A simulation tool for dynamic contrast enhanced MRI.

International audienceThe quantification of bolus-tracking MRI techniques remains challenging. The acquisition usually relies on one contrast and the analysis on a simplified model of the various phenomena that arise within a voxel, leading to inaccurate perfusion estimates. To evaluate how simplifications in the interstitial model impact perfusion estimates, we propose a numerical tool to simulate the MR signal provided by a dynamic contrast enhanced (DCE) MRI experiment. Our model encompasses the intrinsic R1 and R2 relaxations, the magnetic field perturbations induced by susceptibility interfaces (vessels and cells), the diffusion of the water protons, the blood flow, the permeability of the vessel wall to the the contrast agent (CA) and the constrained diffusion of the CA within the voxel. The blood compartment is modeled as a uniform compartment. The different blocks of the simulation are validated and compared to classical models. The impact of the CA diffusivity on the permeability and blood volume estimates is evaluated. Simulations demonstrate that the CA diffusivity slightly impacts the permeability estimates (< 5% for classical blood flow and CA diffusion). The effect of long echo times is investigated. Simulations show that DCE-MRI performed with an echo time TE = 5 ms may already lead to significant underestimation of the blood volume (up to 30% lower for brain tumor permeability values). The potential and the versatility of the proposed implementation are evaluated by running the simulation with realistic vascular geometry obtained from two photons microscopy and with impermeable cells in the extravascular environment. In conclusion, the proposed simulation tool describes DCE-MRI experiments and may be used to evaluate and optimize acquisition and processing strategies

Neuronal transport defects of the MAP6 KO mouse - a model of schizophrenia - and alleviation by Epothilone D treatment, as observed using MEMRI.

International audienceThe MAP6 (microtubule-associated protein 6) KO mouse is a microtubule-deficient model of schizophrenia that exhibits severe behavioral disorders that are associated with synaptic plasticity anomalies. These defects are alleviated not only by neuroleptics, which are the gold standard molecules for the treatment of schizophrenia, but also by Epothilone D (Epo D), which is a microtubule-stabilizing molecule. To compare the neuronal transport between MAP6 KO and wild-type mice and to measure the effect of Epo D treatment on neuronal transport in KO mice, MnCl2 was injected in the primary somatosensory cortex. Then, using manganese-enhanced magnetic resonance imaging (MEMRI), we followed the propagation of Mn(2+) through axonal tracts and brain regions that are connected to the somatosensory cortex. In MAP6 KO mice, the measure of the MRI relative signal intensity over 24h revealed that the Mn(2+) transport rate was affected with a stronger effect on long-range and polysynaptic connections than in short-range and monosynaptic tracts. The chronic treatment of MAP6 KO mice with Epo D strongly increased Mn(2+) propagation within both mono- and polysynaptic connections. Our results clearly indicate an in vivo deficit in neuronal Mn(2+) transport in KO MAP6 mice, which might be due to both axonal transport defects and synaptic transmission impairments. Epo D treatment alleviated the axonal transport defects, and this improvement most likely contributes to the positive effect of Epo D on behavioral defects in KO MAP6 mice

Change in the MR signal for different and values.

<p>(a) S(t) at for 3 values: , and with . (b) S(t) at for 7 values: , , , , , and with .</p

Error on the permeability estimate.

<p>When modeling the outputs of blocks b and c with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057636#pone.0057636.e209" target="_blank">Eq.[14]</a> for various and values: (a) Error on when modeling . (b) Error on when modeling S(t) for with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057636#pone.0057636.e241" target="_blank">Eqs.[16</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057636#pone.0057636.e242" target="_blank">17]</a>.</p

Illustration of the evolution of the concentration of CA.

<p>CA concentration in the vessels (a) and the corresponding MR signal (b). is simulated for 2 echo times: (black) and (grey). The change in CA concentration , represented by the lattices, and in the magnetic field perturbations are presented at five times points labeled (1) to (5). For this longer echo time, one can observe the competition between the susceptibility effect which decreases the signal (point (2)) and the enhancement produced by the relaxation effect of the CA which extravasates into the tissue (points (3) to (5)). At the last simulation time point () (5), is lower than (not shown) and the concentration in the extravascular space begins to decrease. Note the log scale for introduced for sake of clarity.</p

Algorithm sketch of the simulation.

<p>Only the most important parameters have been represented. Data on the left of the gray boxes are inputs to the model. Data on the right are outputs of the simulation. The simulation is organized in three blocks. Block (a) initializes the geometry. Block (b) describes the CA behavior over time. Block (c) estimates the MR signal.</p

Impact of various magnetic field computations on the FID simulation.

<p>(a) 1 vessel in 1 orientation (b) N vessels in 1 orientation (c) N vessels in 3 orientations (d) N vessels in 3D. The vessel arrangement is presented in 3D and for display, the magnetic field perturbation is only presented on each face of the cube but is computed in 3D. (e) Normalized FID for approaches (a)–(d) (averaged across the geometries for approaches (b–d)).</p

Illustration of the weighting lattices and .

<p>(a) Zoom in the diffusion weighting lattice . The diffusion appears restricted near the membranes. (b) Illustration of the geometry lattices. In red, the vessel, in grey the cells. (c) Zoom in the surface weighting lattice that computes the number of contact exchange interfaces between a vessel and its periphery.</p