Retinal vessel multifractals predict pial collateral status in patients with acute ischemic stroke
Pial collateral blood flow is a major determinant of the outcomes of acute ischemic stroke. This study was undertaken to determine whether retinal vessel metrics can predict the pial collateral status and stroke outcomes in patients. Thirty-five patients with acute stroke secondary to middle cerebra...
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| Veröffentlicht in: | PloS one 2022-05, Vol.17 (5), p.e0267837 |
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| creator | Khan, Adnan De Boever, Patrick Gerrits, Nele Akhtar, Naveed Saqqur, Maher Ponirakis, Georgios Gad, Hoda Petropoulos, Ioannis N Shuaib, Ashfaq Faber, James E Kamran, Saadat Malik, Rayaz A |
| description | Pial collateral blood flow is a major determinant of the outcomes of acute ischemic stroke. This study was undertaken to determine whether retinal vessel metrics can predict the pial collateral status and stroke outcomes in patients.
Thirty-five patients with acute stroke secondary to middle cerebral artery (MCA) occlusion underwent grading of their pial collateral status from computed tomography angiography and retinal vessel analysis from retinal fundus images.
The NIHSS (14.7 ± 5.5 vs 10.1 ± 5.8, p = 0.026) and mRS (2.9 ± 1.6 vs 1.9 ± 1.3, p = 0.048) scores were higher at admission in patients with poor compared to good pial collaterals. Retinal vessel multifractals: D0 (1.673±0.028vs1.652±0.025, p = 0.028), D1 (1.609±0.027vs1.590±0.025, p = 0.044) and f(α)max (1.674±0.027vs1.652±0.024, p = 0.019) were higher in patients with poor compared to good pial collaterals. Furthermore, support vector machine learning achieved a fair sensitivity (0.743) and specificity (0.707) for differentiating patients with poor from good pial collaterals. Age (p = 0.702), BMI (p = 0.422), total cholesterol (p = 0.842), triglycerides (p = 0.673), LDL (p = 0.952), HDL (p = 0.366), systolic blood pressure (p = 0.727), HbA1c (p = 0.261) and standard retinal metrics including CRAE (p = 0.084), CRVE (p = 0.946), AVR (p = 0.148), tortuosity index (p = 0.790), monofractal Df (p = 0.576), lacunarity (p = 0.531), curve asymmetry (p = 0.679) and singularity length (p = 0.937) did not differ between patients with poor compared to good pial collaterals.
This is the first translational study to show increased retinal vessel multifractal dimensions in patients with acute ischemic stroke and poor pial collaterals. A retinal vessel classifier was developed to differentiate between patients with poor and good pial collaterals and may allow rapid non-invasive identification of patients with poor pial collaterals. |
| doi_str_mv | 10.1371/journal.pone.0267837 |
| format | Article |
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Thirty-five patients with acute stroke secondary to middle cerebral artery (MCA) occlusion underwent grading of their pial collateral status from computed tomography angiography and retinal vessel analysis from retinal fundus images.
The NIHSS (14.7 ± 5.5 vs 10.1 ± 5.8, p = 0.026) and mRS (2.9 ± 1.6 vs 1.9 ± 1.3, p = 0.048) scores were higher at admission in patients with poor compared to good pial collaterals. Retinal vessel multifractals: D0 (1.673±0.028vs1.652±0.025, p = 0.028), D1 (1.609±0.027vs1.590±0.025, p = 0.044) and f(α)max (1.674±0.027vs1.652±0.024, p = 0.019) were higher in patients with poor compared to good pial collaterals. Furthermore, support vector machine learning achieved a fair sensitivity (0.743) and specificity (0.707) for differentiating patients with poor from good pial collaterals. Age (p = 0.702), BMI (p = 0.422), total cholesterol (p = 0.842), triglycerides (p = 0.673), LDL (p = 0.952), HDL (p = 0.366), systolic blood pressure (p = 0.727), HbA1c (p = 0.261) and standard retinal metrics including CRAE (p = 0.084), CRVE (p = 0.946), AVR (p = 0.148), tortuosity index (p = 0.790), monofractal Df (p = 0.576), lacunarity (p = 0.531), curve asymmetry (p = 0.679) and singularity length (p = 0.937) did not differ between patients with poor compared to good pial collaterals.
