI+ with a new set of conditions based on the theoretical maximum value of water diffusivity. We show where these conditions are violated for different bulk diffusivity values, and how the new constraints affect the QTI scalar maps. The results show violations occurring almost exclusively in voxels containing gray matter and CSF. Imposing the constraints produces metrics closer to the expected values.
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