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Diffusion propagator metrics are biased when simultaneous multi-slice acceleration is used
[article]
2021
arXiv
pre-print
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This has implications for studies using diffusion MRI with SMS acceleration to investigate the effects of a disease or injury on the brain tissues. ...
In this study, the effects of SMS acceleration on the accuracy of propagator metrics obtained from the MAP-MRI technique was investigated. ...
The q-space sampling was optimized for accuracy of MAP-MRI metrics using a genetic search algorithm published earlier [21] . ...
arXiv:2103.13340v3
fatcat:onarm7sgcjhdndhnjfm5aypabq
Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
2021
Frontiers in Physics
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We use the "select and retrieve via direct upsampling" (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. ...
SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design. ...
Examples include the design of optimal diffusion-weighting protocols [4, 5, [23] [24] [25] [26] ; number and spacing of temporal sampling in relaxometry [27, 28] ; DRI sampling [18] . ...
doi:10.3389/fphy.2021.752208
fatcat:jclcpirsdfgmjnuvggc3hpskpm
Reducing the number of samples in spatiotemporal dMRI acquisition design
2018
Magnetic Resonance in Medicine
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The authors introduce an acquisition scheme that reduces the number of samples under adjustable quality loss. ...
Acquisition time is a major limitation in recovering brain white matter microstructure with diffusion magnetic resonance imaging. ...
We use Standard Genetic Algorithm (SGA) [33, 46, 49] for this purpose, which allows us to find approximate solutions in acceptable time. ...
doi:10.1002/mrm.27601
pmid:30450755
fatcat:35pz66yxg5hdhk6edms3hpetdy
Computer-Assisted Analysis of Biomedical Images
[article]
2021
arXiv
pre-print
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As a matter of fact, the proposed computer-assisted bioimage analysis methods can be beneficial for the definition of imaging biomarkers, as well as for quantitative medicine and biology. ...
Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications. ...
Genetic Algorithms Genetic Algorithms (GAs) represent an Evolutionary Computation technique for global optimization tasks [166] . ...
arXiv:2106.04381v1
fatcat:osqiyd3sbja3zgrby7bf4eljfm
Diffusion magnetic resonance imaging for Brainnetome: A critical review
2012
Neuroscience Bulletin
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The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the "Brainnetome" (brain-net-ome) project was proposed. ...
This review focuses on one of the most promising techniques, diffusion magnetic resonance imaging (dMRI), and its use for modeling and analysis in the Brainnetome. tions of sub-networks are also attractive ...
The black dot in q = (0, 0, 0)T is the baseline image without a diffusion gradient. Note that although we showed sampling in R3, normally only samples in a half-space are used, e.g. qz ≥0. ...
doi:10.1007/s12264-012-1245-3
pmid:22833036
pmcid:PMC5560260
fatcat:7zsg3b4hsfbypjqgtdeq66ar6y
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
2013
NeuroImage
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Due to the ease of use of such implementations, and the plausibility of some of their results, DTI was leapt on by imaging neuroscientists who saw it as a powerful and unique new tool for exploring the ...
In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of ...
However, full reconstruction of the diffusion propagator is not possible, as it would require infinite sampling of the q-space (space spanned by gradient directions and b-values). ...
doi:10.1016/j.neuroimage.2012.06.081
pmid:22846632
fatcat:4vw3xz3u4rdyhf2fi7rrwo43hu
A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging
[article]
2019
biorxiv/medrxiv
pre-print
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In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity ...
Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. ...
The predicted variance in the mean per-shell signal can be used to predict the variance in the estimated basis coefficients using the law of propagation of errors (Arras, 1998) . ...
doi:10.1101/661348
fatcat:lryakaesxvcszbnmmuzjicsxb4
"Select and retrieve via direct upsampling" network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design
[article]
2020
bioRxiv
pre-print
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The algorithm consists of two deep neural networks (DNNs) that are trained jointly end-to-end: a selector, identifying a subset of input qMRI measurements, and a predictor, estimating fully-sampled signals ...
The reproducibility of the sub-protocol selection was evaluated, and sub-protocols were assessed for their potential of informing multi-contrast analysis, as for example Hybrid Multi-dimensional MRI (HM-MRI ...
Jin for useful discussion. ...
doi:10.1101/2020.05.26.116491
fatcat:tuon35oeujctdpzvkz7o5nkhg4
A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging
2020
NMR in Biomedicine
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In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity ...
Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. ...
Joint optimisation of both angular and b-value dependences requires extended orthonormal q-space decompositions (D), 48 and is the subject of ongoing work. ...
doi:10.1002/nbm.4348
pmid:32632961
pmcid:PMC7116416
fatcat:j3z6vnnaczfdzddg62z72fwgky
Mapping population-based structural connectomes
2018
NeuroImage
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Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a ...
A robust tractography algorithm and and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness ...
We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data. ...
doi:10.1016/j.neuroimage.2017.12.064
pmid:29355769
pmcid:PMC5910206
fatcat:zm3436yv7newzbv2pqyq63vwae
Deep-learning-based Optimization of the Under-sampling Pattern in MRI
[article]
2020
arXiv
pre-print
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In this paper, we tackle both problems simultaneously for the specific case of 2D Cartesian sampling, using a novel end-to-end learning framework that we call LOUPE (Learning-based Optimization of the ...
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to achieve accelerated scan times. ...
Building on this approach, Curtis et al. employed a genetic algorithm to optimize sampling trajectories in k-space, while accounting for multi-coil configurations [34] . ...
arXiv:1907.11374v3
fatcat:gahgolokgrdexd7jyqi4hshvuu
fNIRS improves seizure detection in multimodal EEG-fNIRS recordings
2019
Journal of Biomedical Optics
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Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task ...
After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS ...
Truncated back propagation through time, a modified form of the conventional back propagation through time (BPTT) training algorithm for RNNs, 38 was used for training. ...
doi:10.1117/1.jbo.24.5.051408
pmid:30734544
pmcid:PMC6992892
fatcat:sirftgoxxvfnfghvt6zm36ikda
Disease-Specific Brain Atlases
[chapter]
2000
Brain Mapping: The Disorders
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National Institute of Mental Health (NINDS/NIMH NS38753), and by a Human Brain Project grant to the International Consortium for Brain Mapping, funded jointly by NIMH and NIDA (P20 MH/DA52176). ...
Acknowledgments This work was supported by research grants from the National Center for Research Resources (P41 RR13642 and RR05956), the National Institute of Neurological Disorders and Stroke and the ...
Specialized algorithms, using corrections for the metric tensor of the underlying surface, are required to calculate these fields at the cortex (see next Section). ...
doi:10.1016/b978-012481460-8/50009-3
fatcat:63fa3fvfdbbf3hq6qm4dy5nwny
A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
2021
Biology
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At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. ...
It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future. ...
Acknowledgments: Thanks to Si-Yuan Lu for his contribution to the revision of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/biology10111084
pmid:34827077
pmcid:PMC8615026
fatcat:dr3b5ozqx5eppdqoara4wctefq
Congenital aortic disease: 4D magnetic resonance segmentation and quantitative analysis
2009
Medical Image Analysis
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Starting with a step of multi-view image registration, our automated segmentation method combines level-set and optimal surface segmentation algorithms in a single optimization process so that the final ...
MR datasets were used for development and performance evaluation of our method. ...
Van Waning for their contribution to the project. This work was supported, in part, by the NIH grants R01HL071809 and R0lEB004640. ...
doi:10.1016/j.media.2009.02.005
pmid:19303351
pmcid:PMC2727644
fatcat:hblw3jldinhyfewbokjoztf6om
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