Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in “double low” chest CTA in children: a feasibility study

Background Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounter limitations under low radiation dose and conventi...

Full description

Saved in:
Bibliographic Details
Published inRadiologia medica Vol. 126; no. 9; pp. 1181 - 1188
Main Authors Sun, Jihang, Li, Haoyan, Gao, Jun, Li, Jianying, Li, Michelle, Zhou, Zuofu, Peng, Yun
Format Journal Article
LanguageEnglish
Published Milan Springer Milan 01.09.2021
Springer Nature B.V
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Background Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounter limitations under low radiation dose and conventional 100 kVp tube voltage may not provide adequate enhancement under low contrast dose. Purpose To evaluate the performance of a deep learning image reconstruction (DLIR) algorithm in conjunction with lower tube voltage in chest CTA in children under reduced radiation and contrast medium (CM) dose. Materials and methods 46 Children (age 5.9 ± 4.2 years) in the study group underwent chest CTA with 70 kVp and CM dose of 0.8–1.2 ml/kg. Images were reconstructed at 0.625 mm using a high setting DLIR (DLIR-H). The control group consisted of 46 age-matching children scanned with 100 kVp, CM dose of 1.3–1.8 ml/kg and images reconstructed with 50% and 100% adaptive statistical iterative reconstruction-V. Two radiologists evaluated images subjectively for overall image noise, vessel contrast and vessel margin clarity separately on a 5-point scale (5, excellent and 1, not acceptable). CT value and image noise of aorta and erector spinae muscle were measured. Results Compared to the control group, the study group reduced the dose-length-product by 11.2% ( p  = 0.01) and CM dose by 24% ( p  < 0.001), improved the enhancement in aorta (416.5 ± 113.1HU vs. 342.0 ± 57.6HU, p  < 0.001) and reduced noise (15.1 ± 3.5HU vs. 18.6 ± 4.4HU, p  < 0.001). The DLIR-H images provided acceptable scores on all 3 aspects of the qualitative evaluation. Conclusion “Double low” chest CTA in children using 70 kVp and DLIR provides high image quality with reduced noise and improved vessel enhancement for diagnosis while further reduces radiation and CM dose.
AbstractList BACKGROUNDChest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounter limitations under low radiation dose and conventional 100 kVp tube voltage may not provide adequate enhancement under low contrast dose. PURPOSETo evaluate the performance of a deep learning image reconstruction (DLIR) algorithm in conjunction with lower tube voltage in chest CTA in children under reduced radiation and contrast medium (CM) dose. MATERIALS AND METHODS46 Children (age 5.9 ± 4.2 years) in the study group underwent chest CTA with 70 kVp and CM dose of 0.8-1.2 ml/kg. Images were reconstructed at 0.625 mm using a high setting DLIR (DLIR-H). The control group consisted of 46 age-matching children scanned with 100 kVp, CM dose of 1.3-1.8 ml/kg and images reconstructed with 50% and 100% adaptive statistical iterative reconstruction-V. Two radiologists evaluated images subjectively for overall image noise, vessel contrast and vessel margin clarity separately on a 5-point scale (5, excellent and 1, not acceptable). CT value and image noise of aorta and erector spinae muscle were measured. RESULTSCompared to the control group, the study group reduced the dose-length-product by 11.2% (p = 0.01) and CM dose by 24% (p < 0.001), improved the enhancement in aorta (416.5 ± 113.1HU vs. 342.0 ± 57.6HU, p < 0.001) and reduced noise (15.1 ± 3.5HU vs. 18.6 ± 4.4HU, p < 0.001). The DLIR-H images provided acceptable scores on all 3 aspects of the qualitative evaluation. CONCLUSION"Double low" chest CTA in children using 70 kVp and DLIR provides high image quality with reduced noise and improved vessel enhancement for diagnosis while further reduces radiation and CM dose.
