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How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning
Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it...
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Published in: | NeuroImage (Orlando, Fla.) Fla.), 2020-05, Vol.211, p.116646-116646, Article 116646 |
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description | Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes “sufficient” is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.
•Many factors influence how many streamlines are needed for reproducible tractograms.•These factors can alter required streamline counts by up to two orders of magnitude.•We supply means to compute required streamline counts for reproducible tractography.•The prospective methods cease streamline generation when stability criteria are met. |
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•Many factors influence how many streamlines are needed for reproducible tractograms.•These factors can alter required streamline counts by up to two orders of magnitude.•We supply means to compute required streamline counts for reproducible tractography.•The prospective methods cease streamline generation when stability criteria are met.</description><identifier>ISSN: 1053-8119</identifier><identifier>ISSN: 1095-9572</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2020.116646</identifier><identifier>PMID: 32084566</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Algorithms ; Bootstrapping ; Datasets as Topic ; Diffusion Tensor Imaging - methods ; Diffusion Tensor Imaging - standards ; Diffusion tractography ; Diffusion weighted imaging ; Estimates ; Humans ; Image processing ; Image Processing, Computer-Assisted - methods ; Image Processing, Computer-Assisted - standards ; Magnetic resonance imaging ; Methods ; Morphology ; Neurosurgery ; Patient safety ; Power analysis ; Prospective Studies ; Reproducibility ; Reproducibility of Results ; Streamline count ; Substantia alba ; White Matter - anatomy & histology ; White Matter - diagnostic imaging</subject><ispartof>NeuroImage (Orlando, Fla.), 2020-05, Vol.211, p.116646-116646, Article 116646</ispartof><rights>2020 The Authors</rights><rights>Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.</rights><rights>2020. The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-d9a7d4270b4261c2b7df280f4ccb1765851ae7482cec920f4962529c957763663</citedby><cites>FETCH-LOGICAL-c448t-d9a7d4270b4261c2b7df280f4ccb1765851ae7482cec920f4962529c957763663</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32084566$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Reid, Lee B.</creatorcontrib><creatorcontrib>Cespedes, Marcela I.</creatorcontrib><creatorcontrib>Pannek, Kerstin</creatorcontrib><title>How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes “sufficient” is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.
•Many factors influence how many streamlines are needed for reproducible tractograms.•These factors can alter required streamline counts by up to two orders of magnitude.•We supply means to compute required streamline counts for reproducible tractography.•The prospective methods cease streamline generation when stability criteria are met.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Bootstrapping</subject><subject>Datasets as Topic</subject><subject>Diffusion Tensor Imaging - methods</subject><subject>Diffusion Tensor Imaging - standards</subject><subject>Diffusion tractography</subject><subject>Diffusion weighted imaging</subject><subject>Estimates</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image Processing, Computer-Assisted - standards</subject><subject>Magnetic resonance imaging</subject><subject>Methods</subject><subject>Morphology</subject><subject>Neurosurgery</subject><subject>Patient safety</subject><subject>Power analysis</subject><subject>Prospective Studies</subject><subject>Reproducibility</subject><subject>Reproducibility of Results</subject><subject>Streamline count</subject><subject>Substantia alba</subject><subject>White Matter - anatomy & histology</subject><subject>White Matter - diagnostic imaging</subject><issn>1053-8119</issn><issn>1095-9572</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNqFUsuO1DAQjBCIfcAvIEtcuGSwHceJTwhWsLvSShyAs9VxOsGjxJ61E9D8BZ9Mz8yySFw42S5XV7vKXRRM8I3gQr_dbgKuKfoZRtxILgkWWiv9pDgX3NSlqRv59LCvq7IVwpwVFzlvOedGqPZ5cVZJ3qpa6_Pi1038yWYIe5aXhDBPPmBmkJAlvF99wp4NMdFh8tBNyHYpdtD5yefFO7YkcEscE-y-79-xL3FaFx9DPpbM3qVIoqtb1gQTmxHymnDGsFCD0LOjBYJG7-h6N0EIPowvimcDTBlfPqyXxbdPH79e3ZR3n69vr97flU6pdil7A02vZMM7JbVwsmv6QbZ8UM51otF1WwvARrXSoTOScKNlLY2jZBpdaV1dFrcn3T7C1u4SZZn2NoK3RyCm0UIijxNagwNQitjo3ijFWzACSd5BDxW4DknrzUmL0rlfMS929tnhRJYwrtnKSkuupTE1UV__Q93GNQVyaqUSDVeirQ-s9sQ6RJgTDo8PFNweJsBu7d8JsIcJsKcJoNJXDw3Wbsb-sfDPlxPhw4mAlO4Pj8lm5zE47Om33UL2_f-7_AYckMqX</recordid><startdate>20200501</startdate><enddate>20200501</enddate><creator>Reid, Lee B.</creator><creator>Cespedes, Marcela I.</creator><creator>Pannek, Kerstin</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>20200501</creationdate><title>How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning</title><author>Reid, Lee B. ; Cespedes, Marcela I. ; Pannek, Kerstin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-d9a7d4270b4261c2b7df280f4ccb1765851ae7482cec920f4962529c957763663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Bootstrapping</topic><topic>Datasets as Topic</topic><topic>Diffusion Tensor Imaging - methods</topic><topic>Diffusion Tensor Imaging - standards</topic><topic>Diffusion tractography</topic><topic>Diffusion weighted imaging</topic><topic>Estimates</topic><topic>Humans</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Image Processing, Computer-Assisted - standards</topic><topic>Magnetic resonance imaging</topic><topic>Methods</topic><topic>Morphology</topic><topic>Neurosurgery</topic><topic>Patient safety</topic><topic>Power analysis</topic><topic>Prospective Studies</topic><topic>Reproducibility</topic><topic>Reproducibility of Results</topic><topic>Streamline count</topic><topic>Substantia alba</topic><topic>White Matter - anatomy & histology</topic><topic>White Matter - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reid, Lee B.</creatorcontrib><creatorcontrib>Cespedes, Marcela I.</creatorcontrib><creatorcontrib>Pannek, Kerstin</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>NeuroImage (Orlando, Fla.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Reid, Lee B.</au><au>Cespedes, Marcela I.</au><au>Pannek, Kerstin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2020-05-01</date><risdate>2020</risdate><volume>211</volume><spage>116646</spage><epage>116646</epage><pages>116646-116646</pages><artnum>116646</artnum><issn>1053-8119</issn><issn>1095-9572</issn><eissn>1095-9572</eissn><abstract>Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes “sufficient” is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.
•Many factors influence how many streamlines are needed for reproducible tractograms.•These factors can alter required streamline counts by up to two orders of magnitude.•We supply means to compute required streamline counts for reproducible tractography.•The prospective methods cease streamline generation when stability criteria are met.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32084566</pmid><doi>10.1016/j.neuroimage.2020.116646</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Algorithms Bootstrapping Datasets as Topic Diffusion Tensor Imaging - methods Diffusion Tensor Imaging - standards Diffusion tractography Diffusion weighted imaging Estimates Humans Image processing Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Magnetic resonance imaging Methods Morphology Neurosurgery Patient safety Power analysis Prospective Studies Reproducibility Reproducibility of Results Streamline count Substantia alba White Matter - anatomy & histology White Matter - diagnostic imaging |
title | How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning |
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