Many pathologies alter the mechanical properties of tissue. Magnetic resonance elastography (MRE) has been developed to noninvasively characterize these quantities in vivo. Typically, small vibrations are induced in the tissue of interest with an external mechanical actuator. The resulting displacements are measured with phase contrast sequences and are then used to estimate the underlying mechanical property distribution. Several MRE studies have quantified brain tissue properties. However, the cranium and meninges, especially the dura, are very effective at damping externally applied vibrations from penetrating deeply into the brain. Here, we report a method, termed 'intrinsic activation', that eliminates the requirement for external vibrations by measuring the motion generated by natural blood vessel pulsation. A retrospectively gated phase contrast MR angiography sequence was used to record the tissue velocity at eight phases of the cardiac cycle. The velocities were numerically integrated via the Fourier transform to produce the harmonic displacements at each position within the brain. The displacements were then reconstructed into images of the shear modulus based on both linear elastic and poroelastic models. The mechanical properties produced fall within the range of brain tissue estimates reported in the literature and, equally important, the technique yielded highly reproducible results. The mean shear modulus was 8.1 kPa for linear elastic reconstructions and 2.4 kPa for poroelastic reconstructions where fluid pressure carries a portion of the stress. Gross structures of the brain were visualized, particularly in the poroelastic reconstructions. Intra-subject variability was significantly less than the inter-subject variability in a study of six asymptomatic individuals. Further, larger changes in mechanical properties were observed in individuals when examined over time than when the MRE procedures were repeated on the same day. Cardiac pulsation, termed intrinsic activation, produces sufficient motion to allow mechanical properties to be recovered. The poroelastic model is more consistent with the measured data from brain at low frequencies than the linear elastic model. Intrinsic activation allows MRE to be performed without a device shaking the head so the patient notices no differences between it and the other sequences in an MR examination.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797022PMC
http://dx.doi.org/10.1088/0031-9155/57/22/7275DOI Listing

Publication Analysis

Top Keywords

mechanical properties
16
intrinsic activation
12
linear elastic
12
mechanical property
8
phase contrast
8
brain tissue
8
shear modulus
8
poroelastic reconstructions
8
brain
7
mechanical
6

Similar Publications

Construction of Supramolecular Polymer Network Elastomers Based on Pillar[5]arene/Alkyl Chain Host-Guest Interactions.

ACS Macro Lett

January 2025

Key Laboratory of Materials Chemistry for Energy Conversion and Storage, Ministry of Education, Hubei Key Laboratory of Materials Chemistry and Service Failure, Hubei Engineering Research Center for Biomaterials and Medical Protective Materials, State Key Laboratory of Materials Processing and Die & Mould Technology, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

As a special kind of supramolecular compound with many favorable properties, pillar[]arene-based supramolecular polymer networks (SPNs) show potential application in many fields. Although we have come a long way using pillar[]arene to prepare SPNs and construct a series of smart materials, it remains a challenge to enhance the mechanical strength of pillar[]arene-based SPNs. To address this issue, a new supramolecular regulation strategy was developed, which could precisely control the preparation of pillar[]arene-based SPN materials with excellent mechanical properties by adjusting the polymer network structures.

View Article and Find Full Text PDF

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties.

J Phys Chem Lett

January 2025

Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments.

View Article and Find Full Text PDF

Soft and stretchable strain sensors are crucial for applications in human-machine interfaces, flexible robotics, and electronic skin. Among these, capacitive strain sensors are widely used and studied; however, they face challenges due to material and structural constraints, such as low baseline capacitance and susceptibility to external interference, which result in low signal-to-noise ratios and poor stability. To address these issues, we propose a U-shaped electrode flexible strain sensor based on liquid metal elastomer (LME).

View Article and Find Full Text PDF

Binuclear ruthenium complexes have been investigated for potential DNA-targeted therapeutic and diagnostic applications. Studies of DNA threading intercalation, in which DNA base pairs must be broken for intercalation, have revealed means of optimizing a model binuclear ruthenium complex to obtain reversible DNA-ligand assemblies with the desired properties of high affinity and slow kinetics. Here, we used single-molecule force spectroscopy to study a binuclear ruthenium complex with a longer semi-rigid linker relative to the model complex.

View Article and Find Full Text PDF

Radiofrequency ablation (RFA) is a minimally invasive procedure that utilizes localized heat to treat tumors by inducing localized tissue thermal damage. The present study aimed to evaluate the temperature evolution and spatial distribution, ablation size, and reproducibility of ablation zones in ex vivo liver, kidney, and lung using a commercial device, i.e.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!