ArticleMagnetic resonance in medicine2018
Artificial neural networks for stiffness estimation in magnetic resonance elastography.
Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Stress-Constrained Physics-Informed UNet for Voxel-Wise Multiparameter Hyperelastic Inversion in Volumetric Medical Imaging.Annals of biomedical engineering · 2026Article
- Deep learning-driven inversion framework for shear modulus estimation in magnetic resonance elastography.Magma (New York, N.Y.) · 2026Article
- Article
- MR elastography datasets including phantom, liver, and brain.Scientific data · 2025Article
- Estimating shear modulus of isotropic materials from scanning laser Doppler vibrometry via convolutional neural networks.Journal of the mechanical behavior of biomedical materials · 2025Article
- Mechanical characterization of brain tissue: experimental techniques, human testing considerations, and perspectives.Acta biomaterialia · 2025Review
- Automated liver magnetic resonance elastography quality control and liver stiffness measurement using deep learning.Abdominal radiology (New York) · 2025Article
- Elastographic magnetization prepared imaging with rapid encoding.Magnetic resonance in medicine · 2025Article
- MR elastography-based detection of impaired skull-brain mechanical decoupling performance in response to repetitive head impacts.European radiology · 2025Article
- Improving the Accuracy and Repeatability of Renal Stiffness Measurement With Magnetic Resonance Elastography: Influence of Vibration Frequency, Acquisition Orientation, and Postprocessing-A Phantom and Volunteer Study.NMR in biomedicine · 2025Article
- Lesion Classification by Model-Based Feature Extraction: A Differential Affine Invariant Model of Soft Tissue Elasticity in CT Images.Journal of imaging informatics in medicine · 2025Article
- Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- The contributions of relative brain viscosity to brain function and health.Brain communications · 2024Review
- Clinical application of magnetic resonance elastography in pediatric neurological disorders.Pediatric radiology · 2023Review
- Data-driven electrical conductivity brain imaging using 3 T MRI.Human brain mapping · 2023Article
- Physics-Informed Neural Networks for Tissue Elasticity Reconstruction in Magnetic Resonance Elastography.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Features of Idiopathic Intracranial Hypertension on MRI With MR Elastography: Prospective Comparison With Control Individuals and Assessment of Postintervention Changes.AJR. American journal of roentgenology · 2022Article
- OSCILLATE: A low-rank approach for accelerated magnetic resonance elastography.Magnetic resonance in medicine · 2022Article
- Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Impact of material homogeneity assumption on cortical stiffness estimates by MR elastography.Magnetic resonance in medicine · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
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 (R
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.
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