Background Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounter limitations under low radiation dose and conventional 100 kVp tube voltage may not provide adequate enhancement under low contrast dose. Purpose To evaluate the performance of a deep learning image reconstruction (DLIR) algorithm in conjunction with lower tube voltage in chest CTA in children under reduced radiation and contrast medium (CM) dose. Materials and methods 46 Children (age 5.9 ± 4.2 years) in the study group underwent chest CTA with 70 kVp and CM dose of 0.8–1.2 ml/kg. Images were reconstructed at 0.625 mm using a high setting DLIR (DLIR-H). The control group consisted of 46 age-matching children scanned with 100 kVp, CM dose of 1.3–1.8 ml/kg and images reconstructed with 50% and 100% adaptive statistical iterative reconstruction-V. Two radiologists evaluated images subjectively for overall image noise, vessel contrast and vessel margin clarity separately on a 5-point scale (5, excellent and 1, not acceptable). CT value and image noise of aorta and erector spinae muscle were measured. Results Compared to the control group, the study group reduced the dose-length-product by 11.2% ( p  = 0.01) and CM dose by 24% ( p  < 0.001), improved the enhancement in aorta (416.5 ± 113.1HU vs. 342.0 ± 57.6HU, p  < 0.001) and reduced noise (15.1 ± 3.5HU vs. 18.6 ± 4.4HU, p  < 0.001). The DLIR-H images provided acceptable scores on all 3 aspects of the qualitative evaluation. Conclusion “Double low” chest CTA in children using 70 kVp and DLIR provides high image quality with reduced noise and improved vessel enhancement for diagnosis while further reduces radiation and CM dose.
Author Zhou, Zuofu
Li, Haoyan
Peng, Yun
Sun, Jihang
Li, Michelle
Gao, Jun
Li, Jianying
Author_xml – sequence: 1
  givenname: Jihang
  surname: Sun
  fullname: Sun, Jihang
  organization: Imaging Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health
– sequence: 2
  givenname: Haoyan
  surname: Li
  fullname: Li, Haoyan
  organization: Imaging Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health
– sequence: 3
  givenname: Jun
  surname: Gao
  fullname: Gao, Jun
  organization: Imaging Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health
– sequence: 4
  givenname: Jianying
  surname: Li
  fullname: Li, Jianying
  organization: GE Healthcare
– sequence: 5
  givenname: Michelle
  surname: Li
  fullname: Li, Michelle
  organization: Stanford University
– sequence: 6
  givenname: Zuofu
  surname: Zhou
  fullname: Zhou, Zuofu
  organization: Department of Radiology, Fujian Provincial Maternity and Children’s Hospital, Affiliated Hospital of Fujian Medical University
– sequence: 7
  givenname: Yun
  orcidid: 0000-0001-8213-9716
  surname: Peng
  fullname: Peng, Yun
  email: ppengyun@yahoo.com
  organization: Imaging Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health
BookMark eNp9kUtqHDEQhkVwwGMnF_BKkI2z6ESlV3dnZyYvw0CMcdaNWl09I6ORJlK3w-x8gpwguZxPEtkTCGSRVVVR319V1H9CjkIMSMgZsDfAWP02AyhZV4xDxUA0suLPyAIarivdNuKILBgTomqE5sfkJOdbxiQD1i7IjytMY0xbEyxSvDN-NpOLgcaRGjog7qhHk4ILa-q2Zo00oY0hT2m2T9z5-9Xl9Wtq_DomN2221AX6cP9ziHPvkfr4_eH-F7UbzBNd3lw8du3G-SFheFcWjGiy6513057maR72L8jz0fiML__EU_L144eb5edq9eXT5fJiVVmh-FT1o2I1H8WgQGilxagbVBq4lEKaVmksmZY9G9HKuhdQKgXALAMNDQgrTsn5Ye4uxW9zua7bumzRexMwzrnjSkLdtC1rC_rqH_Q2zimU6wqlOZdlHxSKHyibYs4Jx26XysPSvgPWPVrUHSzqikXdk0UdLyJxEOUChzWmv6P_o_oNJjCWbA
