scholarly journals Evaluation of six phase encoding based susceptibility distortion correction methods for diffusion MRI

2019 ◽  
Author(s):  
Xuan Gu ◽  
Anders Eklund
Keyword(s):  
Standard Deviation ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Simulated Data ◽  
Ground Truth ◽  
Distortion Correction ◽  
The Other ◽  
Phase Encoding ◽  
Corrected Data ◽  
The Difference

PurposeSusceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons.MethodsIn this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP and PA data.ResultsWe found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b0 images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric.ConclusionWe suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. We also suggest that indirect metrics must be interpreted cautiously when evaluating methods for correcting susceptibility distortions in diffusion MRI data.

scholarly journals Multipath Ghost Suppression Based on Generative Adversarial Nets in Through-Wall Radar Imaging

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 626 ◽  
Author(s):  
Yong Jia ◽  
Ruiyuan Song ◽  
Shengyi Chen ◽  
Gang Wang ◽  
Yong Guo ◽  
...  
Keyword(s):  
Ground Truth ◽  
Radar Imaging ◽  
The Other ◽  
Output Image ◽  
Adversarial Networks ◽  
Ground Truth Image ◽  
Simulation Results ◽  
The Difference ◽  
The One ◽  
Image Difference

In this paper, we propose an approach that uses generative adversarial nets (GAN) to eliminate multipath ghosts with respect to through-wall radar imaging (TWRI). The applied GAN is composed of two adversarial networks, namely generator G and discriminator D. Generator G learns the spatial characteristics of an input radar image to construct a mapping from an input to output image with suppressed ghosts. Discriminator D evaluates the difference (namely, the residual multipath ghosts) between the output image and the ground-truth image without multipath ghosts. On the one hand, by training G, the image difference is gradually diminished. In other words, multipath ghosts are increasingly suppressed in the output image of G. On the other hand, D is trained to improve in evaluating the diminishing difference accompanied with multipath ghosts as much as possible. These two networks, G and D, fight with each other until G eliminates the multipath ghosts. The simulation results demonstrate that GAN can effectively eliminate multipath ghosts in TWRI. A comparison of different methods demonstrates the superiority of the proposed method, such as the exemption of prior wall information, no target images with degradation, and robustness for different scenes.

scholarly journals Intersubject MVPD: Empirical Comparison of fMRI Denoising Methods for Connectivity Analysis

2018 ◽  
Author(s):  
Yichen Li ◽  
Rebecca Saxe ◽  
Stefano Anzellotti
Keyword(s):  
Brain Region ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Brain Regions ◽  
Fmri Data ◽  
Statistical Dependence ◽  
The Real ◽  
The Difference ◽  
Multivariate Pattern

AbstractNoise is a major challenge for the analysis of fMRI data in general and for connectivity analyses in particular. As researchers develop increasingly sophisticated tools to model statistical dependence between the fMRI signal in different brain regions, there is a risk that these models may increasingly capture artifactual relationships between regions, that are the result of noise. Thus, choosing optimal denoising methods is a crucial step to maximize the accuracy and reproducibility of connectivity models. Most comparisons between denoising methods require knowledge of the ground truth: of what is the ‘real signal’. For this reason, they are usually based on simulated fMRI data. However, simulated data may not match the statistical properties of real data, limiting the generalizability of the conclusions. In this article, we propose an approach to evaluate denoising methods using real (non-simulated) fMRI data. First, we introduce an intersubject version of multivariate pattern dependence (iMVPD) that computes the statistical dependence between a brain region in one participant, and another brain region in a different participant. iMVPD has the following advantages: 1) it is multivariate, 2) it trains and tests models on independent folds of the real fMRI data, and 3) it generates predictions that are both between subjects and between regions. Since whole-brain sources of noise are more strongly correlated within subject than between subjects, we can use the difference between standard MVPD and iMVPD as a ‘discrepancy metric’ to evaluate denoising techniques (where more effective techniques should yield smaller differences). As predicted, the difference is the greatest in the absence of denoising methods. Furthermore, a combination of removal of the global signal and CompCorr optimizes denoising (among the set of denoising options tested).

scholarly journals Enabling complex fibre geometries using 3D printed axon-mimetic phantoms

2021 ◽  
Author(s):  
Tristan K. Kuehn ◽  
Farah N. Mushtaha ◽  
Ali R. Khan ◽  
Corey A. Baron
Keyword(s):  
Diffusion Mri ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Ground Truth ◽  
Diffusion Kurtosis Imaging ◽  
Strongly Correlated ◽  
3D Print ◽  
3D Printed ◽  
Stick Model

