ReviewNMR in biomedicine2018
Stiffness reconstruction methods for MR elastography.
Review in NMR in biomedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- MR Elastography Findings in a Cytotoxic Lesion of the Corpus Callosum (CLOCC): a Case Report with Longitudinal Follow-up.Clinical neuroradiology · 2026Article
- Beyond DNA damage: 3D tumor models and the integrin mechanobiology of radioresistance.Journal of experimental & clinical cancer research : CR · 2026Review
- Correlation Between Breast Parenchymal Stiffness Measured by 3T Magnetic Resonance Elastography and CT-Derived Hounsfield Units.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Fast, motion-robust MR elastography with distributed, generalized encoding.Magnetic resonance in medicine · 2025Article
- MR elastography datasets including phantom, liver, and brain.Scientific data · 2025Article
- Sex and Gender Differences in Iron Chelation.Biomedicines · 2024Review
- A Versatile MR Elastography Research Tool with a Modified Motion Signal-to-noise Ratio Approach.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Article
- Clinical application of magnetic resonance elastography in pediatric neurological disorders.Pediatric radiology · 2023Review
- 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
- Asynchronous magnetic resonance elastography: Shear wave speed reconstruction using noise correlation of incoherent waves.Magnetic resonance in medicine · 2023Article
- Improving in situ acoustic intensity estimates using MR acoustic radiation force imaging in combination with multifrequency MR elastography.Magnetic resonance in medicine · 2022Article
- Fast acquisition of propagating waves in humans with low-field MRI: Toward accessible MR elastography.Science advances · 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
- Mechanobiology of Colorectal Cancer.Cancers · 2022Review
- 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
- Shear wave cardiovascular MR elastography using intrinsic cardiac motion for transducer-free non-invasive evaluation of myocardial shear wave velocity.Scientific reports · 2021Article
- Targeted Blood Brain Barrier Opening With Focused Ultrasound Induces Focal Macrophage/Microglial Activation in Experimental Autoimmune Encephalomyelitis.Frontiers in neuroscience · 2021Article
- Tissue Rheology as a Possible Complementary Procedure to Advance Histological Diagnosis of Colon Cancer.ACS biomaterials science & engineering · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Assessment of tissue stiffness is desirable for clinicians and researchers, as it is well established that pathophysiological mechanisms often alter the structural properties of tissue. Magnetic resonance elastography (MRE) provides an avenue for measuring tissue stiffness and has a long history of clinical application, including staging liver fibrosis and stratifying breast cancer malignancy. A vital component of MRE consists of the reconstruction algorithms used to derive stiffness from wave-motion images by solving inverse problems. A large range of reconstruction methods have been presented in the literature, with differing computational expense, required user input, underlying physical assumptions, and techniques for numerical evaluation. These differences, in turn, have led to varying accuracy, robustness, and ease of use. While most reconstruction techniques have been validated against in silico or in vitro phantoms, performance with real data is often more challenging, stressing the robustness and assumptions of these algorithms. This article reviews many current MRE reconstruction methods and discusses the aforementioned differences. The material assumptions underlying the methods are developed and various approaches for noise reduction, regularization, and numerical discretization are discussed. Reconstruction methods are categorized by inversion type, underlying assumptions, and their use in human and animal studies. Future directions, such as alternative material assumptions, are also discussed.
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