CitedBy_id crossref_primary_10_3390_tomography9010018
crossref_primary_10_1007_s11547_023_01710_w
crossref_primary_10_3390_diagnostics12123223
crossref_primary_10_3390_tomography9030075
crossref_primary_10_3390_diagnostics13020302
crossref_primary_10_3390_children9071044
crossref_primary_10_3390_diagnostics12051151
crossref_primary_10_1016_j_ejrad_2024_111301
crossref_primary_10_3390_cancers15174344
crossref_primary_10_3390_children10081372
crossref_primary_10_3390_jcm12041489
crossref_primary_10_3390_jpm12081344
crossref_primary_10_3390_biology12020213
crossref_primary_10_1186_s13027_023_00495_x
crossref_primary_10_3390_jcm11102766
crossref_primary_10_1155_2022_6355098
crossref_primary_10_1053_j_sult_2023_03_009
crossref_primary_10_3390_cancers15020351
crossref_primary_10_5812_iranjradiol_126572
crossref_primary_10_3390_tomography9030095
crossref_primary_10_3390_diagnostics13182877
crossref_primary_10_1007_s00330_022_08975_1
crossref_primary_10_3390_jpm12071153
Cites_doi 10.1148/radiol.14140244
10.1007/s00247-002-0669-8
10.1148/radiol.2016151621
10.1007/s11547-019-00995-0
10.1111/ped.13101
10.1007/s00330-020-06724-w
10.1007/s00330-019-06170-3
10.1016/j.carj.2018.05.004
10.1148/radiol.2015132766
10.1007/s11547-020-01191-1
10.1148/rg.351140089
10.1007/s00330-020-07358-8
10.1097/RCT.0000000000000329
10.1053/j.sempedsurg.2016.02.009
10.1097/MD.0000000000019347
10.1016/j.jpedsurg.2018.08.054
10.1007/s00247-016-3588-9
10.1097/RTI.0000000000000127
10.3174/ajnr.A5372
ContentType Journal Article
Copyright Italian Society of Medical Radiology 2021
Italian Society of Medical Radiology 2021.
Copyright_xml – notice: Italian Society of Medical Radiology 2021
– notice: Italian Society of Medical Radiology 2021.
DBID AAYXX
CITATION
7X8
DOI 10.1007/s11547-021-01384-2
DatabaseName CrossRef
MEDLINE - Academic
DatabaseTitle CrossRef
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic


DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 1826-6983
EndPage 1188
ExternalDocumentID 10_1007_s11547_021_01384_2
GrantInformation_xml – fundername: Beijing Children’s Hospital Young Investigator Program
  grantid: BCH-YIPB-2016-06
GroupedDBID -5E
-5G
-BR
-EM
-Y2
-~C
.86
.VR
06C
06D
0R~
0VY
1N0
203
29P
29~
2J2
2JN
2JY
2KG
2KM
2LR
2VQ
2~H
30V
4.4
406
408
40D
40E
53G
5GY
5VS
67Z
6NX
8TC
8UJ
95-
95.
95~
96X
AAAVM
AABHQ
AAFGU
AAHNG
AAIAL
AAJKR
AANXM
AANZL
AAPBV
AARHV
AARTL
AATNV
AATVU
AAUYE
AAWCG
AAYFA
AAYIU
AAYQN
AAYTO
ABBBX
ABDZT
ABECU
ABFGW
ABFTV
ABHLI
ABHQN
ABIPD
ABJNI
ABJOX
ABKAS
ABKCH
ABKTR
ABMNI
ABMQK
ABNWP
ABPLI
ABQBU
ABSXP
ABTEG
ABTKH
ABTMW
ABULA
ABWNU
ABXPI
ACBMV
ACBRV
ACBXY
ACBYP
ACGFS
ACHSB
ACHXU
ACIGE
ACIPQ
ACKNC
ACMDZ
ACMLO
ACOKC
ACOMO
ACSNA
ACTTH
ACVWB
ACWMK
ADHHG
ADHIR
ADINQ
ADJJI
ADKNI
ADKPE
ADMDM
ADOXG
ADQRH
ADRFC
ADTPH
ADURQ
ADYFF
ADYOE
ADZKW
AEBTG
AEFTE
AEGAL
AEGNC
AEGXH
AEJHL
AEJRE
AEKMD
AENEX
AEOHA
AEPYU
AESKC
AESTI
AETLH
AEVLU
AEVTX
AEXYK
AFLOW
AFNRJ
AFQWF
AFWTZ
AFYQB
AFZKB
AGAYW
AGDGC
AGGBP
AGJBK
AGMZJ
AGQMX
AGWIL
AGWZB
AGYKE
AHAVH
AHBYD
AHIZS
AHKAY
AHSBF
AHYZX
AIAKS
AIIXL
AILAN
AIMYW
AITGF
AJBLW
AJDOV
AJRNO
AJZVZ
AKMHD
AKQUC
ALMA_UNASSIGNED_HOLDINGS
ALWAN
AMKLP
AMTXH
AMXSW
AMYLF
AMYQR
AOCGG
ARMRJ
ASPBG
AVWKF
AXYYD
AZFZN
B-.