AbstractPurposeTo introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterize the performance of diffusion MRI models and representations in the presence of orientation dispersion.MethodsAn extension to an open source 3D printing package was created to 3D print a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A fiducial in each phantom was used to register a ground truth map of that phantom’s crossing angles and/or arc radius to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric.ResultsThe mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle, but increased with fibre curvature. Axial diffusivity (AD) decreased sharply with increasing crossing angle. DKI’s diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric, decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDI’s intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was strongly correlated to crossing angle. Bingham-NODDI’s intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODIP) was also strongly correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle.ConclusionsThis study demonstrates that inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. As a proof of concept, the dependence of several representations and models on fibre dispersion/crossing were investigated. As expected, Bingham-NODDI was best able to characterize planar fibre dispersion in the phantoms.

scholarly journals Enabling constrained spherical deconvolution and diffusional variance decomposition with tensor-valued diffusion MRI

2021 ◽  
Author(s):  
Philippe Karan ◽  
Alexis Reymbaut ◽  
Guillaume Gilbert ◽  
Maxime Descoteaux
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Simulated Data ◽  
Orientation Distribution ◽  
Variance Decomposition ◽  
Distribution Functions ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Fiber Orientation Distribution

Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of HARDI data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), opening the door to the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). However, tensor-valued diffusion MRI data is not compatible with latest versions of CSD and the impacts of such atypical data on fODF reconstruction with CSD are yet to be studied. In this work, we lay down the mathematical and computational foundations of a tensor-valued CSD and use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimised protocol for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

scholarly journals Effect of growth intensity of bulls on the microstructure of musculus longissimus lumborum and meat quality

2012 ◽  
Vol 81 (2) ◽  
pp. 127-131 ◽  
Author(s):  
Krzysztof Młynek ◽  
Izabela Janiuk ◽  
Alicja Dzido
Keyword(s):  
Standard Deviation ◽  
The Other ◽  
Muscular Tissue ◽  
Myofibrillar Atpase ◽  
Black And White ◽  
Genetic Groups ◽  
Growth Intensity ◽  
Longissimus Lumborum ◽  
The Difference ◽  
Giant Fibres

The aim of this study was to evaluate the effect of growth intensity of 43 bulls with different growth intensity (< 900 and ≥ 900 g/day) on the microstructure of musculus longissimus lumborum. Commercial crosses of Polish Lowland black-and-white cows with Charolais and Limousin bulls were used in this study; within the particular genetic groups the hybrids had similar slaughter weight (447.6 and 517.2 kg) and age (526 and 606 days), respectively. The share of fibres with active tetrazole dehydragenase in the more intensively growing animals was smaller. For fibres with myofibrillar ATPase activity, the intensively growing animals produced higher standard deviation values than the other groups. Further analysis of the muscular tissue in this group revealed that out of the 24 muscles, 9 had giant fibres. In comparison with the less intensively growing animals, the muscles of the bulls that gained more than 900 g/day in weight were found to contain significantly less glycogen (P ≤ 0.01) and, consequently, the meat was less acidic. The difference of the pH ranged from 0.19 in the case of pH24 (P ≤ 0.01) to 0.06 for pH48 (P ≤ 0.01). It should be noted that the intensively growing animals were found to have a relatively high pH variability (SD = 0.69 and 0.49, respectively). The pH24 and pH48 values, as well as pH variability show that the meat of this group was dark, firm and dry.

scholarly journals Freewater EstimatoR using iNtErpolated iniTialization (FERNET): Toward Accurate Estimation of Free Water in Peritumoral Region Using Single-Shell Diffusion MRI Data

2019 ◽  
Author(s):  
Abdol Aziz Ould Ismail ◽  
Drew Parker ◽  
Moises Hernandez-Fernandez ◽  
Ronald Wolf ◽  
Steven Brem ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Free Water ◽  
Simulated Data ◽  
Compartment Model ◽  
Accurate Estimation ◽  
Imaging Data ◽  
Tissue Microstructure ◽  
Single Shell

ABSTRACTCharacterization of healthy versus pathological tissue is a key concern when modeling tissue microstructure in the peritumoral area, confounded by the presence of free water (e.g., edema). Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic, or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy and brain tumor datasets, demonstrating its applicability on clinically acquired data. Additionally, it has been applied to data from brain tumor patients to demonstrate the improvement in tractography in the peritumoral region.

scholarly journals Investigation of Human Cardiac Fiber Architecture with Multiple diffusion MRI schemes at Multi-Scales based on Simulation Data

