scholarly journals Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone

2018 ◽  
Vol 5 (8) ◽  
pp. 180563 ◽  
Author(s):  
Chantal Nguyen ◽  
Kimberly J. Schlesinger ◽  
Timothy W. James ◽  
Kristin M. James ◽  
Robert L. Sah ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Trabecular Bone ◽  
Structural Information ◽  
Three Dimensional ◽  
Biological Tissues ◽  
Bone Fragility ◽  
Support Vector ◽  
Osteoporotic Bone ◽  
Micro Computed Tomography ◽  
Trabecular Thickness

Osteoporosis, characterized by increased fracture risk and bone fragility, impacts millions of adults worldwide, but effective, non-invasive and easily accessible diagnostic tests of the disease remain elusive. We present a magnetic resonance (MR) technique that overcomes the motion limitations of traditional MR imaging to acquire high-resolution frequency-domain data to characterize the texture of biological tissues. This technique does not involve obtaining full two-dimensional or three-dimensional images, but can probe scales down to the order of 40 μm and in particular uncover structural information in trabecular bone. Using micro-computed tomography data of vertebral trabecular bone, we computationally validate this MR technique by simulating MR measurements of a ‘ratio metric’ determined from a few k -space values corresponding to trabecular thickness and spacing. We train a support vector machine classifier on ratio metric values determined from healthy and simulated osteoporotic bone data, which we use to accurately classify osteoporotic bone.

The Effect of Trabecular Bone on the Mechanical Response of Human Mandible with Implant

2014 ◽  
Vol 695 ◽  
pp. 588-591
Author(s):  
Khairul Salleh Basaruddin ◽  
Ruslizam Daud
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Trabecular Bone ◽  
Static Analysis ◽  
Load Distribution ◽  
Mechanical Response ◽  
Three Dimensional ◽  
Fe Model ◽  
Micro Computed Tomography ◽  
Bone Specimen

This study aims to investigate the influence of trabecular bone in human mandible bone on the mechanical response under implant load. Three dimensional voxel finite element (FE) model of mandible bone was reconstructed from micro-computed tomography (CT) images that were captured from bone specimen. Two FE models were developed where the first consists of cortical bone, trabecular bone and implants, and trabecular bone part was excluded in the second model. A static analysis was conducted on both models using commercial software Voxelcon. The results suggest that trabecular bone contributed to the strength of human mandible bone and to the effectiveness of load distribution under implant load.

scholarly journals Trabecular Bone Assessment Using Magnetic-Resonance Imaging: A Pilot Study

2020 ◽  
Vol 17 (24) ◽  
pp. 9282
Author(s):  
Lauren Bohner ◽  
Pedro Tortamano ◽  
Norbert Meier ◽  
Felix Gremse ◽  
Johannes Kleinheinz ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Trabecular Bone ◽  
Volume Fraction ◽  
Bone Volume ◽  
Control Group ◽  
Resonance Imaging ◽  
Voxel Size ◽  
Trabecular Thickness ◽  
Spin Echo Sequence

The aim of this study was to assess trabecular bone morphology via magnetic-resonance imaging (MRI) using microcomputed tomography (µCT) as the control group. Porcine bone samples were scanned with T1-weighted turbo spin echo sequence imaging, using TR 25 ms, TE 3.5 ms, FOV 100 × 100 × 90, voxel size 0.22 × 0.22 × 0.50 mm, and scan time of 11:18. µCT was used as the control group with 80 kV, 125 mA, and a voxel size of 16 µm. The trabecular bone was segmented on the basis of a reference threshold value and morphological parameters. Bone volume (BV), Bone-volume fraction (BvTv), Bone specific surface (BsBv), trabecular thickness (TbTh), and trabecular separation (TbSp) were evaluated. Paired t-test and Pearson correlation test were performed at p = 0.05. MRI overestimated BV, BvTv, TbTh, and TbSp values. BsBv was the only parameter that was underestimated by MRI. High statistical correlation (r = 0.826; p < 0.05) was found for BV measurements. Within the limitations of this study, MRI overestimated trabecular bone parameters, but with a statistically significant fixed linear offset.

scholarly journals Loss of trabeculae by mechano-biological means may explain rapid bone loss in osteoporosis

2008 ◽  
Vol 5 (27) ◽  
pp. 1243-1253 ◽  
Author(s):  
Brianne M Mulvihill ◽  
Laoise M McNamara ◽  
Patrick J Prendergast
Keyword(s):  
Trabecular Bone ◽  
Bone Tissue ◽  
Bone Fragility ◽  
Osteoporotic Bone ◽  
Single Fracture ◽  
Rapid Loss ◽  
Fracture Event ◽  
Turnover Process ◽  
Trabecular Bone Mass ◽  
Bone Trabeculae

Osteoporosis is characterized by rapid and irreversible loss of trabecular bone tissue leading to increased bone fragility. In this study, we hypothesize two causes for rapid loss of bone trabeculae; firstly, the perforation of trabeculae is caused by osteoclasts resorbing a cavity so deep that it cannot be refilled and, secondly, the increases in bone tissue elastic modulus lead to increased propensity for trabecular perforation. These hypotheses were tested using an algorithm that was based on two premises: (i) bone remodelling is a turnover process that repairs damaged bone tissue by resorbing and returning it to a homeostatic strain level and (ii) osteoblast attachment is under biochemical control. It was found that a mechano-biological algorithm based on these premises can simulate the remodelling cycle in a trabecular strut where damaged bone is resorbed to form a pit that is subsequently refilled with new bone. Furthermore, the simulation predicts that there is a depth of resorption cavity deeper than which refilling of the resorption pits is impossible and perforation inevitably occurs. However, perforation does not occur by a single fracture event but by continual removal of microdamage after it forms beneath the resorption pit. The simulation also predicts that perforations would occur more easily in trabeculae that are more highly mineralized (stiffer). Since both increased osteoclast activation rates and increased mineralization have been measured in osteoporotic bone, either or both may contribute to the rapid loss of trabecular bone mass observed in osteoporotic patients.