BA0
BDATZ
BGNMA
CAG
COF
CS3
CSCUP
DDRTE
DNIVK
DPUIP
DU5
EBD
EBLON
EBS
EIOEI
EJD
EMOBN
EN4
ESBYG
F5P
FEDTE
FERAY
FFXSO
FIGPU
FINBP
FNLPD
FRRFC
FSGXE
FWDCC
G-Y
G-Z
GGCAI
GGRSB
GJIRD
GNWQR
GQ6
GQ7
HF~
HG5
HG6
HLICF
HMJXF
HRMNR
HVGLF
HZ~
IHE
IJ-
IKXTQ
IMOTQ
ITM
IWAJR
IXC
IXE
IZQ
I~X
I~Z
J-C
J0Z
JBSCW
JZLTJ
KDC
KOV
KPH
LLZTM
M4Y
MA-
N2Q
NB0
NPVJJ
NQJWS
NU0
O9-
O93
O9I
O9J
P9S
PF0
PT4
QOR
QOS
R89
R9I
RIG
RNS
ROL
RPX
RSV
S16
S1Z
S27
S37
S3B
SAP
SDH
SHX
SISQX
SJYHP
SMD
SNE
SNPRN
SNX
SOHCF
SOJ
SPISZ
SRMVM
SSLCW
SSXJD
STPWE
SV3
SZ9
SZN
T13
TSG
TSK
TSV
TT1
TUC
U2A
U9L
UG4
UNUBA
UOJIU
UTJUX
UZXMN
VC2
VFIZW
W23
W48
WK8
YLTOR
Z45
Z7U
Z7X
Z82
Z87
ZA5
ZMTXR
ZOVNA
~A9
~S-
AACDK
AAJBT
AASML
AAYXX
ABAKF
ACAOD
ACDTI
ACZOJ
AEFQL
AEMSY
AFBBN
AGQEE
AGRTI
AIGIU
CITATION
H13
AAYZH
7X8
ID FETCH-LOGICAL-c352t-bf5072f3d5136563f68e56124434a956e24464b0fec47b314465110c0161813c3
IEDL.DBID U2A
ISSN 0033-8362
IngestDate Thu Oct 24 21:06:53 EDT 2024
Sat Oct 26 13:23:01 EDT 2024
Thu Sep 12 19:17:45 EDT 2024
Sat Dec 16 12:09:05 EST 2023
IsPeerReviewed true
IsScholarly true
Issue 9
Keywords Deep learning
Tomography
Thorax
Child
X-ray computed
Image reconstruction
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c352t-bf5072f3d5136563f68e56124434a956e24464b0fec47b314465110c0161813c3
Notes ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Undefined-1
ObjectType-Feature-3
content type line 23
ORCID 0000-0001-8213-9716
PQID 2562249561
PQPubID 2043526
PageCount 8
ParticipantIDs proquest_miscellaneous_2541789909
proquest_journals_2562249561
crossref_primary_10_1007_s11547_021_01384_2
springer_journals_10_1007_s11547_021_01384_2
PublicationCentury 2000
PublicationDate 2021-09-01
PublicationDateYYYYMMDD 2021-09-01
PublicationDate_xml – month: 09
  year: 2021
  text: 2021-09-01
  day: 01
PublicationDecade 2020
PublicationPlace Milan
PublicationPlace_xml – name: Milan
– name: Torino
PublicationSubtitle Official Journal of the Italian Society of Medical and Interventional Radiology
PublicationTitle Radiologia medica
PublicationTitleAbbrev Radiol med
PublicationYear 2021
Publisher Springer Milan
Springer Nature B.V
Publisher_xml – name: Springer Milan
– name: Springer Nature B.V
References Greffier, Hamard, Pereira (CR17) 2020; 30
Takanori, Yoshinori, Takeshi (CR21) 2018; 69
Carl, Michael, Andrada (CR2) 2016; 25
Andrea, Marie, Oliver (CR23) 2020; 94
CR15
Akagi, Nakamura, Higaki (CR16) 2019; 9
MacDougall, Kleinman, Yu (CR10) 2016; 46
Haner, Sibel, Peter (CR20) 2011; 29
Sergei, Bernadette, Pierluigi (CR7) 2021; 37
Sun, Yang, Zhou (CR12) 2020; 125
Han, Kyung, Jai (CR5) 2020; 99
Geyer, Schoepf, Meinel (CR22) 2015; 276
Trotman-Dickenson (CR4) 2015; 30
Basheer, Roya, Jose (CR3) 2015; 9
Chankue, Ki, Yunsub (CR24) 2020
Na, Qi, Chenghao (CR6) 2019; 54
Hasan, Erkut, Okan (CR1) 2017; 59
(CR8) 2002; 32
Benz, Benetos, Rampidis (CR18) 2020; 13
Shimoyama, Nishii, Watanabe (CR11) 2017; 38
Mathias, Holger, Joseph (CR9) 2014; 273
Sun, Hu, Shen (CR14) 2019; 124
Christopher, Melissa, Santiago (CR19) 2015; 35
Goenka, Herts, Dong (CR13) 2016; 280
Zhang, Li, Schoepf (CR25) 2016; 40
M Akagi (1384_CR16) 2019; 9
C Benz (1384_CR18) 2020; 13
D Haner (1384_CR20) 2011; 29
