Intracranial arterial flow velocity mapping in quantitative time-of-flight MR angiography using deep machine learning

To evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow velocity within the intracranial arteries. Intracranial qTOF MRA was acquired in 15 subjects at 3 T. Blood flow velocity quantitation with qT...

Full description

Saved in:
Bibliographic Details
Published inMagnetic resonance imaging Vol. 100; pp. 10 - 17
Main Authors Koktzoglou, Ioannis, Huang, Rong
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier Inc 01.07.2023
Subjects
Online AccessGet full text
ISSN0730-725X
1873-5894
1873-5894
DOI10.1016/j.mri.2023.02.005

Cover

Abstract To evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow velocity within the intracranial arteries. Intracranial qTOF MRA was acquired in 15 subjects at 3 T. Blood flow velocity quantitation with qTOF MRA was done using a non-DML computer-vision procedure, and using convolutional DML neural networks. 3D phase contrast (PC) MRA was used as the comparator. Using PC velocity measures as the output target and qTOF two-echo source image data as inputs, DML neural networks were trained to predict component blood flow velocities. Total velocities and peak intracranial arterial blood flow velocities were computed from component velocities. Compared to non-DML image analysis, DML-based analysis of qTOF MRA image data improved agreement with PC for mean component velocity (intraclass correlation coefficient (ICC) = 0.966 versus 0.939), mean total velocity (ICC = 0.835 versus 0.723), and peak velocity (ICC = 0.816 versus 0.597), as well as narrowed the 95% Bland-Altman limits of agreement for mean component velocity ([−5.16, +4.31]cm/s versus [−6.86, +6.53]cm/s), mean total velocity ([−6.78,+3.59]cm/s versus [−9.39, +7.09]cm/s) and peak velocity ([−13.5,+10.2]cm/s versus [−21.3, +10.2]cm/s). Compared to non-DML analysis, DML image analysis reduced the root-mean-square deviation from PC velocity measures by 28%–36%, and shortened calculation times by 35-fold. The application of DML image analysis to intracranial qTOF MRA for velocity quantitation markedly shortened calculation times, substantially improved the agreement of component, total, and peak arterial blood flow velocities, and provided excellent agreement of hemodynamic measures with respect to 3D PC. •Deep machine learning (DML) can improve intracranial arterial velocimetry with qTOF MRA•Component, total, and peak flow velocity measures were improved with DML•DML-derived flow velocity measures showed excellent agreement with 3D phase contrast•DML reduced calculation times for intracranial arterial flow velocity mapping by 35-fold
AbstractList To evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow velocity within the intracranial arteries. Intracranial qTOF MRA was acquired in 15 subjects at 3 T. Blood flow velocity quantitation with qTOF MRA was done using a non-DML computer-vision procedure, and using convolutional DML neural networks. 3D phase contrast (PC) MRA was used as the comparator. Using PC velocity measures as the output target and qTOF two-echo source image data as inputs, DML neural networks were trained to predict component blood flow velocities. Total velocities and peak intracranial arterial blood flow velocities were computed from component velocities. Compared to non-DML image analysis, DML-based analysis of qTOF MRA image data improved agreement with PC for mean component velocity (intraclass correlation coefficient (ICC) = 0.966 versus 0.939), mean total velocity (ICC = 0.835 versus 0.723), and peak velocity (ICC = 0.816 versus 0.597), as well as narrowed the 95% Bland-Altman limits of agreement for mean component velocity ([−5.16, +4.31]cm/s versus [−6.86, +6.53]cm/s), mean total velocity ([−6.78,+3.59]cm/s versus [−9.39, +7.09]cm/s) and peak velocity ([−13.5,+10.2]cm/s versus [−21.3, +10.2]cm/s). Compared to non-DML analysis, DML image analysis reduced the root-mean-square deviation from PC velocity measures by 28%–36%, and shortened calculation times by 35-fold. The application of DML image analysis to intracranial qTOF MRA for velocity quantitation markedly shortened calculation times, substantially improved the agreement of component, total, and peak arterial blood flow velocities, and provided excellent agreement of hemodynamic measures with respect to 3D PC. •Deep machine learning (DML) can improve intracranial arterial velocimetry with qTOF MRA•Component, total, and peak flow velocity measures were improved with DML•DML-derived flow velocity measures showed excellent agreement with 3D phase contrast•DML reduced calculation times for intracranial arterial flow velocity mapping by 35-fold
To evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow velocity within the intracranial arteries.PURPOSETo evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow velocity within the intracranial arteries.Intracranial qTOF MRA was acquired in 15 subjects at 3 T. Blood flow velocity quantitation with qTOF MRA was done using a non-DML computer-vision procedure, and using convolutional DML neural networks. 3D phase contrast (PC) MRA was used as the comparator. Using PC velocity measures as the output target and qTOF two-echo source image data as inputs, DML neural networks were trained to predict component blood flow velocities. Total velocities and peak intracranial arterial blood flow velocities were computed from component velocities.MATERIALS AND METHODSIntracranial qTOF MRA was acquired in 15 subjects at 3 T. Blood flow velocity quantitation with qTOF MRA was done using a non-DML computer-vision procedure, and using convolutional DML neural networks. 3D phase contrast (PC) MRA was used as the comparator. Using PC velocity measures as the output target and qTOF two-echo source image data as inputs, DML neural networks were trained to predict component blood flow velocities. Total velocities and peak intracranial arterial blood flow velocities were computed from component velocities.Compared to non-DML image analysis, DML-based analysis of qTOF MRA image data improved agreement with PC for mean component velocity (intraclass correlation coefficient (ICC) = 0.966 versus 0.939), mean total velocity (ICC = 0.835 versus 0.723), and peak velocity (ICC = 0.816 versus 0.597), as well as narrowed the 95% Bland-Altman limits of agreement for mean component velocity ([-5.16, +4.31]cm/s versus [-6.86, +6.53]cm/s), mean total velocity ([-6.78,+3.59]cm/s versus [-9.39, +7.09]cm/s) and peak velocity ([-13.5,+10.2]cm/s versus [-21.3, +10.2]cm/s). Compared to non-DML analysis, DML image analysis reduced the root-mean-square deviation from PC velocity measures by 28%-36%, and shortened calculation times by 35-fold.RESULTSCompared to non-DML image analysis, DML-based analysis of qTOF MRA image data improved agreement with PC for mean component velocity (intraclass correlation coefficient (ICC) = 0.966 versus 0.939), mean total velocity (ICC = 0.835 versus 0.723), and peak velocity (ICC = 0.816 versus 0.597), as well as narrowed the 95% Bland-Altman limits of agreement for mean component velocity ([-5.16, +4.31]cm/s versus [-6.86, +6.53]cm/s), mean total velocity ([-6.78,+3.59]cm/s versus [-9.39, +7.09]cm/s) and peak velocity ([-13.5,+10.2]cm/s versus [-21.3, +10.2]cm/s). Compared to non-DML analysis, DML image analysis reduced the root-mean-square deviation from PC velocity measures by 28%-36%, and shortened calculation times by 35-fold.The application of DML image analysis to intracranial qTOF MRA for velocity quantitation markedly shortened calculation times, substantially improved the agreement of component, total, and peak arterial blood flow velocities, and provided excellent agreement of hemodynamic measures with respect to 3D PC.CONCLUSIONThe application of DML image analysis to intracranial qTOF MRA for velocity quantitation markedly shortened calculation times, substantially improved the agreement of component, total, and peak arterial blood flow velocities, and provided excellent agreement of hemodynamic measures with respect to 3D PC.
To evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow velocity within the intracranial arteries. Intracranial qTOF MRA was acquired in 15 subjects at 3 T. Blood flow velocity quantitation with qTOF MRA was done using a non-DML computer-vision procedure, and using convolutional DML neural networks. 3D phase contrast (PC) MRA was used as the comparator. Using PC velocity measures as the output target and qTOF two-echo source image data as inputs, DML neural networks were trained to predict component blood flow velocities. Total velocities and peak intracranial arterial blood flow velocities were computed from component velocities. Compared to non-DML image analysis, DML-based analysis of qTOF MRA image data improved agreement with PC for mean component velocity (intraclass correlation coefficient (ICC) = 0.966 versus 0.939), mean total velocity (ICC = 0.835 versus 0.723), and peak velocity (ICC = 0.816 versus 0.597), as well as narrowed the 95% Bland-Altman limits of agreement for mean component velocity ([-5.16, +4.31]cm/s versus [-6.86, +6.53]cm/s), mean total velocity ([-6.78,+3.59]cm/s versus [-9.39, +7.09]cm/s) and peak velocity ([-13.5,+10.2]cm/s versus [-21.3, +10.2]cm/s). Compared to non-DML analysis, DML image analysis reduced the root-mean-square deviation from PC velocity measures by 28%-36%, and shortened calculation times by 35-fold. The application of DML image analysis to intracranial qTOF MRA for velocity quantitation markedly shortened calculation times, substantially improved the agreement of component, total, and peak arterial blood flow velocities, and provided excellent agreement of hemodynamic measures with respect to 3D PC.
Author Huang, Rong
Koktzoglou, Ioannis
AuthorAffiliation 1 Department of Radiology, NorthShore University HealthSystem, Evanston, IL, USA
2 University of Chicago Pritzker School of Medicine, Chicago, IL, USA
AuthorAffiliation_xml – name: 1 Department of Radiology, NorthShore University HealthSystem, Evanston, IL, USA
– name: 2 University of Chicago Pritzker School of Medicine, Chicago, IL, USA
Author_xml – sequence: 1
  givenname: Ioannis
  surname: Koktzoglou
  fullname: Koktzoglou, Ioannis
  email: ikoktzoglou@gmail.com
  organization: Department of Radiology, NorthShore University HealthSystem, Evanston, IL, USA
– sequence: 2
  givenname: Rong
  surname: Huang
  fullname: Huang, Rong
  organization: Department of Radiology, NorthShore University HealthSystem, Evanston, IL, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36822451$$D View this record in MEDLINE/PubMed
BookMark eNqFkU9v1DAQxS1URLeFD8AF5cgli__E60QcEKqgrVSEhEDiZnmdye4sjp06zqL99jhsqaCHchrLfr8343ln5MQHD4S8ZHTJKFu92S37iEtOuVhSvqRUPiELVitRyrqpTsiCKkFLxeX3U3I2jjuaFVzIZ-RUrGrOK8kWZLr2KRobjUfjChMTxPnQufCz2IMLFtOh6M0woN8U6IvbyfiEySTcQ5GwhzJ0Zedws03Fpy-F8RsMm2iG7aGYxplpAYZsYLfooXBgos-3z8nTzrgRXtzVc_Lt44evF1flzefL64v3N6WVlKfSMN7IyoJsJe2q1VxUXVVrUdVtVZtOdh3thGpaWldWiDVthBJsxeRaNqqpGnFO3h19h2ndQ2th_qzTQ8TexIMOBvW_Lx63ehP2mtFsqRjNDq_vHGK4nWBMusfRgnPGQ5hGzVWdpZSrWfrq72b3Xf4sOwvYUWBjGMcI3b2EUT0Hqnc6B6rnQDXlOseVGfWAsb-3H-Z50T1Kvj2SkBe8R4h6tAjeQosRbNJtwEfp5gFtHXq0xv2Aw3_YX7tTz6I
CitedBy_id crossref_primary_10_1002_jmri_29395
crossref_primary_10_1002_jmri_29398
crossref_primary_10_1002_jmri_29701
Cites_doi 10.1002/mrm.1910090117
10.1016/0090-3019(94)90394-8
10.1111/j.1552-6569.2012.00711.x
10.1148/radiol.2018171820
10.1097/00004728-198609000-00001
10.1159/000506924
10.1007/s002340050645
10.1148/radiology.193.1.8090890
10.1148/radiology.154.2.3966130
10.1179/016164106X130380
10.1002/mrm.1910170215
10.1148/radiology.183.2.1561338
10.1161/STROKEAHA.113.003133
10.1002/mrm.28969
10.1126/science.aaa8415
10.2466/pr0.1966.19.1.3
10.1186/s40537-019-0192-5
10.2463/mrms.mp.2016-0060
10.1097/WCO.0000000000000341
10.1016/0730-725X(82)90170-9
10.1037/1040-3590.6.4.284
10.1007/s12194-017-0406-5
10.1002/mrm.29060
10.1002/mrm.21763
10.2214/ajr.155.1.2112840
10.3233/JAD-190977
10.1002/jmri.26288
10.1152/physrev.1959.39.2.183
10.1002/jmri.20828
ContentType Journal Article
Copyright 2023 Elsevier Inc.
