Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach
Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable...
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| Veröffentlicht in: | NeuroImage (Orlando, Fla.) Fla.), 2017-02, Vol.147, p.964-975 |
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| creator | Reisert, Marco Kellner, Elias Dhital, Bibek Hennig, Jürgen Kiselev, Valerij G. |
| description | Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications.
•Disentanglement of microstructural properties of neurites from their orientation distribution.•Microstructure estimation from clinical feasible dMRI, including fast protocols (as few as 28 diffusion weighting directions).•Computation time of seconds.•In-vivo results are consistent with existing anatomical knowledge. |
| doi_str_mv | 10.1016/j.neuroimage.2016.09.058 |
| format | Article |
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•Disentanglement of microstructural properties of neurites from their orientation distribution.•Microstructure estimation from clinical feasible dMRI, including fast protocols (as few as 28 diffusion weighting directions).•Computation time of seconds.•In-vivo results are consistent with existing anatomical knowledge.</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2016.09.058</identifier><identifier>PMID: 27746388</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Artificial intelligence ; Axonal density ; Bayes Theorem ; Bayesian analysis ; Brain ; Diffusion Magnetic Resonance Imaging - methods ; Diffusion Magnetic Resonance Imaging - standards ; Diffusion MRI ; Fibers ; Humans ; Image Processing, Computer-Assisted - methods ; Image Processing, Computer-Assisted - standards ; Learning algorithms ; Magnetic resonance imaging ; Microstructural parameters ; Microstructure imaging ; Models, Neurological ; Multi-shell dMRI ; Neurites ; Neuroimaging ; NMR ; Noise ; Nuclear magnetic resonance ; Probes ; White matter ; White Matter - diagnostic imaging</subject><ispartof>NeuroImage (Orlando, Fla.), 2017-02, Vol.147, p.964-975</ispartof><rights>2016 The Authors</rights><rights>Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Feb 15, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c518t-b26bef8b03203d238f3ebfa6f9cbaf3978248fee8ab636d72f7fa4797cb4a2cf3</citedby><cites>FETCH-LOGICAL-c518t-b26bef8b03203d238f3ebfa6f9cbaf3978248fee8ab636d72f7fa4797cb4a2cf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1053811916305353$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27746388$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Reisert, Marco</creatorcontrib><creatorcontrib>Kellner, Elias</creatorcontrib><creatorcontrib>Dhital, Bibek</creatorcontrib><creatorcontrib>Hennig, Jürgen</creatorcontrib><creatorcontrib>Kiselev, Valerij G.</creatorcontrib><title>Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>Diffusion-sensitized magnetic resonance imaging probes the cellular structure of the human brain, but the primary microstructural information gets lost in averaging over higher-level, mesoscopic tissue organization such as different orientations of neuronal fibers. While such averaging is inevitable due to the limited imaging resolution, we propose a method for disentangling the microscopic cell properties from the effects of mesoscopic structure. We further avoid the classical fitting paradigm and use supervised machine learning in terms of a Bayesian estimator to estimate the microstructural properties. The method finds detectable parameters of a given microstructural model and calculates them within seconds, which makes it suitable for a broad range of neuroscientific applications.
•Disentanglement of microstructural properties of neurites from their orientation distribution.•Microstructure estimation from clinical feasible dMRI, including fast protocols (as few as 28 diffusion weighting directions).•Computation time of seconds.•In-vivo results are consistent with existing anatomical knowledge.</description><subject>Adult</subject><subject>Artificial intelligence</subject><subject>Axonal density</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Brain</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Diffusion Magnetic Resonance Imaging - standards</subject><subject>Diffusion MRI</subject><subject>Fibers</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image Processing, Computer-Assisted - standards</subject><subject>Learning algorithms</subject><subject>Magnetic resonance imaging</subject><subject>Microstructural parameters</subject><subject>Microstructure imaging</subject><subject>Models, Neurological</subject><subject>Multi-shell dMRI</subject><subject>Neurites</subject><subject>Neuroimaging</subject><subject>NMR</subject><subject>Noise</subject><subject>Nuclear magnetic resonance</subject><subject>Probes</subject><subject>White matter</subject><subject>White Matter - 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•Disentanglement of microstructural properties of neurites from their orientation distribution.•Microstructure estimation from clinical feasible dMRI, including fast protocols (as few as 28 diffusion weighting directions).•Computation time of seconds.•In-vivo results are consistent with existing anatomical knowledge.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>27746388</pmid><doi>10.1016/j.neuroimage.2016.09.058</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Artificial intelligence Axonal density Bayes Theorem Bayesian analysis Brain Diffusion Magnetic Resonance Imaging - methods Diffusion Magnetic Resonance Imaging - standards Diffusion MRI Fibers Humans Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Learning algorithms Magnetic resonance imaging Microstructural parameters Microstructure imaging Models, Neurological Multi-shell dMRI Neurites Neuroimaging NMR Noise Nuclear magnetic resonance Probes White matter White Matter - diagnostic imaging |
| title | Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach |
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