scholarly journals Denoising of brain DW-MR data by single and multiple diffusion kernels

2010 ◽  
Vol 20 (3) ◽  
pp. 44-50
Author(s):  
Alonso Ramírez-Manzanares ◽  
Jonathan Rafael-Patiño ◽  
Manzar Ashtari
Keyword(s):  
Spatial Information ◽  
Diffusion Tensor ◽  
Signal To Noise Ratio ◽  
Tensor Models ◽  
Different Types ◽  
Weighted Magnetic Resonance Imaging ◽  
Axon Bundle ◽  
The Brain ◽  
Spatial Regularization

Diffusion Weighted Magnetic Resonance Imaging is widely used to study the structure ofthe fiber pathways of white matter in the brain. However, the recovered axon orientationscan be prone to error because of the low signal to noise ratio. Spatial regularization canreduce the error, but it must be done carefully so that real spatial information is not removedand false orientations are not introduced. In this paper we investigate the advantagesof applying an anisotropic filter based on single and multiple axon bundle orientation kernels.To this end, we compute local diffusion kernels based on Diffusion Tensor and multiDiffusion Tensor models. We show the benefits of our approach to three different types ofDW-MRI data: synthetic, in vivo human, and acquired from a diffusion phantom.

scholarly journals Uncertainty in the DTI Visualization Pipeline

2021 ◽  
pp. 125-148
Author(s):  
Faizan Siddiqui ◽  
Thomas Höllt ◽  
Anna Vilanova
Keyword(s):  
Magnetic Resonance Imaging ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Research Directions ◽  
Uncertainty Visualization ◽  
Weighted Magnetic Resonance Imaging ◽  
Fibrous Tissues ◽  
The Brain

AbstractDiffusion-Weighted Magnetic Resonance Imaging (DWI) enables the in-vivo visualization of fibrous tissues such as white matter in the brain. Diffusion-Tensor Imaging (DTI) specifically models the DWI diffusion measurements as a second order-tensor. The processing pipeline to visualize this data, from image acquisition to the final rendering, is rather complex. It involves a considerable amount of measurements, parameters and model assumptions, all of which generate uncertainties in the final result which typically are not shown to the analyst in the visualization. In recent years, there has been a considerable amount of work on the visualization of uncertainty in DWI, and specifically DTI. In this chapter, we primarily focus on DTI given its simplicity and applicability, however, several aspects presented are valid for DWI as a whole. We explore the various sources of uncertainties involved, approaches for modeling those uncertainties, and, finally, we survey different strategies to visually represent them. We also look at several related methods of uncertainty visualization that have been applied outside DTI and discuss how these techniques can be adopted to the DTI domain. We conclude our discussion with an overview of potential research directions.

scholarly journals Theranostic cranial implant for hyperspectral light delivery and microcirculation imaging without scalp removal

2019 ◽  
Author(s):  
Nami Davoodzadeh ◽  
Mildred S. Cano-Velázquez ◽  
Carrie R. Jonak ◽  
David L. Halaney ◽  
Devin K. Binder ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Image Resolution ◽  
Laser Speckle ◽  
Laser Speckle Imaging ◽  
Speckle Imaging ◽  
Optical Access ◽  
Optical Attenuation ◽  
Cranial Implant ◽  
The Brain

Light based techniques for imaging, diagnosing and treating the brain have become widespread clinical tools, but application of these techniques is limited by optical attenuation in the scalp and skull. This optical attenuation reduces the achievable spatial resolution, precluding the visualization of small features such as brain microvessels. The goal of this study was to assess a strategy for providing ongoing optical access to the brain without the need for repeated craniectomy or retraction of the scalp. This strategy involves the use of a transparent cranial implant and skin optical clearing agents, and was tested in mice to assess improvements in optical access which could be achieved for laser speckle imaging of cerebral microvasculature. Combined transmittance of the optically cleared scalp overlying the transparent cranial implant was as high as 89% in the NIR range, 50% in red range, 24% in green range, and 20% in blue range. In vivo laser speckle imaging experiments of mouse cerebral blood vessels showed that the proposed optical access increased signal-to-noise ratio and image resolution, allowing for visualization of microvessels through the transparent implant, which was not possible through the uncleared scalp and intact skull.

