Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2020-05, Vol.211, p.116646-116646, Article 116646
Main Authors: Reid, Lee B., Cespedes, Marcela I., Pannek, Kerstin
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c448t-d9a7d4270b4261c2b7df280f4ccb1765851ae7482cec920f4962529c957763663
cites cdi_FETCH-LOGICAL-c448t-d9a7d4270b4261c2b7df280f4ccb1765851ae7482cec920f4962529c957763663
container_end_page 116646
container_issue
container_start_page 116646
container_title NeuroImage (Orlando, Fla.)
container_volume 211
creator Reid, Lee B.
Cespedes, Marcela I.
Pannek, Kerstin
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.
doi_str_mv 10.1016/j.neuroimage.2020.116646
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_9efa811e76d94408a91e51acada3acbe</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1053811920301336</els_id><doaj_id>oai_doaj_org_article_9efa811e76d94408a91e51acada3acbe</doaj_id><sourcerecordid>2362062995</sourcerecordid><originalsourceid>FETCH-LOGICAL-c448t-d9a7d4270b4261c2b7df280f4ccb1765851ae7482cec920f4962529c957763663</originalsourceid><addsrcrecordid>eNqFUsuO1DAQjBCIfcAvIEtcuGSwHceJTwhWsLvSShyAs9VxOsGjxJ61E9D8BZ9Mz8yySFw42S5XV7vKXRRM8I3gQr_dbgKuKfoZRtxILgkWWiv9pDgX3NSlqRv59LCvq7IVwpwVFzlvOedGqPZ5cVZJ3qpa6_Pi1038yWYIe5aXhDBPPmBmkJAlvF99wp4NMdFh8tBNyHYpdtD5yefFO7YkcEscE-y-79-xL3FaFx9DPpbM3qVIoqtb1gQTmxHymnDGsFCD0LOjBYJG7-h6N0EIPowvimcDTBlfPqyXxbdPH79e3ZR3n69vr97flU6pdil7A02vZMM7JbVwsmv6QbZ8UM51otF1WwvARrXSoTOScKNlLY2jZBpdaV1dFrcn3T7C1u4SZZn2NoK3RyCm0UIijxNagwNQitjo3ijFWzACSd5BDxW4DknrzUmL0rlfMS929tnhRJYwrtnKSkuupTE1UV__Q93GNQVyaqUSDVeirQ-s9sQ6RJgTDo8PFNweJsBu7d8JsIcJsKcJoNJXDw3Wbsb-sfDPlxPhw4mAlO4Pj8lm5zE47Om33UL2_f-7_AYckMqX</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2417041855</pqid></control><display><type>article</type><title>How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning</title><source>ScienceDirect Freedom Collection</source><creator>Reid, Lee B. ; Cespedes, Marcela I. ; Pannek, Kerstin</creator><creatorcontrib>Reid, Lee B. ; Cespedes, Marcela I. ; Pannek, Kerstin</creatorcontrib><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><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 &amp; 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 &amp; 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 &amp; 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 &amp; 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 &amp; Medical Complete (Alumni)</collection><collection>Biological Sciences</collection><collection>Health &amp; 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>
fulltext fulltext
identifier ISSN: 1053-8119
ispartof NeuroImage (Orlando, Fla.), 2020-05, Vol.211, p.116646-116646, Article 116646
issn 1053-8119
1095-9572
1095-9572
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_9efa811e76d94408a91e51acada3acbe
source ScienceDirect Freedom Collection
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T19%3A22%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=How%20many%20streamlines%20are%20required%20for%20reliable%20probabilistic%20tractography?%20Solutions%20for%20microstructural%20measurements%20and%20neurosurgical%20planning&rft.jtitle=NeuroImage%20(Orlando,%20Fla.)&rft.au=Reid,%20Lee%20B.&rft.date=2020-05-01&rft.volume=211&rft.spage=116646&rft.epage=116646&rft.pages=116646-116646&rft.artnum=116646&rft.issn=1053-8119&rft.eissn=1095-9572&rft_id=info:doi/10.1016/j.neuroimage.2020.116646&rft_dat=%3Cproquest_doaj_%3E2362062995%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c448t-d9a7d4270b4261c2b7df280f4ccb1765851ae7482cec920f4962529c957763663%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2417041855&rft_id=info:pmid/32084566&rfr_iscdi=true