Copyright © 2023 Elsevier Inc. All rights reserved.
Copyright_xml – notice: 2023 Elsevier Inc.
– notice: Copyright © 2023 Elsevier Inc. All rights reserved.
DBID AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
5PM
DOI 10.1016/j.mri.2023.02.005
DatabaseName CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList

MEDLINE - Academic
MEDLINE
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 1873-5894
EndPage 17
ExternalDocumentID PMC10084710
36822451
10_1016_j_mri_2023_02_005
S0730725X23000395
Genre Journal Article
Research Support, N.I.H., Extramural
GrantInformation_xml – fundername: NIBIB NIH HHS
  grantid: R01 EB027475
GroupedDBID ---
--K
--M
.1-
.FO
.GJ
.~1
0R~
1B1
1P~
1RT
1~.
1~5
29M
3O-
4.4
457
4CK
4G.
53G
5GY
5RE
5VS
7-5
71M
8P~
9JM
9JN
AABNK
AAEDT
AAEDW
AAIKJ
AAKOC
AALRI
AAOAW
AAQFI
AAQXK
AATTM
AAXKI
AAXUO
AAYWO
ABBQC
ABDPE
ABFNM
ABGSF
ABJNI
ABMAC
ABMZM
ABNEU
ABOCM
ABUDA
ABWVN
ABXDB
ACDAQ
ACFVG
ACGFS
ACIEU
ACIUM
ACNNM
ACRLP
ACRPL
ACVFH
ADBBV
ADCNI
ADEZE
ADMUD
ADNMO
ADUVX
AEBSH
AEHWI
AEIPS
AEKER
AENEX
AEUPX
AEVXI
AFFNX
AFJKZ
AFPUW
AFRHN
AFTJW
AFXIZ
AGCQF
AGHFR
AGQPQ
AGRDE
AGUBO
AGYEJ
AHHHB
AIEXJ
AIGII
AIIUN
AIKHN
AITUG
AIVDX
AJRQY
AJUYK
AKBMS
AKRWK
AKYEP
ALMA_UNASSIGNED_HOLDINGS
AMRAJ
ANKPU
ANZVX
APXCP
ASPBG
AVWKF
AXJTR
AZFZN
BKOJK
BLXMC
BNPGV
CS3
EBS
EFJIC
EFKBS
EJD
EO8
EO9
EP2
EP3
F5P
FDB
FEDTE
FGOYB
FIRID
FNPLU
FYGXN
G-2
G-Q
GBLVA
HEI
HMK
HMO
HVGLF
HZ~
IHE
J1W
KOM
M29
M41
MO0
N9A
O-L
O9-
OAUVE
OGIMB
OI~
OU0
OZT
P-8
P-9
P2P
PC.
Q38
R2-
ROL
RPZ
SAE
SCC
SDF
SDG
SDP
SEL
SES
SEW
SPC
SPCBC
SSH
SSQ
SSU
SSZ
T5K
WUQ
XPP
Z5R
ZGI
ZMT
~G-
~S-
AACTN
AAIAV
ABLVK
ABYKQ
AFCTW
AFKWA
AJBFU
AJOXV
AMFUW
DOVZS
EFLBG
G8K
LCYCR
RIG
AAYXX
AGRNS
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
ACLOT
~HD
5PM
ID FETCH-LOGICAL-c502t-a12954ce5d50f465d507844b348d48af5ff0f379d084c33b093731615b5979493
IEDL.DBID AIKHN
ISSN 0730-725X
1873-5894
IngestDate Thu Aug 21 18:32:40 EDT 2025
Sat Sep 27 18:19:01 EDT 2025
Mon Jul 21 05:14:23 EDT 2025
Thu Apr 24 23:11:09 EDT 2025
Tue Jul 01 01:55:28 EDT 2025
Fri Feb 23 02:40:32 EST 2024
Tue Aug 26 16:33:45 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Deep learning
TOF
Quantitative
MRI
MRA
Language English
License Copyright © 2023 Elsevier Inc. All rights reserved.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c502t-a12954ce5d50f465d507844b348d48af5ff0f379d084c33b093731615b5979493
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Ioannis Koktzoglou: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Writing – Original Draft, Writing – Review and Editing, Funding Acquisition. Rong Huang: Conceptualization, Methodology, Software, Formal Analysis, Writing – Review and Editing.