Three-Dimensional Characterization of Cell Clusters Using Synchrotron-Radiation-Based Micro-Computed Tomography

2006 ◽  
Vol 12 (2) ◽  
pp. 97-105 ◽  
Author(s):  
Bert Müller ◽  
Marco Riedel ◽  
Philipp J. Thurner
Keyword(s):  
Computed Tomography ◽  
Dimensional Space ◽  
Surrounding Medium ◽  
Three Dimensional ◽  
Biological Tissues ◽  
X Rays ◽  
Micro Computed Tomography ◽  
Hek 293 ◽  
Cell Clusters ◽  
Cell Compartments

Micro-computed tomography with the highly intense, monochromatic X rays produced by the synchrotron is a superior method to nondestructively measure the local absorption in three-dimensional space. Because biological tissues and cells consist mainly of water as the surrounding medium, higher absorbing agents have to be incorporated into the structures of interest. Even without X-ray optics such as refractive lens, one can uncover the stain distribution with the spatial resolution of about 1 μm. Incorporating the stain at selected cell compartments, for example, binding to the RNA/DNA, their density distribution becomes quantified. In this communication, we demonstrate that tomograms obtained at the beamlines BW2 and W2 (HASYLAB at DESY, Hamburg, Germany) and 4S (SLS, Villigen, Switzerland) clearly show that the RNA/DNA-stained HEK 293 cell clusters have a core of high density and a peripheral part of lower density, which correlate with results of optical microscopy. The inner part of the clusters is associated with nonvital cells as the result of insufficient oxygen and nutrition supply. This necrotic part is surrounded by (6 ± 1) layers of vital cells.

scholarly journals Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
R. Rajesh Sharma ◽  
P. Marikkannu
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Gravitational Search Algorithm ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Classification Model ◽  
Support Vector ◽  
Preliminary Evaluation ◽  
Distribution Method

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, andk-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

scholarly journals Recent Advances in Metal-Based Magnetic Composites as High-Efficiency Candidates for Ultrasound-Assisted Effects in Cancer Therapy

2021 ◽  
Vol 22 (19) ◽  
pp. 10461
Author(s):  
Zhenyu Wang ◽  
Xiaoxiao He ◽  
Shiyue Chen ◽  
Chengdian He ◽  
Teng Wang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Cancer Cells ◽  
Magnetic Materials ◽  
High Efficiency ◽  
Structural Information ◽  
Drug Loading ◽  
Biological Tissues ◽  
Tissue Imaging ◽  
Mechanical Stimuli ◽  
Magnetic Composites

Metal-based magnetic materials have been used in different fields due to their particular physical or chemical properties. The original magnetic properties can be influenced by the composition of constituent metals. As utilized in different application fields, such as imaging monitoring, thermal treatment, and combined integration in cancer therapies, fabricated metal-based magnetic materials can be doped with target metal elements in research. Furthermore, there is one possible new trend in human activities and basic cancer treatment. As has appeared in characterizations such as magnetic resonance, catalytic performance, thermal efficiency, etc., structural information about the real morphology, size distribution, and composition play important roles in its further applications. In cancer studies, metal-based magnetic materials are considered one appropriate material because of their ability to penetrate biological tissues, interact with cellular components, and induce noxious effects. The disruptions of cytoskeletons, membranes, and the generation of reactive oxygen species (ROS) further influence the efficiency of metal-based magnetic materials in related applications. While combining with cancer cells, these magnetic materials are not only applied in imaging monitoring focus areas but also could give the exact area information in the cure process while integrating ultrasound treatment. Here, we provide an overview of metal-based magnetic materials of various types and then their real applications in the magnetic resonance imaging (MRI) field and cancer cell treatments. We will demonstrate advancements in using ultrasound fields co-worked with MRI or ROS approaches. Besides iron oxides, there is a super-family of heterogeneous magnetic materials used as magnetic agents, imaging materials, catalytic candidates in cell signaling and tissue imaging, and the expression of cancer cells and their high sensitivity to chemical, thermal, and mechanical stimuli. On the other hand, the interactions between magnetic candidates and cancer tissues may be used in drug delivery systems. The materials’ surface structure characteristics are introduced as drug loading substrates as much as possible. We emphasize that further research is required to fully characterize the mechanisms of underlying ultrasounds induced together, and their appropriate relevance for materials toxicology and biomedical applications.

scholarly journals Assisted Diagnosis of Alzheimer’s Disease Based on Deep Learning and Multimodal Feature Fusion

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Wang ◽  
Xi Liu ◽  
Chongchong Yu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Support Vector ◽  
Image Size ◽  
Resonance Imaging

With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. Because Alzheimer’s disease (AD) has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two-dimensional (2D) ShuffleNet is developed three-dimensional (3D), which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis (KCCA) is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines (SVM) method classifier, which proves the feasibility and effectiveness of the proposed method.

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