Quantitative comparison between aMRI and DENSE for the assessment of brain tissue motion
Adams, A. L.; Terem, I.; Champagne, A.; Holdsworth, S. J.; Zwanenburg, J. J. M.
Show abstract
PurposeAmplified MRI (aMRI) holds potential for assessing brain tissue motion and strain, using images acquired from readily-available sequences. However, image registration is necessary to extract displacements from the motion-amplified images, which may limit its accuracy. We aimed to separately assess the errors from imperfections in the aMRI amplification, and errors from the registration algorithm, using a semi-synthetic approach. MethodsGround truth brain tissue motion was derived from smoothed Displacement Encoding with Stimulated Echoes (DENSE) measurements acquired at 7T (8 subjects). Those were then applied to a still sagittal anatomical balanced-SSFP image to obtain a DENSE-animated MRI series to which aMRI (amplification factor 10) was applied. DENSE-amplified MRI series served as a reference (Damp-MRI; amplification factor, 10). Amplified displacements were extracted from aMRI and Damp-MRI using a common registration algorithm. Linear regression was used to estimate the amplification and r2 agreement of the amplified displacements relative to the ground truth. ResultsThe estimated amplification was consistently lower for aMRI-derived displacements (range: [4.9{+/-}0.3 5.7{+/-}0.3]) than for Damp-MRI measurements (range: [6.7{+/-}0.5 7.7{+/-}0.5]). Nevertheless, the spatial, temporal and average characteristics of brain tissue motion derived from aMRI were comparable to the ground truth for Anterior-Posterior and Feet-Head displacements: (group averaged r2[≥]0.84), as were the Damp-MRI derived displacements (r2[≥]0.88). When aMRI was applied to in-vivo cine-bSSFP images and compared to the ground truth, the results were less favorable, highlighting the need for artefact-free images. ConclusionThese results strengthen the potential of aMRI as a tool for semi-quantitative assessment of brain tissue motion in disease.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Combined Angiographic, Structural and Perfusion Radial Imaging using Arterial Spin Labeling 97%
- QSM Reconstruction Challenge 2.0: a realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures 96%
- Prospective Motion Correction and Automatic Segmentation of Penetrating Arteries in Phase Contrast MRI at 7 T 96%
Similar papers in this journal
- Multi-parametric quantitative spinal cord MRI with unified signal readout and image denoising 96%
- Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity 96%
- NOise Reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing 96%
Similar papers in this journal
- An in-vivo study of BOLD laminar responses as a function of echo time and static magnetic field strength 96%
- Temporal signal-to-noise changes in combined multiband- and slice-accelerated echo-planar imaging with a 20- and 64-channel coil 96%
- Reproducibility of 4D Flow MRI-based Personalized Cardiovascular Models; Inter-sequence, Intra-observer, and Inter-observer variability 95%
Similar papers in this journal
Similar papers in this journal
- Deep-Learning-Based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient Recalled Echo (qGRE) MRI metrics associated with Human Brain Neuronal Structure and Hemodynamic Properties 95%
- Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data 95%
- Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.