OpenAccessLink https://www.ncbi.nlm.nih.gov/pmc/articles/10084710
PMID 36822451
PQID 2780080270
PQPubID 23479
PageCount 8
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_10084710
proquest_miscellaneous_2780080270
pubmed_primary_36822451
crossref_primary_10_1016_j_mri_2023_02_005
crossref_citationtrail_10_1016_j_mri_2023_02_005
elsevier_sciencedirect_doi_10_1016_j_mri_2023_02_005
elsevier_clinicalkey_doi_10_1016_j_mri_2023_02_005
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2023-07-01
PublicationDateYYYYMMDD 2023-07-01
PublicationDate_xml – month: 07
  year: 2023
  text: 2023-07-01
  day: 01
PublicationDecade 2020
PublicationPlace Netherlands
PublicationPlace_xml – name: Netherlands
PublicationTitle Magnetic resonance imaging
PublicationTitleAlternate Magn Reson Imaging
PublicationYear 2023
Publisher Elsevier Inc
Publisher_xml – name: Elsevier Inc
References Sailer, Wagemans, Nelemans, de Graaf, van Zwam (bb0025) 2014; 45
Edelman, Koktzoglou (bb0030) 2019; 49
Seber, Lee (bb0175) 2003
Dumoulin, Souza, Walker, Wagle (bb0070) 1989; 9
Korogi, Takahashi, Mabuchi, Miki, Shiga, Watabe (bb0020) 1994; 193
Suzuki (bb0185) 2017; 10
Yamashita, Isoda, Hirano, Takeda, Inagawa, Takehara (bb0095) 2007; 25
Cicchetti (bb0180) 1994; 6
Araki, Kohmura, Tsukaguchi (bb0105) 1994; 15
Enzmann, Ross, Marks, Pelc (bb0085) 1994; 15
Koktzoglou, Huang, Edelman (bb0125) 2022; 87
Ikawa, Sumida, Uozumi, Kuwabara, Kiya, Kurisu (bb0110) 1994; 42
Doepp, Valdueza, Schreiber (bb0035) 2006; 28
Altman, Bland (bb0170) 1983; 32
Marshall, Pavol, Cheung, Asllani, Lazar (bb0045) 2020; 10
Ross, Pelc, Enzmann (bb0080) 1993; 14
Choy, Khalilzadeh, Michalski, Do, Samir, Pianykh (bb0140) 2018; 288
Lassen (bb0005) 1959; 39
Johnson, Lum, Turski, Block, Mistretta, Wieben (bb0100) 2008; 60
Moran (bb0055) 1982; 1
Schnell, Wu, Ansari (bb0120) 2016; 29
Gu, Korosec, Block, Fain, Turk, Lum (bb0090) 2005; 26
Tegeler, Crutchfield, Katsnelson, Kim, Tang, Passmore Griffin (bb0040) 2013; 23
Gulli, Pal (bb0155) 2017
Klein (bb0145) 2021
Bartko (bb0165) 1966; 19
Fukuyama, Isoda, Morita, Mori, Watanabe, Ishiguro (bb0190) 2017; 16
Thomas, Tarumi, Sheng, Tseng, Womack, Cullum (bb0050) 2020; 75
Kim, Eisenmenger, Turski, Johnson (bb0130) 2022; 87
Johnson, Khoshgoftaar (bb0160) 2019; 6
Pernicone, Siebert, Potchen, Pera, Dumoulin, Souza (bb0075) 1990; 155
Nayler, Firmin, Longmore (bb0065) 1986; 10
Oelerich, Lentschig, Zunker, Reimer, Rummeny, Schuierer (bb0115) 1998; 40
Jordan, Mitchell (bb0135) 2015; 349
Moran, Moran, Karstaedt (bb0060) 1985; 154
Parker, Yuan, Blatter (bb0010) 1991; 17
Blatter, Parker, Ahn, Bahr, Robison, Schwartz (bb0015) 1992; 183
Ronneberger, Fischer, Brox (bb0150) 2015
Oelerich (10.1016/j.mri.2023.02.005_bb0115) 1998; 40
Johnson (10.1016/j.mri.2023.02.005_bb0100) 2008; 60
Dumoulin (10.1016/j.mri.2023.02.005_bb0070) 1989; 9
Suzuki (10.1016/j.mri.2023.02.005_bb0185) 2017; 10
Edelman (10.1016/j.mri.2023.02.005_bb0030) 2019; 49
Ikawa (10.1016/j.mri.2023.02.005_bb0110) 1994; 42
Tegeler (10.1016/j.mri.2023.02.005_bb0040) 2013; 23
Gu (10.1016/j.mri.2023.02.005_bb0090) 2005; 26
Johnson (10.1016/j.mri.2023.02.005_bb0160) 2019; 6
Sailer (10.1016/j.mri.2023.02.005_bb0025) 2014; 45
Cicchetti (10.1016/j.mri.2023.02.005_bb0180) 1994; 6
Yamashita (10.1016/j.mri.2023.02.005_bb0095) 2007; 25
Fukuyama (10.1016/j.mri.2023.02.005_bb0190) 2017; 16
Lassen (10.1016/j.mri.2023.02.005_bb0005) 1959; 39
Araki (10.1016/j.mri.2023.02.005_bb0105) 1994; 15
Ronneberger (10.1016/j.mri.2023.02.005_bb0150) 2015
Thomas (10.1016/j.mri.2023.02.005_bb0050) 2020; 75
Ross (10.1016/j.mri.2023.02.005_bb0080) 1993; 14
Jordan (10.1016/j.mri.2023.02.005_bb0135) 2015; 349
Kim (10.1016/j.mri.2023.02.005_bb0130) 2022; 87
Doepp (10.1016/j.mri.2023.02.005_bb0035) 2006; 28
Schnell (10.1016/j.mri.2023.02.005_bb0120) 2016; 29
Bartko (10.1016/j.mri.2023.02.005_bb0165) 1966; 19
Moran (10.1016/j.mri.2023.02.005_bb0055) 1982; 1
Pernicone (10.1016/j.mri.2023.02.005_bb0075) 1990; 155
Klein (10.1016/j.mri.2023.02.005_bb0145) 2021
Choy (10.1016/j.mri.2023.02.005_bb0140) 2018; 288
Korogi (10.1016/j.mri.2023.02.005_bb0020) 1994; 193
Gulli (10.1016/j.mri.2023.02.005_bb0155) 2017
Enzmann (10.1016/j.mri.2023.02.005_bb0085) 1994; 15
Moran (10.1016/j.mri.2023.02.005_bb0060) 1985; 154
Nayler (10.1016/j.mri.2023.02.005_bb0065) 1986; 10
Parker (10.1016/j.mri.2023.02.005_bb0010) 1991; 17
Altman (10.1016/j.mri.2023.02.005_bb0170) 1983; 32
Blatter (10.1016/j.mri.2023.02.005_bb0015) 1992; 183
Marshall (10.1016/j.mri.2023.02.005_bb0045) 2020; 10
Koktzoglou (10.1016/j.mri.2023.02.005_bb0125) 2022; 87
Seber (10.1016/j.mri.2023.02.005_bb0175) 2003
References_xml – volume: 1
  start-page: 197
  year: 1982
  end-page: 203
  ident: bb0055
  article-title: A flow velocity zeugmatographic interlace for NMR imaging in humans
  publication-title: Magn Reson Imaging
– volume: 16
  start-page: 311
  year: 2017
  end-page: 316
  ident: bb0190
  article-title: Influence of spatial resolution in three-dimensional cine phase contrast magnetic resonance imaging on the accuracy of hemodynamic analysis
  publication-title: Magn Reson Med Sci
– volume: 193
  start-page: 187
  year: 1994
  end-page: 193
  ident: bb0020
  article-title: Intracranial vascular stenosis and occlusion: diagnostic accuracy of three-dimensional, Fourier transform, time-of-flight MR angiography