2020 ◽  
Author(s):  
Hongbo DU ◽  
Lihui wang ◽  
Jianping HUANG
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
A Priori ◽  
False Negative ◽  
Quantitative Description ◽  
Ground Truth ◽  
Estimation Accuracy ◽  
Fiber Architecture ◽  
Diffusion Spectrum Imaging ◽  
Cardiac Fiber

Abstract Background Diffusion tensor imaging (DTI), diffusion spectrum imaging (DSI) and Q-ball imaging (QBI) are currently three main diffusion MRI (dMRI) schemes available for non-invasive investigation of cardiac fiber architecture. Although DSI and QBI have undoubtedly greater potential to reveal complex cardiac fiber structures than DTI, it however remains unclear to which level and at which scale they provide more gain for investigating cardiac fiber structure.Method This work attends to provide a quantitative description of cardiac fiber architecture derived from different schemes at various scales. Due to the limit of the spatial resolution of clinical MRI scanner and with the absence of the ground-truth, it is difficult to give the accurate description. To deal with this issue, we simulate firstly DTI, DSI and QBI of a cardiac fiber model with the structure a priori known at different scales, and then the estimation accuracy, the diffusion metrics and the helix and transverse angles of cardiac fiber obtained by different schemes at different scales are calculated. Results The results show that although DSI and QBI can distinguish multiple fiber orientations, they are readily to generate false positive and false negative fibers which influence therefore the estimation accuracy. When there are multiple fiber orientations in one voxel, the diffusion anisotropy detected by DTI is higher than DSI and QBI, the range of helix and transverse angle decreases with increasing of the scales, and that detected by DSI is larger than DTI and QBI. Conclusion The results showed that the proposed dMRI simulator provides a valuable tool for simulating realistic DW images of whole human hearts, which can be used as the gold standard to study the fiber structures of the heart.

Enhancement of ADP-induced Platelet Aggregation by Adrenaline in Vivo and Its Prevention

1973 ◽  
Vol 29 (02) ◽  
pp. 490-498 ◽  
Author(s):  
Hiroh Yamazaki ◽  
Itsuro Kobayashi ◽  
Tadahiro Sano ◽  
Takio Shimamoto
Keyword(s):  
Platelet Count ◽  
Platelet Aggregation ◽  
Platelet Rich Plasma ◽  
The Other ◽  
Endothelial Surface ◽  
Important Interaction ◽  
Blood Coagulability ◽  
The Difference

SummaryThe authors previously reported a transient decrease in adhesive platelet count and an enhancement of blood coagulability after administration of a small amount of adrenaline (0.1-1 µg per Kg, i. v.) in man and rabbit. In such circumstances, the sensitivity of platelets to aggregation induced by ADP was studied by an optical density method. Five minutes after i. v. injection of 1 µg per Kg of adrenaline in 10 rabbits, intensity of platelet aggregation increased to 115.1 ± 4.9% (mean ± S. E.) by 10∼5 molar, 121.8 ± 7.8% by 3 × 10-6 molar and 129.4 ± 12.8% of the value before the injection by 10”6 molar ADP. The difference was statistically significant (P<0.01-0.05). The above change was not observed in each group of rabbits injected with saline, 1 µg per Kg of 1-noradrenaline or 0.1 and 10 µg per Kg of adrenaline. Also, it was prevented by oral administration of 10 mg per Kg of phenoxybenzamine or propranolol or aspirin or pyridinolcarbamate 3 hours before the challenge. On the other hand, the enhancement of ADP-induced platelet aggregation was not observed in vitro, when 10-5 or 3 × 10-6 molar and 129.4 ± 12.8% of the value before 10∼6 molar ADP was added to citrated platelet rich plasma (CPRP) of rabbit after incubation at 37°C for 30 second with 0.01, 0.1, 1, 10 or 100 µg per ml of adrenaline or noradrenaline. These results suggest an important interaction between endothelial surface and platelets in connection with the enhancement of ADP-induced platelet aggregation by adrenaline in vivo.

Energy Approximation

2017 ◽  
Author(s):  
Philip Isett
Keyword(s):  
Main Lemma ◽  
The Other ◽  
Coarse Scale ◽  
Continuous Solutions ◽  
Material Derivative ◽  
Energy Variation ◽  
Energy Approximation ◽  
The Difference ◽  
Derivatives Of

This chapter presents the equations and calculations for energy approximation. It establishes the estimates (261) and (262) of the Main Lemma (10.1) for continuous solutions; these estimates state that we are able to accurately prescribe the energy that the correction adds to the solution, as well as bound the difference between the time derivatives of these two quantities. The chapter also introduces the proposition for prescribing energy, followed by the relevant computations. Each integral contributing to the other term can be estimated. Another proposition for estimating control over the rate of energy variation is given. Finally, the coarse scale material derivative is considered.

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