viscoelastic-characterization-soft-tissue-mimicking-gelatin-phantoms

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography

Characterization of biomechanical properties of soft biological tissues is important to understand the tissue mechanics and explore the ...

Shanghai Jiao Tong University

An electromagnetic actuator was designed for magnetic resonance elastography (MRE). The actuator is unique in that it is simple, portable, and capable of brain, abdomen, and phantom imagings. A custom-built control unit was used for controlling the vibration frequency and synchronizing the trigger signals. An actuation unit was built and mounted on the specifically designed clamp and holders for different imaging applications. MRE experiments with respect to gel phantoms, brain, and liver showed that the actuator could produce stable and consistent mechanical waves. Estimated shear modulus using local frequency estimate method demonstrated that the measurement results were in line with that from MRE studies using different actuation systems. The relatively easy setup procedure and simple design indicated that the actuator system had the potential to be applied in many different clinical studies.

A multi-purpose electromagnetic actuator for magnetic resonance elastography.

Liver cancer is one of the leading cancers, especially in developing countries. Understanding the biomechanical properties of the liver cancer cells can not only help to elucidate the mechanisms behind the cancer progression, but also provide important information for diagnosis and treatment. At the cellular level, we used well-established atomic force microscopy (AFM) techniques to characterize the heterogeneity of mechanical properties of two different types of human liver cancer cells and a normal liver cell line. Stiffness maps with a resolution of 128x128 were acquired for each cell. The distributions of the indentation moduli of the cells showed significant differences between cancerous cells and healthy controls. Significantly, the variability was even greater amongst different types of cancerous cells. Fitting of the histogram of the effective moduli using a normal distribution function showed the Bel7402 cells were stiffer than the normal cells while HepG2 cells were softer. Morphological analysis of the cell structures also showed a higher cytoskeleton content among the cancerous cells. Results provided a foundation for applying knowledge of cell stiffness heterogeneity to search for tissue-level, early-stage indicators of liver cancer.

Regional biomechanical imaging of liver cancer cells.

Mechanical properties of the brain tissue are crucial to understand the mechanisms of traumatic brain injury (TBI). Injured brain tissue could induce changes of mechanical properties and anatomical structures. However, limited data is available for the injured tissue.

Viscoelastic characterization of injured brain tissue after controlled cortical impact (CCI) using a mouse model.

Real-time interventional MRI (I-MRI) could help to visualize the position of the interventional feature, thus improving patient outcomes in MR-guided neurosurgery. In particular, in deep brain stimulation, real-time visualization of the intervention procedure using I-MRI could improve the accuracy of the electrode placement. However, the requirements of a high undersampling rate and fast reconstruction speed for real-time imaging pose a great challenge for reconstruction of the interventional images. Based on recent advances in deep learning (DL), we proposed a feature-based convolutional neural network (FbCNN) for reconstructing interventional images from golden-angle radially sampled data. The method was composed of two stages: (a) reconstruction of the interventional feature and (b) feature refinement and postprocessing. With only five radially sampled spokes, the interventional feature was reconstructed with a cascade CNN. The final interventional image was constructed with a refined feature and a fully sampled reference image. With a comparison of traditional reconstruction techniques and recent DL-based methods, it was shown that only FbCNN could reconstruct the interventional feature and the final interventional image. With a reconstruction time of ~ 500 ms per frame and an acceleration factor of ~ 80, it was demonstrated that FbCNN had the potential for application in real-time I-MRI.

A feature-based convolutional neural network for reconstruction of interventional MRI.

Our goal is to design, test and verify an electromagnetic actuator for brain magnetic resonance elastography (MRE). We proposed a grappler-shaped design that can transmit stable vibrations into the brain. To validate its performance, simulations were carried out to ensure the electromagnetic field generated by the actuator did not interfere with the B field. The actuation vibration spectrum was analyzed to verify the actuation accuracy. Phantom and volunteer experiments were carried out to evaluate the performance of the actuator. Simulation of the magnetic field showed that the proposed actuator has a fringe field of less than 3 G in the imaging region. The phantom experiments showed that the proposed actuator did not interfere with the routine imaging sequences. The measured vibration spectra demonstrated that the frequency offset was about one third that of a pneumatic device and the transmission efficiency was three times higher. The shear moduli estimated from brain MRE were consistent with those from the literature. The actuation frequency of the proposed actuator has less frequency offset and off-center frequency components compared with the pneumatic counterpart. The whole actuator weighted only 980 g. The actuator can carry out multifrequency MRE on the brain with high accuracy. It is easy to use, comfortable for the patient and portable.

An electromagnetic actuator for brain magnetic resonance elastography with high frequency accuracy.

. To achieve fast magnetic resonance elastography (MRE) at a low frequency for better shear modulus estimation of the brain.. We proposed a multiphase radial DENSE MRE (MRD-MRE) sequence and an improved GRASP algorithm utilizing the sparsity of the harmonic motion (SH-GRASP) for fast MRE at 20 Hz. For the MRD-MRE sequence, the initial position encoded by spatial modulation of magnetization (SPAMM) was decoded by an arbitrary number of readout blocks without increasing the number of phase offsets. Based on the harmonic motion, a modified total variation and temporal Fourier transform were introduced to utilize the sparsity in the temporal domain. Both phantom and brain experiments were carried out and compared with that from multiphase Cartesian DENSE-MRE (MCD-MRE), and conventional gradient echo sequence (GRE-MRE). Reconstruction performance was also compared with GRASP and compressed sensing.. Results showed the scanning time of a fully sampled image with four phase offsets for MRD-MRE was only 1/5 of that from GRE-MRE. The wave patterns and estimated stiffness maps were similar to those from MCD-MRE and GRE-MRE. With SH-GRASP, the total scan time could be shortened by additional 4 folds, achieving a total acceleration factor of 20. Better metric values were also obtained using SH-GRASP for reconstruction compared with other algorithms.. The MRD-MRE sequence and SH-GRASP algorithm can be used either in combination or independently to accelerate MRE, showing the potentials for imaging the brain as well as other organs.

Suhao Qiu

School of Biomedical Engineering

Shanghai Jiao Tong University

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography

Suhao Qiu

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}School of Biomedical Engineering

Shanghai Jiao Tong University

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography

School of Biomedical Engineering