  publication-title: Radiology
– volume: 39
  start-page: 183
  year: 1959
  end-page: 238
  ident: bb0005
  article-title: Cerebral blood flow and oxygen consumption in man
  publication-title: Physiol Rev
– volume: 6
  start-page: 27
  year: 2019
  ident: bb0160
  article-title: Survey on deep learning with class imbalance
  publication-title: J Big Data
– year: 2015
  ident: bb0150
  article-title: U-Net: Convolutional networks for biomedical image segmentation. ArXiv:150504597 [Cs]
– volume: 9
  start-page: 139
  year: 1989
  end-page: 149
  ident: bb0070
  article-title: Three-dimensional phase contrast angiography
  publication-title: Magn Reson Med
– volume: 32
  start-page: 307
  year: 1983
  end-page: 317
  ident: bb0170
  article-title: Measurement in medicine: the analysis of method comparison studies
  publication-title: J Royal Stat Soc Series D (The Statistician)
– volume: 28
  start-page: 645
  year: 2006
  end-page: 649
  ident: bb0035
  article-title: Transcranial and extracranial ultrasound assessment of cerebral hemodynamics in vascular and Alzheimer’s dementia
  publication-title: Neurol Res
– volume: 19
  start-page: 3
  year: 1966
  end-page: 11
  ident: bb0165
  article-title: The Intraclass correlation coefficient as a measure of reliability
  publication-title: Psychol Rep
– volume: 6
  start-page: 284
  year: 1994
  end-page: 290
  ident: bb0180
  article-title: Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology
  publication-title: Psychol Assess
– year: 2021
  ident: bb0145
  article-title: PyElastix - Python wrapper for the Elastix nonrigid registration toolkit
– volume: 288
  start-page: 318
  year: 2018
  end-page: 328
  ident: bb0140
  article-title: Current applications and future impact of machine learning in radiology
  publication-title: Radiology
– year: 2017
  ident: bb0155
  article-title: Deep learning with Keras
– year: 2003
  ident: bb0175
  article-title: Linear regression analysis
– volume: 45
  start-page: 119
  year: 2014
  end-page: 126
  ident: bb0025
  article-title: Diagnosing intracranial aneurysms with MR angiography: systematic review and meta-analysis
  publication-title: Stroke
– volume: 26
  start-page: 743
  year: 2005
  end-page: 749
  ident: bb0090
  article-title: PC VIPR: a high-speed 3D phase-contrast method for flow quantification and high-resolution angiography
  publication-title: AJNR Am J Neuroradiol
– volume: 60
  start-page: 1329
  year: 2008
  end-page: 1336
  ident: bb0100
  article-title: Improved 3D phase contrast MRI with off-resonance corrected dual echo VIPR
  publication-title: Magn Reson Med
– volume: 17
  start-page: 434
  year: 1991
  end-page: 451
  ident: bb0010
  article-title: MR angiography by multiple thin slab 3D acquisition
  publication-title: Magn Reson Med
– volume: 10
  start-page: 257
  year: 2017
  end-page: 273
  ident: bb0185
  article-title: Overview of deep learning in medical imaging
  publication-title: Radiol Phys Technol
– volume: 155
  start-page: 167
  year: 1990
  end-page: 176
  ident: bb0075
  article-title: Three-dimensional phase-contrast MR angiography in the head and neck: preliminary report
  publication-title: AJR Am J Roentgenol
– volume: 349
  start-page: 255
  year: 2015
  end-page: 260
  ident: bb0135
  article-title: Machine learning: trends, perspectives, and prospects
  publication-title: Science
– volume: 42
  start-page: 287
  year: 1994
  end-page: 292
  ident: bb0110
  article-title: Comparison of three-dimensional phase-contrast magnetic resonance angiography with three-dimensional time-of-flight magnetic resonance angiography in cerebral aneurysms
  publication-title: Surg Neurol
– volume: 10
  start-page: 21
  year: 2020
  end-page: 27
  ident: bb0045
  article-title: Cognitive impairment correlates linearly with mean flow velocity by transcranial Doppler below a definable threshold
  publication-title: Cerebrovasc Dis Extra
– volume: 14
  start-page: 19
  year: 1993
  end-page: 25
  ident: bb0080
  article-title: Qualitative phase contrast MRA in the normal and abnormal circle of Willis
  publication-title: AJNR Am J Neuroradiol
– volume: 40
  start-page: 567
  year: 1998
  end-page: 573
  ident: bb0115
  article-title: Intracranial vascular stenosis and occlusion: comparison of 3D time-of-flight and 3D phase-contrast MR angiography
  publication-title: Neuroradiology
– volume: 183
  start-page: 379
  year: 1992
  end-page: 389
  ident: bb0015
  article-title: Cerebral MR angiography multiple overlapping thin slab acquisition. Part II Early clinical experience
  publication-title: Radiology
– volume: 49
  start-page: 355
  year: 2019
  end-page: 373
  ident: bb0030
  article-title: Noncontrast MR angiography: an update
  publication-title: J Magn Reson Imaging
– volume: 15
  start-page: 1618
  year: 1994
  end-page: 1623
  ident: bb0105
  article-title: A pitfall in detection of intracranial unruptured aneurysms on three-dimensional phase-contrast MR angiography
  publication-title: AJNR Am J Neuroradiol
– volume: 29
  start-page: 419
  year: 2016
  end-page: 428
  ident: bb0120
  article-title: Four-dimensional MRI flow examinations in cerebral and extracerebral vessels – ready for clinical routine?