This is the first translational study to show increased retinal vessel multifractal dimensions in patients with acute ischemic stroke and poor pial collaterals. A retinal vessel classifier was developed to differentiate between patients with poor and good pial collaterals and may allow rapid non-invasive identification of patients with poor pial collaterals.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0267837</identifier><identifier>PMID: 35511879</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Angiography ; Biology and Life Sciences ; Blood flow ; Blood pressure ; Blood vessels ; Brain Ischemia - diagnostic imaging ; Care and treatment ; Cerebral Angiography - methods ; Cerebral blood flow ; Cholesterol ; Collateral Circulation - physiology ; Computed tomography ; Computer and Information Sciences ; CT imaging ; Evaluation ; Fractal geometry ; Fractals ; High density lipoprotein ; Humans ; Infarction, Middle Cerebral Artery ; Ischemia ; Ischemic Stroke ; Low density lipoprotein ; Machine learning ; Medicine and Health Sciences ; Occlusion ; Physical Sciences ; Research and Analysis Methods ; Retina ; Retinal Vessels - diagnostic imaging ; Retrospective Studies ; Stroke ; Stroke (Disease) ; Stroke - diagnostic imaging ; Support vector machines ; Tomography ; Tortuosity ; Triglycerides ; Veins & arteries</subject><ispartof>PloS one, 2022-05, Vol.17 (5), p.e0267837</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Khan et al 2022 Khan et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-9130b9c5cee778e094ff4ba2d0c3c14fd82b40b85e5496a97243d65a447632be3</citedby><cites>FETCH-LOGICAL-c692t-9130b9c5cee778e094ff4ba2d0c3c14fd82b40b85e5496a97243d65a447632be3</cites><orcidid>0000-0003-4647-6672 ; 0000-0002-5197-8215 ; 0000-0002-1126-7638</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070887/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070887/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35511879$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Khan, Adnan</creatorcontrib><creatorcontrib>De Boever, Patrick</creatorcontrib><creatorcontrib>Gerrits, Nele</creatorcontrib><creatorcontrib>Akhtar, Naveed</creatorcontrib><creatorcontrib>Saqqur, Maher</creatorcontrib><creatorcontrib>Ponirakis, Georgios</creatorcontrib><creatorcontrib>Gad, Hoda</creatorcontrib><creatorcontrib>Petropoulos, Ioannis N</creatorcontrib><creatorcontrib>Shuaib, Ashfaq</creatorcontrib><creatorcontrib>Faber, James E</creatorcontrib><creatorcontrib>Kamran, Saadat</creatorcontrib><creatorcontrib>Malik, Rayaz A</creatorcontrib><title>Retinal vessel multifractals predict pial collateral status in patients with acute ischemic stroke</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Pial collateral blood flow is a major determinant of the outcomes of acute ischemic stroke. This study was undertaken to determine whether retinal vessel metrics can predict the pial collateral status and stroke outcomes in patients.
Thirty-five patients with acute stroke secondary to middle cerebral artery (MCA) occlusion underwent grading of their pial collateral status from computed tomography angiography and retinal vessel analysis from retinal fundus images.
The NIHSS (14.7 ± 5.5 vs 10.1 ± 5.8, p = 0.026) and mRS (2.9 ± 1.6 vs 1.9 ± 1.3, p = 0.048) scores were higher at admission in patients with poor compared to good pial collaterals. Retinal vessel multifractals: D0 (1.673±0.028vs1.652±0.025, p = 0.028), D1 (1.609±0.027vs1.590±0.025, p = 0.044) and f(α)max (1.674±0.027vs1.652±0.024, p = 0.019) were higher in patients with poor compared to good pial collaterals. Furthermore, support vector machine learning achieved a fair sensitivity (0.743) and specificity (0.707) for differentiating patients with poor from good pial collaterals. Age (p = 0.702), BMI (p = 0.422), total cholesterol (p = 0.842), triglycerides (p = 0.673), LDL (p = 0.952), HDL (p = 0.366), systolic blood pressure (p = 0.727), HbA1c (p = 0.261) and standard retinal metrics including CRAE (p = 0.084), CRVE (p = 0.946), AVR (p = 0.148), tortuosity index (p = 0.790), monofractal Df (p = 0.576), lacunarity (p = 0.531), curve asymmetry (p = 0.679) and singularity length (p = 0.937) did not differ between patients with poor compared to good pial collaterals.