scholarly journals Diffusion tensor-MRI detects exercise-induced neuroplasticity in the hippocampal microstructure in mice

2020 ◽  
Vol 5 (2) ◽  
pp. 147-159
Author(s):  
Mohammad R. Islam ◽  
Renhao Luo ◽  
Sophia Valaris ◽  
Erin B. Haley ◽  
Hajime Takase ◽  
...  
Keyword(s):  
Animal Studies ◽  
Wheel Running ◽  
Diffusion Tensor ◽  
Hippocampal Volume ◽  
Mri Scans ◽  
Oligodendrocyte Precursor ◽  
Exercise Induced ◽  
Response To Exercise ◽  
The Brain

Background: Despite considerable research on exercise-induced neuroplasticity in the brain, a major ongoing challenge in translating findings from animal studies to humans is that clinical and preclinical settings employ very different techniques. Objective: Here we aim to bridge this divide by using diffusion tensor imaging MRI (DTI), an advanced imaging technique commonly applied in human studies, in a longitudinal exercise study with mice. Methods: Wild-type mice were exercised using voluntary free-wheel running, and MRI scans were at baseline and after four weeks and nine weeks of running. Results: Both hippocampal volume and fractional anisotropy, a surrogate for microstructural directionality, significantly increased with exercise. In addition, exercise levels correlated with effect size. Histological analysis showed more PDGFRα+ oligodendrocyte precursor cells in the corpus callosum of running mice. Conclusions: These results provide compelling in vivo support for the concept that similar adaptive changes occur in the brains of mice and humans in response to exercise.

scholarly journals Comprehensive ultrahigh resolution whole brain in vivo MRI dataset as a human phantom

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Falk Lüsebrink ◽  
Hendrik Mattern ◽  
Renat Yakupov ◽  
Julio Acosta-Cabronero ◽  
Mohammad Ashtarayeh ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Proton Density ◽  
Resonance Imaging ◽  
Quantitative Susceptibility Mapping ◽  
Ultrahigh Resolution ◽  
Isotropic Resolution ◽  
Comprehensive Collection ◽  
Weighted Magnetic Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

AbstractHere, we present an extension to our previously published structural ultrahigh resolution T1-weighted magnetic resonance imaging (MRI) dataset with an isotropic resolution of 250 µm, consisting of multiple additional ultrahigh resolution contrasts. Included are up to 150 µm Time-of-Flight angiography, an updated 250 µm structural T1-weighted reconstruction, 330 µm quantitative susceptibility mapping, up to 450 µm structural T2-weighted imaging, 700 µm T1-weighted back-to-back scans, 800 µm diffusion tensor imaging, one hour continuous resting-state functional MRI with an isotropic spatial resolution of 1.8 mm as well as more than 120 other structural T1-weighted volumes together with multiple corresponding proton density weighted acquisitions collected over ten years. All data are from the same participant and were acquired on the same 7 T scanner. The repository contains the unprocessed data as well as (pre-)processing results. The data were acquired in multiple studies with individual goals. This is a unique and comprehensive collection comprising a “human phantom” dataset. Therefore, we compiled, processed, and structured the data, making them publicly available for further investigation.

scholarly journals Top-down control of hippocampal signal-to-noise by prefrontal long-range inhibition

2021 ◽  
Author(s):  
Ruchi Malik ◽  
Yi Li ◽  
Selin Schamiloglu ◽  
Vikaas S. Sohal
Keyword(s):  
Information Processing ◽  
Long Range ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Top Down ◽  
Object Exploration ◽  
Hippocampal Activity ◽  
The Brain ◽  
Stimulation Of