  publication-title: Curr Opin Neurol
– volume: 10
  start-page: 715
  year: 1986
  end-page: 722
  ident: bb0065
  article-title: Blood flow imaging by cine magnetic resonance
  publication-title: J Comput Assist Tomogr
– volume: 25
  start-page: 473
  year: 2007
  end-page: 478
  ident: bb0095
  article-title: Visualization of hemodynamics in intracranial arteries using time-resolved three-dimensional phase-contrast MRI
  publication-title: J Magn Reson Imaging
– volume: 87
  start-page: 1401
  year: 2022
  end-page: 1417
  ident: bb0130
  article-title: Simultaneous 3D-TOF angiography and 4D-flow MRI with enhanced flow signal using multiple overlapping thin slab acquisition and magnetization transfer
  publication-title: Magn Reson Med
– volume: 15
  start-page: 123
  year: 1994
  end-page: 129
  ident: bb0085
  article-title: Blood flow in major cerebral arteries measured by phase-contrast cine MR
  publication-title: AJNR Am J Neuroradiol
– volume: 154
  start-page: 433
  year: 1985
  end-page: 441
  ident: bb0060
  article-title: Verification and evaluation of internal flow and motion. True magnetic resonance imaging by the phase gradient modulation method
  publication-title: Radiology
– volume: 87
  start-page: 150
  year: 2022
  end-page: 162
  ident: bb0125
  article-title: Quantitative time-of-flight MR angiography for simultaneous luminal and hemodynamic evaluation of the intracranial arteries
  publication-title: Magn Reson Med
– volume: 23
  start-page: 466
  year: 2013
  end-page: 472
  ident: bb0040
  article-title: Transcranial Doppler velocities in a large, healthy population
  publication-title: J Neuroimaging
– volume: 75
  start-page: 617
  year: 2020
  end-page: 631
  ident: bb0050
  article-title: Brain perfusion change in patients with mild cognitive impairment after 12 months of aerobic exercise training
  publication-title: J Alzheimers Dis
– volume: 14
  start-page: 19
  year: 1993
  ident: 10.1016/j.mri.2023.02.005_bb0080
  article-title: Qualitative phase contrast MRA in the normal and abnormal circle of Willis
  publication-title: AJNR Am J Neuroradiol
– volume: 9
  start-page: 139
  year: 1989
  ident: 10.1016/j.mri.2023.02.005_bb0070
  article-title: Three-dimensional phase contrast angiography
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.1910090117
– volume: 42
  start-page: 287
  year: 1994
  ident: 10.1016/j.mri.2023.02.005_bb0110
  article-title: Comparison of three-dimensional phase-contrast magnetic resonance angiography with three-dimensional time-of-flight magnetic resonance angiography in cerebral aneurysms
  publication-title: Surg Neurol
  doi: 10.1016/0090-3019(94)90394-8
– volume: 23
  start-page: 466
  year: 2013
  ident: 10.1016/j.mri.2023.02.005_bb0040
  article-title: Transcranial Doppler velocities in a large, healthy population
  publication-title: J Neuroimaging
  doi: 10.1111/j.1552-6569.2012.00711.x
– volume: 288
  start-page: 318
  year: 2018
  ident: 10.1016/j.mri.2023.02.005_bb0140
  article-title: Current applications and future impact of machine learning in radiology
  publication-title: Radiology
  doi: 10.1148/radiol.2018171820
– volume: 10
  start-page: 715
  year: 1986
  ident: 10.1016/j.mri.2023.02.005_bb0065
  article-title: Blood flow imaging by cine magnetic resonance
  publication-title: J Comput Assist Tomogr
  doi: 10.1097/00004728-198609000-00001
– volume: 10
  start-page: 21
  year: 2020
  ident: 10.1016/j.mri.2023.02.005_bb0045
  article-title: Cognitive impairment correlates linearly with mean flow velocity by transcranial Doppler below a definable threshold
  publication-title: Cerebrovasc Dis Extra
  doi: 10.1159/000506924
– volume: 40
  start-page: 567
  year: 1998
  ident: 10.1016/j.mri.2023.02.005_bb0115
  article-title: Intracranial vascular stenosis and occlusion: comparison of 3D time-of-flight and 3D phase-contrast MR angiography
  publication-title: Neuroradiology
  doi: 10.1007/s002340050645
– volume: 193
  start-page: 187
  year: 1994
  ident: 10.1016/j.mri.2023.02.005_bb0020
  article-title: Intracranial vascular stenosis and occlusion: diagnostic accuracy of three-dimensional, Fourier transform, time-of-flight MR angiography
  publication-title: Radiology
  doi: 10.1148/radiology.193.1.8090890
– volume: 32
  start-page: 307
  year: 1983
  ident: 10.1016/j.mri.2023.02.005_bb0170
  article-title: Measurement in medicine: the analysis of method comparison studies
  publication-title: J Royal Stat Soc Series D (The Statistician)
– volume: 154
  start-page: 433
  year: 1985
  ident: 10.1016/j.mri.2023.02.005_bb0060
  article-title: Verification and evaluation of internal flow and motion. True magnetic resonance imaging by the phase gradient modulation method
  publication-title: Radiology
  doi: 10.1148/radiology.154.2.3966130
– volume: 28
  start-page: 645
  year: 2006
  ident: 10.1016/j.mri.2023.02.005_bb0035
  article-title: Transcranial and extracranial ultrasound assessment of cerebral hemodynamics in vascular and Alzheimer’s dementia
  publication-title: Neurol Res
  doi: 10.1179/016164106X130380
– volume: 17
  start-page: 434
  year: 1991
  ident: 10.1016/j.mri.2023.02.005_bb0010
  article-title: MR angiography by multiple thin slab 3D acquisition
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.1910170215
– volume: 183
  start-page: 379
  year: 1992
  ident: 10.1016/j.mri.2023.02.005_bb0015
  article-title: Cerebral MR angiography multiple overlapping thin slab acquisition. Part II Early clinical experience