AH Goenka (1384_CR13) 2016; 280
RD MacDougall (1384_CR10) 2016; 46
W Christopher (1384_CR19) 2015; 35
MH Sergei (1384_CR7) 2021; 37
Z Na (1384_CR6) 2019; 54
M Mathias (1384_CR9) 2014; 273
J Sun (1384_CR14) 2019; 124
LL Geyer (1384_CR22) 2015; 276
P Chankue (1384_CR24) 2020
The Society for Pediatric Radiology (1384_CR8) 2002; 32
S Andrea (1384_CR23) 2020; 94
JK Han (1384_CR5) 2020; 99
S Shimoyama (1384_CR11) 2017; 38
J Sun (1384_CR12) 2020; 125
T Basheer (1384_CR3) 2015; 9
M Takanori (1384_CR21) 2018; 69
LJ Zhang (1384_CR25) 2016; 40
T Hasan (1384_CR1) 2017; 59
LB Carl (1384_CR2) 2016; 25
J Greffier (1384_CR17) 2020; 30
1384_CR15
B Trotman-Dickenson (1384_CR4) 2015; 30
References_xml – volume: 273
  start-page: 373
  year: 2014
  end-page: 382
  ident: CR9
  article-title: Closing in on the K edge: coronary CT angiography at 100, 80, and 70 kV-initial comparison of a second-versus a third-generation dual-source CT system
  publication-title: Radiology
  doi: 10.1148/radiol.14140244
  contributor:
    fullname: Joseph
– volume: 32
  start-page: 217
  year: 2002
  end-page: 313
  ident: CR8
  article-title: The ALARA (as low as reasonably achievable) concept in pediatric CT intelligent dose reduction. Multidisciplinary conference organized by the Society of Pediatric Radiology
  publication-title: Pediatr Radiol
  doi: 10.1007/s00247-002-0669-8
– volume: 280
  start-page: 475
  year: 2016
  end-page: 482
  ident: CR13
  article-title: Image noise, cnr, and detectability of low-contrast, low-attenuation liver lesions in a phantom: effects of radiation exposure, phantom size, integrated circuit detector, and iterative reconstruction
  publication-title: Radiology
  doi: 10.1148/radiol.2016151621
  contributor:
    fullname: Dong
– volume: 124
  start-page: 595
  year: 2019
  end-page: 601
  ident: CR14
  article-title: Improving image quality with model-based iterative reconstruction algorithm for chest CT in children with reduced contrast concentration
  publication-title: Radiol Med
  doi: 10.1007/s11547-019-00995-0
  contributor:
    fullname: Shen
– volume: 59
  start-page: 134
  year: 2017
  end-page: 140
  ident: CR1
  article-title: Assessment of children with vascular ring
  publication-title: Pediatr Int
  doi: 10.1111/ped.13101
  contributor:
    fullname: Okan
– volume: 30
  start-page: 3951
  year: 2020
  end-page: 3959
  ident: CR17
  article-title: Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study
  publication-title: Eur Radiol
  doi: 10.1007/s00330-020-06724-w
  contributor:
    fullname: Pereira
– volume: 9
  start-page: 6163
  year: 2019
  end-page: 6171
  ident: CR16
  article-title: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
  publication-title: Eur Radiol
  doi: 10.1007/s00330-019-06170-3
  contributor:
    fullname: Higaki
– volume: 69
  start-page: 390
  year: 2018
  end-page: 396
  ident: CR21
  article-title: Radiation dose reduction with a low-tube voltage technique for pediatric chest computed tomographic angiography based on the contrast-to-noise ratio index
  publication-title: Can Assoc Radiol J
  doi: 10.1016/j.carj.2018.05.004
  contributor:
    fullname: Takeshi
– volume: 37