This is the first translational study to show increased retinal vessel multifractal dimensions in patients with acute ischemic stroke and poor pial collaterals. A retinal vessel classifier was developed to differentiate between patients with poor and good pial collaterals and may allow rapid non-invasive identification of patients with poor pial collaterals.</description><subject>Algorithms</subject><subject>Angiography</subject><subject>Biology and Life Sciences</subject><subject>Blood flow</subject><subject>Blood pressure</subject><subject>Blood vessels</subject><subject>Brain Ischemia - diagnostic imaging</subject><subject>Care and treatment</subject><subject>Cerebral Angiography - methods</subject><subject>Cerebral blood flow</subject><subject>Cholesterol</subject><subject>Collateral Circulation - physiology</subject><subject>Computed tomography</subject><subject>Computer and Information Sciences</subject><subject>CT imaging</subject><subject>Evaluation</subject><subject>Fractal geometry</subject><subject>Fractals</subject><subject>High density lipoprotein</subject><subject>Humans</subject><subject>Infarction, Middle Cerebral Artery</subject><subject>Ischemia</subject><subject>Ischemic Stroke</subject><subject>Low density lipoprotein</subject><subject>Machine learning</subject><subject>Medicine and Health Sciences</subject><subject>Occlusion</subject><subject>Physical Sciences</subject><subject>Research and Analysis Methods</subject><subject>Retina</subject><subject>Retinal Vessels - diagnostic imaging</subject><subject>Retrospective Studies</subject><subject>Stroke</subject><subject>Stroke (Disease)</subject><subject>Stroke - diagnostic imaging</subject><subject>Support vector machines</subject><subject>Tomography</subject><subject>Tortuosity</subject><subject>Triglycerides</subject><subject>Veins & arteries</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNkluL1DAYhoso7rr6D0QLguDFjDk1aW6EZfEwsLCwHm5Dmn6dZmybbpKu-u_NON1lCgrSi4b0-d6GJ2-WPcdojanAb3du8oPu1qMbYI0IFyUVD7JTLClZcYLow6P1SfYkhB1CBS05f5yd0KLAuBTyNKuuIdoUk99CCNDl_dRF23htou5CPnqorYn5aBNhXNfpCD4tQ9RxCrkd8lFHC0MM-Q8b21ybKUJug2mhtyZh3n2Hp9mjJoXBs_l9ln398P7LxafV5dXHzcX55cpwSeJKYooqaQoDIEQJSLKmYZUmNTLUYNbUJakYqsoCCia5loIwWvNCMyY4JRXQs-zlIXfsXFCznqAIL5MCKQlPxOZA1E7v1Ohtr_0v5bRVfzac3yrtozUdKExqgTAHihll0oAsYO9OCKhoYRqTst7Nf5uqHmqTJCQzi9Dll8G2autulUQClaVIAa_mAO9uJgjxH0eeqa1Op7JD41KY6ZNidS7SraOiJDJR679Q6an395AK0ti0vxh4sxhITISfcaunENTm8_X_s1ffluzrI7YF3cU2uG6K1g1hCbIDaLwLwUNzbw4jte_3nQ2177ea-53GXhxbvx-6KzT9DXjI9po</recordid><startdate>20220505</startdate><enddate>20220505</enddate><creator>Khan, Adnan</creator><creator>De Boever, Patrick</creator><creator>Gerrits, Nele</creator><creator>Akhtar, Naveed</creator><creator>Saqqur, Maher</creator><creator>Ponirakis, Georgios</creator><creator>Gad, Hoda</creator><creator>Petropoulos, Ioannis N</creator><creator>Shuaib, Ashfaq</creator><creator>Faber, James E</creator><creator>Kamran, Saadat</creator><creator>Malik, Rayaz A</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4647-6672</orcidid><orcidid>https://orcid.org/0000-0002-5197-8215</orcidid><orcidid>https://orcid.org/0000-0002-1126-7638</orcidid></search><sort><creationdate>20220505</creationdate><title>Retinal vessel multifractals predict pial collateral status in patients with acute ischemic stroke</title><author>Khan, Adnan ; De Boever, Patrick ; Gerrits, Nele ; Akhtar, Naveed ; Saqqur, Maher ; Ponirakis, Georgios ; Gad, Hoda ; Petropoulos, Ioannis N ; Shuaib, Ashfaq ; Faber, James E ; Kamran, Saadat ; Malik, Rayaz A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-9130b9c5cee778e094ff4ba2d0c3c14fd82b40b85e5496a97243d65a447632be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Angiography</topic><topic>Biology and Life Sciences</topic><topic>Blood flow</topic><topic>Blood pressure</topic><topic>Blood vessels</topic><topic>Brain Ischemia - diagnostic imaging</topic><topic>Care and treatment</topic><topic>Cerebral Angiography - methods</topic><topic>Cerebral blood flow</topic><topic>Cholesterol</topic><topic>Collateral Circulation - physiology</topic><topic>Computed tomography</topic><topic>Computer and Information Sciences</topic><topic>CT imaging</topic><topic>Evaluation</topic><topic>Fractal geometry</topic><topic>Fractals</topic><topic>High density lipoprotein</topic><topic>Humans</topic><topic>Infarction, Middle Cerebral Artery</topic><topic>Ischemia</topic><topic>Ischemic Stroke</topic><topic>Low density lipoprotein</topic><topic>Machine learning</topic><topic>Medicine and Health Sciences</topic><topic>Occlusion</topic><topic>Physical Sciences</topic><topic>Research and Analysis Methods</topic><topic>Retina</topic><topic>Retinal Vessels - diagnostic imaging</topic><topic>Retrospective Studies</topic><topic>Stroke</topic><topic>Stroke (Disease)</topic><topic>Stroke - diagnostic imaging</topic><topic>Support vector machines</topic><topic>Tomography</topic><topic>Tortuosity</topic><topic>Triglycerides</topic><topic>Veins & arteries</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khan, Adnan</creatorcontrib><creatorcontrib>De Boever, Patrick</creatorcontrib><creatorcontrib>Gerrits, Nele</creatorcontrib><creatorcontrib>Akhtar, Naveed</creatorcontrib><creatorcontrib>Saqqur, Maher</creatorcontrib><creatorcontrib>Ponirakis, Georgios</creatorcontrib><creatorcontrib>Gad, Hoda</creatorcontrib><creatorcontrib>Petropoulos, Ioannis N</creatorcontrib><creatorcontrib>Shuaib, Ashfaq</creatorcontrib><creatorcontrib>Faber, James E</creatorcontrib><creatorcontrib>Kamran, Saadat</creatorcontrib><creatorcontrib>Malik, Rayaz A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</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>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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This study was undertaken to determine whether retinal vessel metrics can predict the pial collateral status and stroke outcomes in patients.