  publication-title: Radiology
  doi: 10.1148/radiology.183.2.1561338
– volume: 45
  start-page: 119
  year: 2014
  ident: 10.1016/j.mri.2023.02.005_bb0025
  article-title: Diagnosing intracranial aneurysms with MR angiography: systematic review and meta-analysis
  publication-title: Stroke
  doi: 10.1161/STROKEAHA.113.003133
– volume: 87
  start-page: 150
  year: 2022
  ident: 10.1016/j.mri.2023.02.005_bb0125
  article-title: Quantitative time-of-flight MR angiography for simultaneous luminal and hemodynamic evaluation of the intracranial arteries
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.28969
– volume: 349
  start-page: 255
  year: 2015
  ident: 10.1016/j.mri.2023.02.005_bb0135
  article-title: Machine learning: trends, perspectives, and prospects
  publication-title: Science
  doi: 10.1126/science.aaa8415
– volume: 19
  start-page: 3
  year: 1966
  ident: 10.1016/j.mri.2023.02.005_bb0165
  article-title: The Intraclass correlation coefficient as a measure of reliability
  publication-title: Psychol Rep
  doi: 10.2466/pr0.1966.19.1.3
– volume: 6
  start-page: 27
  year: 2019
  ident: 10.1016/j.mri.2023.02.005_bb0160
  article-title: Survey on deep learning with class imbalance
  publication-title: J Big Data
  doi: 10.1186/s40537-019-0192-5
– volume: 26
  start-page: 743
  year: 2005
  ident: 10.1016/j.mri.2023.02.005_bb0090
  article-title: PC VIPR: a high-speed 3D phase-contrast method for flow quantification and high-resolution angiography
  publication-title: AJNR Am J Neuroradiol
– year: 2003
  ident: 10.1016/j.mri.2023.02.005_bb0175
– volume: 16
  start-page: 311
  year: 2017
  ident: 10.1016/j.mri.2023.02.005_bb0190
  article-title: Influence of spatial resolution in three-dimensional cine phase contrast magnetic resonance imaging on the accuracy of hemodynamic analysis
  publication-title: Magn Reson Med Sci
  doi: 10.2463/mrms.mp.2016-0060
– volume: 29
  start-page: 419
  year: 2016
  ident: 10.1016/j.mri.2023.02.005_bb0120
  article-title: Four-dimensional MRI flow examinations in cerebral and extracerebral vessels – ready for clinical routine?
  publication-title: Curr Opin Neurol
  doi: 10.1097/WCO.0000000000000341
– year: 2017
  ident: 10.1016/j.mri.2023.02.005_bb0155
– volume: 1
  start-page: 197
  year: 1982
  ident: 10.1016/j.mri.2023.02.005_bb0055
  article-title: A flow velocity zeugmatographic interlace for NMR imaging in humans
  publication-title: Magn Reson Imaging
  doi: 10.1016/0730-725X(82)90170-9
– volume: 6
  start-page: 284
  year: 1994
  ident: 10.1016/j.mri.2023.02.005_bb0180
  article-title: Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology
  publication-title: Psychol Assess
  doi: 10.1037/1040-3590.6.4.284
– volume: 10
  start-page: 257
  year: 2017
  ident: 10.1016/j.mri.2023.02.005_bb0185
  article-title: Overview of deep learning in medical imaging
  publication-title: Radiol Phys Technol
  doi: 10.1007/s12194-017-0406-5
– volume: 87
  start-page: 1401
  year: 2022
  ident: 10.1016/j.mri.2023.02.005_bb0130
  article-title: Simultaneous 3D-TOF angiography and 4D-flow MRI with enhanced flow signal using multiple overlapping thin slab acquisition and magnetization transfer
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.29060
– volume: 60
  start-page: 1329
  year: 2008
  ident: 10.1016/j.mri.2023.02.005_bb0100
  article-title: Improved 3D phase contrast MRI with off-resonance corrected dual echo VIPR
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.21763
– volume: 155
  start-page: 167
  year: 1990
  ident: 10.1016/j.mri.2023.02.005_bb0075
  article-title: Three-dimensional phase-contrast MR angiography in the head and neck: preliminary report
  publication-title: AJR Am J Roentgenol
  doi: 10.2214/ajr.155.1.2112840
– year: 2015
  ident: 10.1016/j.mri.2023.02.005_bb0150
– volume: 15
  start-page: 1618
  year: 1994
  ident: 10.1016/j.mri.2023.02.005_bb0105
  article-title: A pitfall in detection of intracranial unruptured aneurysms on three-dimensional phase-contrast MR angiography
  publication-title: AJNR Am J Neuroradiol
– volume: 75
  start-page: 617
  year: 2020
  ident: 10.1016/j.mri.2023.02.005_bb0050
  article-title: Brain perfusion change in patients with mild cognitive impairment after 12 months of aerobic exercise training
  publication-title: J Alzheimers Dis
  doi: 10.3233/JAD-190977
– volume: 49
  start-page: 355
  year: 2019
  ident: 10.1016/j.mri.2023.02.005_bb0030
  article-title: Noncontrast MR angiography: an update
  publication-title: J Magn Reson Imaging
  doi: 10.1002/jmri.26288
– volume: 15
  start-page: 123
  year: 1994
  ident: 10.1016/j.mri.2023.02.005_bb0085
  article-title: Blood flow in major cerebral arteries measured by phase-contrast cine MR
  publication-title: AJNR Am J Neuroradiol
– volume: 39
  start-page: 183
  year: 1959
  ident: 10.1016/j.mri.2023.02.005_bb0005
  article-title: Cerebral blood flow and oxygen consumption in man
  publication-title: Physiol Rev
  doi: 10.1152/physrev.1959.39.2.183
– year: 2021
  ident: 10.1016/j.mri.2023.02.005_bb0145
– volume: 25
  start-page: 473
  year: 2007
  ident: 10.1016/j.mri.2023.02.005_bb0095
  article-title: Visualization of hemodynamics in intracranial arteries using time-resolved three-dimensional phase-contrast MRI
  publication-title: J Magn Reson Imaging
  doi: 10.1002/jmri.20828
SSID ssj0005235
Score 2.406164
Snippet To evaluate the application of deep machine learning (DML) to 3D quantitative time-of-flight (qTOF) magnetic resonance angiography (MRA) to measure blood flow...
