scholarly journals Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques

2021 ◽  
Vol 15 ◽  
Author(s):  
Marco Marino ◽  
Lucilio Cordero-Grande ◽  
Dante Mantini ◽  
Giulio Ferrazzi
Keyword(s):  
Spatial Resolution ◽  
Diffusion Tensor ◽  
Signal To Noise Ratio ◽  
Water Concentration ◽  
B Value ◽  
Conductivity Tensor ◽  
Source Imaging ◽  
Low Frequencies ◽  
Diffusion Data ◽  
Fitting Procedure

Conductivity tensor imaging (CTI) has been recently proposed to map the conductivity tensor in 3D using magnetic resonance imaging (MRI) at the frequency range of the brain at rest, i.e., low-frequencies. Conventional CTI mapping methods process the trans-receiver phase of the MRI signal using the MR electric properties tomography (MR-EPT) technique, which in turn involves the application of the Laplace operator. This results in CTI maps with a low signal-to-noise ratio (SNR), artifacts at tissue boundaries and a limited spatial resolution. In order to improve on these aspects, a methodology independent from the MR-EPT method is proposed. This relies on the strong assumption for which electrical conductivity is univocally pre-determined by water concentration. In particular, CTI maps are calculated by combining high-frequency conductivity derived from water maps and multi b-value diffusion tensor imaging (DTI) data. Following the implementation of a pipeline to optimize the pre-processing of diffusion data and the fitting routine of a multi-compartment diffusivity model, reconstructed conductivity images were evaluated in terms of the achieved spatial resolution in five healthy subjects scanned at rest. We found that the pre-processing of diffusion data and the optimization of the fitting procedure improve the quality of conductivity maps. We achieve reproducible measurements across healthy participants and, in particular, we report conductivity values across subjects of 0.55 ± 0.01Sm, 0.3 ± 0.01Sm and 2.15 ± 0.02Sm for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), respectively. By attaining an actual spatial resolution of the conductivity tensor close to 1 mm in-plane isotropic, partial volume effects are reduced leading to good discrimination of tissues with similar conductivity values, such as GM and WM. The application of the proposed framework may contribute to a better definition of the head tissue compartments in electroencephalograpy/magnetoencephalography (EEG/MEG) source imaging and be used as biomarker for assessing conductivity changes in pathological conditions, such as stroke and brain tumors.

scholarly journals Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5499
Author(s):  
Nitish Katoch ◽  
Bup-Kyung Choi ◽  
Ji-Ae Park ◽  
In-Ok Ko ◽  
Hyung-Joong Kim
Keyword(s):  
Diffusion Tensor ◽  
Extracellular Volume ◽  
Clinical Applications ◽  
Conductivity Tensor ◽  
Source Imaging ◽  
Ion Concentrations ◽  
Reconstruction Methods ◽  
Eeg Source Imaging ◽  
Diffusion Tensor Images ◽  
Human Brains

Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to prior information of the mean conductivity values. The conductivity tensor images obtained from five human brains through the CTI method were comparable to previously reported literature values. The images by the four methods using DTI were highly correlated with the diffusion tensor images, showing a coefficient of determination (R2) value of 0.65 to 1.00. However, the images by the CTI method were less correlated with the diffusion tensor images and exhibited an averaged R2 value of 0.51. The CTI method could handle the effects of different ion concentrations as well as mobilities and extracellular volume fractions by collecting and processing additional B1 map data. It is necessary to select an application-specific model taking into account the pros and cons of each model. Future studies are essential to confirm the usefulness of these conductivity tensor imaging methods in clinical applications, such as tumor characterization, EEG source imaging, and treatment planning for electrical stimulation.

Biological applications of microscopic magnetic resonance imaging

1993 ◽  
Vol 51 ◽  
pp. 90-91
Author(s):  
Russell E. Jacobs ◽  
S. Earl Fraser
Keyword(s):  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Biological Samples ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Water Concentration ◽  
Clinical Tool ◽  
Mr Image ◽  
Magnetic Field Gradients

