Murphy, Matthew C.

PurposeTo develop a novel magnetic resonance elastography (MRE) acquisition using a hybrid radial EPI readout scheme (TURBINE), and to demonstrate its feasibility to obtain wave images and stiffness maps in a phantom and in vivo brain.MethodThe proposed 3D TURBINE‐MRE is based on a spoiled gradient‐echo MRE sequence with the EPI readout radially rotating about the phase‐encoding axis to sample a full 3D k‐space. A polyvinyl chloride phantom and 6 volunteers were scanned on a compact 3T GE scanner with a 32‐channel head coil at 80 Hz and 60 Hz external vibration, respectively. For comparison, a standard 2D, multislice, spin‐echo (SE) EPI‐MRE acquisition was also performed with the same motion encoding and resolution. The TURBINE‐MRE images were off‐line reconstructed with iterative SENSE algorithm. The regional ROI analysis was performed on the 6 volunteers, and the median stiffness values were compared between SE‐EPI‐MRE and TURBINE‐MRE.ResultsThe 3D wave‐field images and the generated stiffness maps were comparable between TURBINE‐MRE and standard SE‐EPI‐MRE for the phantom and the volunteers. The Bland–Altman plot showed no significant difference in the median regional stiffness values between the two methods. The stiffness measured with the 2 methods had a strong linear relationship with a Pearson correlation coefficient of 0.943.ConclusionWe demonstrated the feasibility of the new TURBINE‐MRE sequence for acquiring the desired 3D wave‐field data and stiffness maps in a phantom and in‐vivo brains. This pilot study encourages further exploration of TURBINE‐MRE for functional MRE, free‐breathing abdominal MRE, and cardiac MRE applications.

ObjectivesThe aim of this study was to perform a whole-brain analysis of alterations in brain mechanical properties due to normal pressure hydrocephalus (NPH).Materials and MethodsMagnetic resonance elastography (MRE) examinations were performed on 85 participants, including 44 cognitively unimpaired controls, 33 with NPH, and 8 who were amyloid-positive with Alzheimer clinical syndrome. A custom neural network inversion was used to estimate stiffness and damping ratio from patches of displacement data, accounting for edges by training the network to estimate the mechanical properties in the presence of missing data. This learned inversion was first compared with a standard analytical approach in simulation experiments and then applied to the in vivo MRE measurements. The effect of NPH on the mechanical properties was then assessed by voxel-wise modeling of the stiffness and damping ratio maps. Finally, a pattern analysis was performed on each individual's mechanical property maps by computing the correlation between each person's maps with the expected NPH effect. These features were used to fit a classifier and assess diagnostic accuracy.ResultsThe voxel-wise analysis of the in vivo mechanical property maps revealed a unique pattern in participants with NPH, including a concentric pattern of stiffening near the dural surface and softening near the ventricles, as well as decreased damping ratio predominantly in superior regions of the white matter (family-wise error correctedP< 0.05 at cluster level). The pattern of viscoelastic changes in each participant predicted NPH status in this cohort, separating participants with NPH from the control and the amyloid-positive with Alzheimer clinical syndrome groups, with areas under the receiver operating characteristic curve of 0.999 and 1, respectively.ConclusionsThis study provides motivation for further development of the neural network inversion framework and demonstrates the potential of MRE as a novel tool to diagnose NPH and provide a window into its pathogenesis.

PurposeTo investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data.MethodsArtificial neural networks were fit using model‐based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods.ResultsIn 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated with DI results in the liver (R2 = 0.974) and in the brain (R2 = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel‐wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing.ConclusionArtificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med 80:351–360, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

PurposeTo investigate the feasibility of utilizing brain stiffness as a potential biomarker for behavioral variant frontotemporal dementia (bvFTD) patients. Magnetic resonance elastography (MRE) is a noninvasive technique for evaluating the mechanical properties of brain tissue in vivo. MRE has demonstrated decreased brain stiffness in patients with Alzheimer's disease.Materials and MethodsWe examined five male subjects with bvFTD and nine cognitively normal age‐matched male controls (NC) with brain 3T MRE. Stiffness was calculated in nine regions of interest (ROIs): whole brain (entire cerebrum excluding cerebellum), frontal lobes, occipital lobes, parietal lobes, temporal lobes, deep gray matter / white matter (GM/WM; insula, deep gray nuclei and white matter tracts), cerebellum, sensorimotor cortex (pre‐ and postcentral gyri), and a composite region labeled FT (frontal and temporal lobes excluding the pre‐ and postcentral gyri).ResultsSignificantly lower stiffness values were observed in the whole brain (P = 0.007), frontal lobe (P = 0.001), and temporal lobes (P = 0.005) of bvFTD patients compared to NC. No significant stiffness differences were observed in any other ROIs of bvFTD patients compared to NC (P > 0.05). These results demonstrate that statistically significant brain softening occurs in the frontal and temporal lobes of bvFTD patients, which corresponds to the expected pathophysiology of bvFTD.ConclusionFuture studies evaluating the feasibility of brain MRE for early disease detection and monitoring disease progression could shed new insights into understanding the mechanisms involved in bvFTD. J. Magn. Reson. Imaging 2016;43:474–478.

PurposeTo investigate the feasibility of utilizing brain stiffness as a potential biomarker for behavioral variant frontotemporal dementia (bvFTD) patients. Magnetic resonance elastography (MRE) is a noninvasive technique for evaluating the mechanical properties of brain tissue in vivo. MRE has demonstrated decreased brain stiffness in patients with Alzheimer's disease.Materials and MethodsWe examined five male subjects with bvFTD and nine cognitively normal age‐matched male controls (NC) with brain 3T MRE. Stiffness was calculated in nine regions of interest (ROIs): whole brain (entire cerebrum excluding cerebellum), frontal lobes, occipital lobes, parietal lobes, temporal lobes, deep gray matter / white matter (GM/WM; insula, deep gray nuclei and white matter tracts), cerebellum, sensorimotor cortex (pre‐ and postcentral gyri), and a composite region labeled FT (frontal and temporal lobes excluding the pre‐ and postcentral gyri).ResultsSignificantly lower stiffness values were observed in the whole brain (P = 0.007), frontal lobe (P = 0.001), and temporal lobes (P = 0.005) of bvFTD patients compared to NC. No significant stiffness differences were observed in any other ROIs of bvFTD patients compared to NC (P > 0.05). These results demonstrate that statistically significant brain softening occurs in the frontal and temporal lobes of bvFTD patients, which corresponds to the expected pathophysiology of bvFTD.ConclusionFuture studies evaluating the feasibility of brain MRE for early disease detection and monitoring disease progression could shed new insights into understanding the mechanisms involved in bvFTD. J. Magn. Reson. Imaging 2016;43:474–478.