SourceID pubmedcentral
proquest
pubmed
crossref
elsevier
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 10
SubjectTerms Blood Flow Velocity
Cerebral Arteries - diagnostic imaging
Deep learning
Hemodynamics
Humans
Imaging, Three-Dimensional - methods
Machine Learning
Magnetic Resonance Angiography - methods
Magnetic Resonance Imaging - methods
MRA
MRI
Quantitative
TOF
Title Intracranial arterial flow velocity mapping in quantitative time-of-flight MR angiography using deep machine learning
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0730725X23000395
https://dx.doi.org/10.1016/j.mri.2023.02.005
https://www.ncbi.nlm.nih.gov/pubmed/36822451
https://www.proquest.com/docview/2780080270
https://pubmed.ncbi.nlm.nih.gov/PMC10084710
Volume 100
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1La9tAEB7ygNJL6btO2rCFngobr_ZhSccQGpwW59A2kNsiaXddFUd2E5vc8tszI61M3NIUerJs7ZhFM5r5lvlmBuCDMIXyQQtuch24Tn3Cc-ckl74IRsnSJ4EyupOz0fhcf74wF1tw3NfCEK0y-v7Op7feOv4yjE9zuKjr4TcyzlTiYUu1FaZmG3YlRns8ge0enX4Zn91jenRzNnE9J4E-udnSvC6v6kMaId517jR_C09_ws_fWZT3wtLJU3gS8SQ76rb8DLZ88xweTWLG_AWsTkmswoiEhsZaAiddhNn8hhFdqEIUzi4L6tIwZXXDfq2Kpq07Qy_IaPA8nwceZnSEZ5OvrGimdWxyzYgyP2XO-wX-AVEyPYszKKYv4fzk0_fjMY-jFnhlhFzyIqF8X-WNMyLoEX2kmdal0pnTGaotBBFUmjuR6UqpUiCqUQQWSzyQ5DpXr2CnmTf-DTCRhmyEoBEDndBOmaJEhOYQF7kqyCrVAxD9E7ZV7ENO4zBmtiec_bSoFEtKsUJaVMoAPq5FFl0TjocWy15ttq8uRX9oMUQ8JKTXQhvm9y-x971dWHwtKddSNH6-urYyzdoy5lQM4HVnJ-utqxFRd00ygGzDgtYLqOX35p2m_tG2_qZWTAgnxN7_7XcfHtO3jm78FnaWVyv_DkHVsjyA7cPb5CC-OndVCSGD
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9NAEB6VVIJeEG_Cc5E4IS3Z7CO2j1VFldAmB2il3la2dzcYpU5aEvH3mbHXUQOiSJwc2TvRyjOe-VbzzQzAe2Fy5YMW3GQ6cJ34Ic-ck1z6PBglCz8MlNGdzkbjc_35wlzswVFXC0O0yuj7W5_eeOt4ZxDf5mBVVYOvZJyJxMOWaipMzR3Y1wZ9cg_2Dycn49kNpkc7ZxPXcxLokpsNzevyuvpII8Tbzp3mb-HpT_j5O4vyRlg6fgD3I55kh-2WH8Kerx_B3WnMmD-GzYTESoxIaGisIXDSj7BY_mREFyoRhbPLnLo0zFlVs6tNXjd1Z-gFGQ2e58vAw4KO8Gz6heX1vIpNrhlR5ufMeb_CPyBKpmdxBsX8CZwffzo7GvM4aoGXRsg1z4eU7yu9cUYEPaJLkmpdKJ06naLaQhBBJZkTqS6VKgSiGkVgscADSaYz9RR69bL2z4GJJKQjBI0Y6IR2yuQFIjSHuMiVQZaJ7oPo3rAtYx9yGoexsB3h7LtFpVhSihXSolL68GErsmqbcNy2WHZqs111KfpDiyHiNiG9Fdoxv3-JvevswuJnSbmWvPbLzQ8rk7QpY05EH561drLduhoRddcM-5DuWNB2AbX83n1SV9-a1t_UignhhHjxf_t9C_fGZ9NTezqZnbyEA3rSUo9fQW99vfGvEWCtizfxA_oFI1AjaQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Intracranial+arterial+flow+velocity+mapping+in+quantitative+time-of-flight+MR+angiography+using+deep+machine+learning&rft.jtitle=Magnetic+resonance+imaging&rft.au=Koktzoglou%2C+Ioannis&rft.au=Huang%2C+Rong&rft.date=2023-07-01&rft.issn=1873-5894&rft.eissn=1873-5894&rft.volume=100&rft.spage=10&rft_id=info:doi/10.1016%2Fj.mri.2023.02.005&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0730-725X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0730-725X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0730-725X&client=summon