The ability of MRI to provide three dimensional images of thick opaque samples in a noninvasive manner has made it an extremely important clinical tool. In addition, the large number of types of contrast mechanisms in a MR experiment offer the clinician and research scientist the possibility of adapting the image contrast to fit the problem of interest. While typical resolutions employed clinically are on the order of a millimeter, the notion of using MRI at microscopic resolutions arose early in the development of this technique. Spatial information is encoded in both the frequency and phase of the nuclear magnetic resonance signal by selective application of magnetic field gradients. Spatial resolution in biological samples is typically limited by a number of physical effects as well as signal-to-noise ratio (S/N) considerations. Estimate of the theoretical limits of resolution in the MR image arising from these phenomena range from 2 to 0.5μm. The practical spatial resolution is currently determined by the S/N which is often limited by the amount of time available to actually acquire the image (i.e. the temporal resolution). For example, a reasonable S/N clinical MR image can be obtained in about 5 minutes with a voxel (volume element) size of (1mm). We are interested in voxels down to ∼1μm on a side. Because most of the proton MR signal arises from water in biological samples and water concentration is roughly constant, the S/N change in the image will be proportional to the volume change: a factor of 10−9. Of course, this is true only if all experimental parameters are the same.

Effects of Angular Resolution and b Value on Diffusion Tensor Imaging in Knee Joint

Cartilage ◽  
2021 ◽  
pp. 194760352110079
Author(s):  
Qi Zhao ◽  
Rees P. Ridout ◽  
Jikai Shen ◽  
Nian Wang
Keyword(s):  
Diffusion Tensor Imaging ◽  
Knee Joint ◽  
Growth Plate ◽  
Angular Resolution ◽  
Diffusion Tensor ◽  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Mean Diffusivity ◽  
B Value

Objective To investigate the influences of the diffusion gradient directions (angular resolution) and the strength of the diffusion gradient ( b value) on diffusion tensor imaging (DTI) metrics and tractography of various connective tissues in knee joint. Design Two rat knee joints were scanned on a preclinical 9.4-T system using a 3-dimensional diffusion-weighted spin echo pulse sequence. One protocol with b value of 500, 1500, and 2500 s/mm2 were acquired separately using 43 diffusion gradient directions. The other protocol with b value of 1000 s/mm2 was performed using 147 diffusion gradient directions. The in-plane resolution was 45 µm isotropic. Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different angular resolution. Tractography was quantitatively evaluated at different b values and angular resolutions in cartilage, ligament, meniscus, and growth plate. Results The ligament showed higher FA value compared with growth plate and cartilage. The FA values were largely overestimated at the angular resolution of 6. Compared with FA, MD showed less sensitivity to the angular resolution. The fiber tracking was failed at low angular resolution (6 diffusion gradient directions) or high b value (2500 s/mm2). The quantitative measurements of tract length and track volume were strongly dependent on angular resolution and b value. Conclusions To obtain consistent DTI outputs and tractography in knee joint, the scan may require a proper b value (ranging from 500 to 1500 s/mm2) and sufficient angular resolution (>14) with signal-to-noise ratio >10.

scholarly journals DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
M. Laganà ◽  
M. Rovaris ◽  
A. Ceccarelli ◽  
C. Venturelli ◽  
S. Marini ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Signal To Noise Ratio ◽  
Mean Diffusivity ◽  
B Value ◽  
Voxel Size ◽  
Tissue Microstructure ◽  
Mr Diffusion ◽  
The Central Nervous System ◽  
Tissue Abnormalities

Background. Magnetic Resonance (MR) diffusion tensor imaging (DTI) is able to quantify in vivo tissue microstructure properties and to detect disease related pathology of the central nervous system. Nevertheless, DTI is limited by low spatial resolution associated with its low signal-to-noise-ratio (SNR).Aim. The aim is to select a DTI sequence for brain clinical studies, optimizing SNR and resolution.Methods and Results. We applied 6 methods for SNR computation in 26 DTI sequences with different parameters using 4 healthy volunteers (HV). We choosed two DTI sequences for their high SNR, they differed by voxel size and b-value. Subsequently, the two selected sequences were acquired from 30 multiple sclerosis (MS) patients with different disability and lesion load and 18 age matched HV. We observed high concordance between mean diffusivity (MD) and fractional anysotropy (FA), nonetheless the DTI sequence with smaller voxel size displayed a better correlation with disease progression, despite a slightly lower SNR. The reliability of corpus callosum (CC) fiber tracking with the chosen DTI sequences was also tested.Conclusion. The sensitivity of DTI-derived indices to MS-related tissue abnormalities indicates that the optimized sequence may be a powerful tool in studies aimed at monitoring the disease course and severity.

Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging

2007 ◽  
Vol 107 (3) ◽  
pp. 488-494 ◽  
Author(s):  
Jeffrey I. Berman ◽  
Mitchel S. Berger ◽  
Sungwon Chung ◽  
Srikantan S. Nagarajan ◽  
Roland G. Henry
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Bipolar Electrode ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Motor Pathway ◽  
Magnetic Source ◽  
Subcortical Stimulation

Object Resecting brain tumors involves the risk of damaging the descending motor pathway. Diffusion tensor (DT)–imaged fiber tracking is a noninvasive magnetic resonance (MR) technique that can delineate the subcortical course of the motor pathway. The goal of this study was to use intraoperative subcortical stimulation mapping of the motor tract and magnetic source imaging to validate the utility of DT-imaged fiber tracking as a tool for presurgical planning. Methods Diffusion tensor-imaged fiber tracks of the motor tract were generated preoperatively in nine patients with gliomas. A mask of the resultant fiber tracks was overlaid on high-resolution T1- and T2-weighted anatomical MR images and used for stereotactic surgical navigation. Magnetic source imaging was performed in seven of the patients to identify functional somatosensory cortices. During resection, subcortical stimulation mapping of the motor pathway was performed within the white matter using a bipolar electrode. Results A total of 16 subcortical motor stimulations were stereotactically identified in nine patients. The mean distance between the stimulation sites and the DT-imaged fiber tracks was 8.7 ±3.1 mm (±standard deviation). The measured distance between subcortical stimulation sites and DT-imaged fiber tracks combines tracking technique errors and all errors encountered with stereotactic navigation. Conclusions Fiber tracks delineated using DT imaging can be used to identify the motor tract in deep white matter and define a safety margin around the tract.

scholarly journals Diffusion tensor imaging and fiber tractography of the median nerve at 1.5T: optimization of b value

2008 ◽  
Vol 38 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Gustav Andreisek ◽  
Lawrence M. White ◽  
Andrea Kassner ◽  
George Tomlinson ◽  
Marshall S. Sussman
Keyword(s):  
Diffusion Tensor Imaging ◽  
Median Nerve ◽  
Diffusion Tensor ◽  
B Value ◽  
Fiber Tractography

scholarly journals Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 2603
Author(s):  
George Xian ◽  
Hua Shi ◽  
Cody Anderson ◽  
Zhuoting Wu
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Regression Tree ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Landsat 8 ◽  
Signal To Noise ◽  
Study Results ◽  
The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

scholarly journals Including measures of high gamma power can improve the decoding of natural speech from EEG

2019 ◽  
Author(s):  
Shyanthony R. Synigal ◽  
Emily S. Teoh ◽  
Edmund C. Lalor
Keyword(s):  
Brain Imaging ◽  
Neural Activity ◽  
Speech Processing ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Natural Speech ◽  
Low Frequencies ◽  
High Gamma ◽  
Gamma Power ◽  
Speech Tracking

ABSTRACTThe human auditory system is adept at extracting information from speech in both single-speaker and multi-speaker situations. This involves neural processing at the rapid temporal scales seen in natural speech. Non-invasive brain imaging (electro-/magnetoencephalography [EEG/MEG]) signatures of such processing have shown that the phase of neural activity below 16 Hz tracks the dynamics of speech, whereas invasive brain imaging (electrocorticography [ECoG]) has shown that such rapid processing is even more strongly reflected in the power of neural activity at high frequencies (around 70-150 Hz; known as high gamma). The aim of this study was to determine if high gamma power in scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. Furthermore, we aimed to assess whether any such information might be complementary to that reflected in well-established low frequency EEG indices of speech processing. We used linear regression to investigate speech envelope and attention decoding in EEG at low frequencies, in high gamma power, and in both signals combined. While low frequency speech tracking was evident for almost all subjects as expected, high gamma power also showed robust speech tracking in a minority of subjects. This same pattern was true for attention decoding using a separate group of subjects who undertook a cocktail party attention experiment. For the subjects who showed speech tracking in high gamma power, the spatiotemporal characteristics of that high gamma tracking differed from that of low-frequency EEG. Furthermore, combining the two neural measures led to improved measures of speech tracking for several subjects. Overall, this indicates that high gamma power EEG can carry useful information regarding speech processing and attentional selection in some subjects and combining it with low frequency EEG can improve the mapping between natural speech and the resulting neural responses.

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