SummaryThe prefrontal cortex (PFC) is postulated to exert ‘top-down control’ by modulating information processing throughout the brain to promote specific actions based on current goals. However, the pathways mediating top-down control remain poorly understood. In particular, knowledge about direct prefrontal connections that might facilitate top-down prefrontal control of information processing in the hippocampus remains sparse. Here we describe novel monosynaptic long-range GABAergic projections from PFC to hippocampus. These preferentially inhibit vasoactive intestinal polypeptide expressing interneurons, which are known to disinhibit hippocampal microcircuits. Indeed, stimulating prefrontal–hippocampal GABAergic projections increases hippocampal feedforward inhibition and reduces hippocampal activity in vivo. The net effect of these actions is to specifically enhance the signal-to-noise ratio for hippocampal representations of objects. Correspondingly, stimulation of PFC-to-hippocampus GABAergic projections promotes object exploration. Together, these results elucidate a novel top-down pathway in which long-range GABAergic projections target disinhibitory microcircuits, thereby enhancing signals and network dynamics underlying exploratory behavior.

scholarly journals Exploiting Spatial Information to Enhance DTI Segmentations via Spatial Fuzzy c-Means with Covariance Matrix Data and Non-Euclidean Metrics

2021 ◽  
Vol 11 (15) ◽  
pp. 7003
Author(s):  
Safa Elsheikh ◽  
Andrew Fish ◽  
Diwei Zhou
Keyword(s):  
Spatial Information ◽  
Diffusion Tensor ◽  
Contextual Information ◽  
Fuzzy C Means ◽  
Tensor Models ◽  
Diffusion Tensor Images ◽  
Fuzzy C Means Clustering ◽  
Euclidean Metrics ◽  
Diffusion Tensors ◽  
Tensor Data

A diffusion tensor models the covariance of the Brownian motion of water at a voxel and is required to be symmetric and positive semi-definite. Therefore, image processing approaches, designed for linear entities, are not effective for diffusion tensor data manipulation, and the existence of artefacts in diffusion tensor imaging acquisition makes diffusion tensor data segmentation even more challenging. In this study, we develop a spatial fuzzy c-means clustering method for diffusion tensor data that effectively segments diffusion tensor images by accounting for the noise, partial voluming, magnetic field inhomogeneity, and other imaging artefacts. To retain the symmetry and positive semi-definiteness of diffusion tensors, the log and root Euclidean metrics are used to estimate the mean diffusion tensor for each cluster. The method exploits spatial contextual information and provides uncertainty information in segmentation decisions by calculating the membership values for assigning a diffusion tensor at one voxel to different clusters. A regularisation model that allows the user to integrate their prior knowledge into the segmentation scheme or to highlight and segment local structures is also proposed. Experiments on simulated images and real brain datasets from healthy and Spinocerebellar ataxia 2 subjects showed that the new method was more effective than conventional segmentation methods.

Amperometric enzyme electrodes

1987 ◽  
Vol 316 (1176) ◽  
pp. 107-119 ◽  
Keyword(s):  
Organic Salt ◽  
Redox Catalysis ◽  
Glucose Levels ◽  
The Third ◽  
Different Types ◽  
Freely Moving ◽  
Low Levels ◽  
The Brain ◽  
Amperometric Enzyme Electrode

Three different types of amperometric enzyme electrode are described. The first type uses a conducting organic-salt electrode to oxidize NADH. Results for sensors for ethanol and for bile acids are presented. In the second type of sensor, flavoenzymes are directly oxidized on the surface of the conducting organic-salt electrode. Results for five different enzymes are described. The mechanism of the enzyme oxidation is discussed and the reaction is shown to take place by heterogeneous redox catalysis and not by homogeneous mediation. The enzymes are strongly adsorbed on the electrode; microelectrodes for in vivo studies can be constructed without a membrane. Results for in vivo studies of changing glucose levels in the brain of a freely moving rat are presented. The third type of sensor is designed to measure low levels of toxic gases such as H 2 S and HCN. This is done by monitoring the inhibition by the toxic gas of the activity of the respiratory enzyme cytochrome oxidase.

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.

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