ArticleMedical image analysis2020
Artificial neural networks for magnetic resonance elastography stiffness estimation in inhomogeneous materials.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
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- Deep learning-driven inversion framework for shear modulus estimation in magnetic resonance elastography.Magma (New York, N.Y.) · 2026Article
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- MR elastography datasets including phantom, liver, and brain.Scientific data · 2025Article
- Automated liver magnetic resonance elastography quality control and liver stiffness measurement using deep learning.Abdominal radiology (New York) · 2025Article
- Mechanical characterization of brain tissue: experimental techniques, human testing considerations, and perspectives.Acta biomaterialia · 2025Review
- Evaluation of MR Elastography as a Noninvasive Diagnostic Test for Spontaneous Intracranial Hypotension.AJNR. American journal of neuroradiology · 2024Article
- Mechanics of Small-Scale Spherical Inclusions Using Nonlocal Poroelasticity Integrated with Light Gradient Boosting Machine.Micromachines · 2024Article
- Clinical application of magnetic resonance elastography in pediatric neurological disorders.Pediatric radiology · 2023Review
- Reverberant magnetic resonance elastographic imaging using a single mechanical driver.Physics in medicine and biology · 2023Article
- OSCILLATE: A low-rank approach for accelerated magnetic resonance elastography.Magnetic resonance in medicine · 2022Article
- Impact of material homogeneity assumption on cortical stiffness estimates by MR elastography.Magnetic resonance in medicine · 2022Article
- Numerical simulation of wave propagation through interfaces using the extended finite element method for magnetic resonance elastography.The Journal of the Acoustical Society of America · 2022Article
- MR elastography: Principles, guidelines, and terminology.Magnetic resonance in medicine · 2021Review
- Harnessing brain waves: a review of brain magnetic resonance elastography for clinicians and scientists entering the field.The British journal of radiology · 2021Review
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo test the hypothesis that removing the assumption of material homogeneity will improve the spatial accuracy of stiffness estimates made by Magnetic Resonance Elastography (MRE).
methodsAn artificial neural network was trained using synthetic wave data computed using a coupled harmonic oscillator model. Material properties were allowed to vary in a piecewise smooth pattern. This neural network inversion (Inhomogeneous Learned Inversion (ILI)) was compared against a previous homogeneous neural network inversion (Homogeneous Learned Inversion (HLI)) and conventional direct inversion (DI) in simulation, phantom, and in-vivo experiments.
resultsIn simulation experiments, ILI was more accurate than HLI and DI in predicting the stiffness of an inclusion in noise-free, low-noise, and high-noise data. In the phantom experiment, ILI delineated inclusions ≤ 2.25 cm in diameter more clearly than HLI and DI, and provided a higher contrast-to-noise ratio for all inclusions. In a series of stiff brain tumors, ILI shows sharper stiffness transitions at the edges of tumors than the other inversions evaluated.
conclusionILI is an artificial neural network based framework for MRE inversion that does not assume homogeneity in material stiffness. Preliminary results suggest that it provides more accurate stiffness estimates and better contrast in small inclusions and at large stiffness gradients than existing algorithms that assume local homogeneity. These results support the need for continued exploration of learning-based approaches to MRE inversion, particularly for applications where high resolution is required.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.