  start-page: 80
  year: 2021
  end-page: 88
  ident: CR7
  article-title: A clinical guideline for structured assessment of CT-imaging in congenital lung abnormalities
  publication-title: Paediatr Respir Rev
  contributor:
    fullname: Pierluigi
– volume: 276
  start-page: 339
  year: 2015
  end-page: 357
  ident: CR22
  article-title: State of the art: iterative CT reconstruction techniques
  publication-title: Radiology
  doi: 10.1148/radiol.2015132766
  contributor:
    fullname: Meinel
– volume: 125
  start-page: 918
  year: 2020
  end-page: 925
  ident: CR12
  article-title: Performance evaluation of two iterative reconstruction algorithms, MBIR and ASIR, in low radiation dose and low contrast dose abdominal CT in children
  publication-title: Radiol Med
  doi: 10.1007/s11547-020-01191-1
  contributor:
    fullname: Zhou
– volume: 35
  start-page: 32
  year: 2015
  end-page: 49
  ident: CR19
  article-title: Bronchial arteries: anatomy, function, hypertrophy, and anomalies
  publication-title: Radiographics
  doi: 10.1148/rg.351140089
  contributor:
    fullname: Santiago
– year: 2020
  ident: CR24
  article-title: CT iterative vs deep learning reconstruction: comparison of noise and sharpness
  publication-title: Eur Radiol
  doi: 10.1007/s00330-020-07358-8
  contributor:
    fullname: Yunsub
– volume: 40
  start-page: 109
  year: 2016
  end-page: 117
  ident: CR25
  article-title: Non-electrocardiogram-triggered 70-kVp high-pitch computed tomography angiography of the whole aorta with iterative reconstruction: initial results
  publication-title: J Comput Assist Tomogr
  doi: 10.1097/RCT.0000000000000329
  contributor:
    fullname: Schoepf
– volume: 13
  start-page: 30464
  year: 2020
  end-page: 30472
  ident: CR18
  article-title: Validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy
  publication-title: J Cardiovasc Comput Tomogr
  contributor:
    fullname: Rampidis
– ident: CR15
– volume: 25
  start-page: 165
  year: 2016
  end-page: 175
  ident: CR2
  article-title: Vascular rings
  publication-title: Semin Pediatr Surg
  doi: 10.1053/j.sempedsurg.2016.02.009
  contributor:
    fullname: Andrada
– volume: 99
  start-page: e19347
  year: 2020
  ident: CR5
  article-title: Intralobar pulmonary sequestration with cystic degeneration mimicking a bronchogenic cyst in an elderly patient: a case report and literature review
  publication-title: Medicine (Baltimore)
  doi: 10.1097/MD.0000000000019347
  contributor:
    fullname: Jai
– volume: 54
  start-page: 1286
  year: 2019
  end-page: 1292
  ident: CR6
  article-title: Distribution, diagnosis, and treatment of pulmonary sequestration: report of 208 cases
  publication-title: J Pediatr Surg
  doi: 10.1016/j.jpedsurg.2018.08.054
  contributor:
    fullname: Chenghao
– volume: 46
  start-page: 1114
  year: 2016
  end-page: 1119
  ident: CR10
  article-title: Pediatric thoracic CT angiography at 70 kV: a phantom study to investigate the effects on image quality and radiation dose
  publication-title: Pediatr Radiol
  doi: 10.1007/s00247-016-3588-9
  contributor:
    fullname: Yu
– volume: 9
  start-page: 05
  year: 2015
  end-page: 8
  ident: CR3