Thirty-five patients with acute stroke secondary to middle cerebral artery (MCA) occlusion underwent grading of their pial collateral status from computed tomography angiography and retinal vessel analysis from retinal fundus images.
The NIHSS (14.7 ± 5.5 vs 10.1 ± 5.8, p = 0.026) and mRS (2.9 ± 1.6 vs 1.9 ± 1.3, p = 0.048) scores were higher at admission in patients with poor compared to good pial collaterals. Retinal vessel multifractals: D0 (1.673±0.028vs1.652±0.025, p = 0.028), D1 (1.609±0.027vs1.590±0.025, p = 0.044) and f(α)max (1.674±0.027vs1.652±0.024, p = 0.019) were higher in patients with poor compared to good pial collaterals. Furthermore, support vector machine learning achieved a fair sensitivity (0.743) and specificity (0.707) for differentiating patients with poor from good pial collaterals. Age (p = 0.702), BMI (p = 0.422), total cholesterol (p = 0.842), triglycerides (p = 0.673), LDL (p = 0.952), HDL (p = 0.366), systolic blood pressure (p = 0.727), HbA1c (p = 0.261) and standard retinal metrics including CRAE (p = 0.084), CRVE (p = 0.946), AVR (p = 0.148), tortuosity index (p = 0.790), monofractal Df (p = 0.576), lacunarity (p = 0.531), curve asymmetry (p = 0.679) and singularity length (p = 0.937) did not differ between patients with poor compared to good pial collaterals.
This is the first translational study to show increased retinal vessel multifractal dimensions in patients with acute ischemic stroke and poor pial collaterals. A retinal vessel classifier was developed to differentiate between patients with poor and good pial collaterals and may allow rapid non-invasive identification of patients with poor pial collaterals.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35511879</pmid><doi>10.1371/journal.pone.0267837</doi><tpages>e0267837</tpages><orcidid>https://orcid.org/0000-0003-4647-6672</orcidid><orcidid>https://orcid.org/0000-0002-5197-8215</orcidid><orcidid>https://orcid.org/0000-0002-1126-7638</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2022-05, Vol.17 (5), p.e0267837 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2686209926 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Algorithms Angiography Biology and Life Sciences Blood flow Blood pressure Blood vessels Brain Ischemia - diagnostic imaging Care and treatment Cerebral Angiography - methods Cerebral blood flow Cholesterol Collateral Circulation - physiology Computed tomography Computer and Information Sciences CT imaging Evaluation Fractal geometry Fractals High density lipoprotein Humans Infarction, Middle Cerebral Artery Ischemia Ischemic Stroke Low density lipoprotein Machine learning Medicine and Health Sciences Occlusion Physical Sciences Research and Analysis Methods Retina Retinal Vessels - diagnostic imaging Retrospective Studies Stroke Stroke (Disease) Stroke - diagnostic imaging Support vector machines Tomography Tortuosity Triglycerides Veins & arteries |
| title | Retinal vessel multifractals predict pial collateral status in patients with acute ischemic stroke |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T09%3A00%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Retinal%20vessel%20multifractals%20predict%20pial%20collateral%20status%20in%20patients%20with%20acute%20ischemic%20stroke&rft.jtitle=PloS%20one&rft.au=Khan,%20Adnan&rft.date=2022-05-05&rft.volume=17&rft.issue=5&rft.spage=e0267837&rft.pages=e0267837-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0267837&rft_dat=%3Cgale_plos_%3EA702605829%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2686209926&rft_id=info:pmid/35511879&rft_galeid=A702605829&rft_doaj_id=oai_doaj_org_article_12d7016e314349ce95e386677eb35cfc&rfr_iscdi=true |