Filters








951 Hits in 7.5 sec

Diffusion propagator metrics are biased when simultaneous multi-slice acceleration is used [article]

L. Tugan Muftuler, Andrew S. Nencka, Kevin M. Koch
2021 arXiv   pre-print
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

Francesco Grussu, Stefano B. Blumberg, Marco Battiston, Lebina S. Kakkar, Hongxiang Lin, Andrada Ianuş, Torben Schneider, Saurabh Singh, Roger Bourne, Shonit Punwani, David Atkinson, Claudia A. M. Gandini Wheeler-Kingshott (+3 others)
2021 Frontiers in Physics  
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

Patryk Filipiak, Rutger Fick, Alexandra Petiet, Mathieu Santin, Anne-Charlotte Philippe, Stephane Lehericy, Philippe Ciuciu, Rachid Deriche, Demian Wassermann
2018 Magnetic Resonance in Medicine  
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]

Leonardo Rundo
2021 arXiv   pre-print
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

Nianming Zuo, Jian Cheng, Tianzi Jiang
2012 Neuroscience Bulletin  
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

Derek K. Jones, Thomas R. Knösche, Robert Turner
2013 NeuroImage  
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]

J-Donald Tournier, Daan Christiaens, Jana Hutter, Anthony Price, lucilio Cordero-Grande, Emer Hughes, Matteo Bastiani, stamatios Sotiropoulos, Stephen M. Smith, Daniel Rueckert, Serena J. Counsell, A. David Edwards (+1 others)
2019 biorxiv/medrxiv   pre-print
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]

Francesco Grussu, Stefano B. Blumberg, Marco Battiston, Lebina S. Kakkar, Hongxiang Lin, Andrada Ianus, Torben Schneider, Saurabh Singh, Roger Bourne, Shonit Punwani, David Atkinson, Claudia A. M. Gandini Wheeler-kingshott (+3 others)
2020 bioRxiv   pre-print
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

Jacques-Donald Tournier, Daan Christiaens, Jana Hutter, Anthony N Price, Lucilio Cordero-Grande, Emer Hughes, Matteo Bastiani, Stamatios N Sotiropoulos, Stephen M Smith, Daniel Rueckert, Serena J Counsell, A David Edwards (+1 others)
2020 NMR in Biomedicine  
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

Zhengwu Zhang, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, David Dunson, Anuj Srivastava, Hongtu Zhu
2018 NeuroImage  
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]

Cagla D. Bahadir, Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
2020 arXiv   pre-print
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

Parikshat Sirpal, Ali Kassab, Philippe Pouliot, Dang Khoa Nguyen
2019 Journal of Biomedical Optics  
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]

Paul M. Thompson, Michael S. Mega, Arthur W. Toga
2000 Brain Mapping: The Disorders  
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

Yan Yan, Xu-Jing Yao, Shui-Hua Wang, Yu-Dong Zhang
2021 Biology  
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

Fei Zhao, Honghai Zhang, Andreas Wahle, Matthew T. Thomas, Alan H. Stolpen, Thomas D. Scholz, Milan Sonka
2009 Medical Image Analysis  
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
« Previous Showing results 1 — 15 out of 951 results