  article-title: Pulmonary sequestration: A 29 patient case series and review
  publication-title: J Clin Diagn Res
  contributor:
    fullname: Jose
– volume: 30
  start-page: 46
  year: 2015
  end-page: 59
  ident: CR4
  article-title: Congenital lung disease in the adult: guide to the evaluation and management
  publication-title: J Thorac Imaging
  doi: 10.1097/RTI.0000000000000127
  contributor:
    fullname: Trotman-Dickenson
– volume: 38
  start-page: 2399
  year: 2017
  end-page: 2405
  ident: CR11
  article-title: Advantages of 70-kV CT angiography for the visualization of the adamkiewicz artery: comparison with 120-kV imaging
  publication-title: AJNR Am J Neuroradiol
  doi: 10.3174/ajnr.A5372
  contributor:
    fullname: Watanabe
– volume: 94
  start-page: 20200677
  year: 2020
  ident: CR23
  article-title: Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography
  publication-title: Br J Radiol
  contributor:
    fullname: Oliver
– volume: 29
  start-page: S86
  year: 2011
  end-page: 91
  ident: CR20
  article-title: Assessment of disease activity and progression in Takayasu's arteritis
  publication-title: Clin Exp Rheumatol
  contributor:
    fullname: Peter
– volume: 124
  start-page: 595
  year: 2019
  ident: 1384_CR14
  publication-title: Radiol Med
  doi: 10.1007/s11547-019-00995-0
  contributor:
    fullname: J Sun
– volume: 273
  start-page: 373
  year: 2014
  ident: 1384_CR9
  publication-title: Radiology
  doi: 10.1148/radiol.14140244
  contributor:
    fullname: M Mathias
– volume: 54
  start-page: 1286
  year: 2019
  ident: 1384_CR6
  publication-title: J Pediatr Surg
  doi: 10.1016/j.jpedsurg.2018.08.054
  contributor:
    fullname: Z Na
– volume: 94
  start-page: 20200677
  year: 2020
  ident: 1384_CR23
  publication-title: Br J Radiol
  contributor:
    fullname: S Andrea
– volume: 280
  start-page: 475
  year: 2016
  ident: 1384_CR13
  publication-title: Radiology
  doi: 10.1148/radiol.2016151621
  contributor:
    fullname: AH Goenka
– volume: 276
  start-page: 339
  year: 2015
  ident: 1384_CR22
  publication-title: Radiology
  doi: 10.1148/radiol.2015132766
  contributor:
    fullname: LL Geyer
– volume: 38
  start-page: 2399
  year: 2017
  ident: 1384_CR11
  publication-title: AJNR Am J Neuroradiol
  doi: 10.3174/ajnr.A5372
  contributor:
    fullname: S Shimoyama
– volume: 125
  start-page: 918
  year: 2020
  ident: 1384_CR12
  publication-title: Radiol Med
  doi: 10.1007/s11547-020-01191-1
  contributor:
    fullname: J Sun
– volume: 30
  start-page: 3951
  year: 2020
  ident: 1384_CR17
  publication-title: Eur Radiol
  doi: 10.1007/s00330-020-06724-w
  contributor:
    fullname: J Greffier
– volume: 25
  start-page: 165
  year: 2016
  ident: 1384_CR2
  publication-title: Semin Pediatr Surg
  doi: 10.1053/j.sempedsurg.2016.02.009
  contributor:
    fullname: LB Carl
– volume: 35
  start-page: 32
  year: 2015
  ident: 1384_CR19
  publication-title: Radiographics
  doi: 10.1148/rg.351140089
  contributor:
    fullname: W Christopher
– volume: 32
  start-page: 217
  year: 2002
  ident: 1384_CR8
  publication-title: Pediatr Radiol
  doi: 10.1007/s00247-002-0669-8
  contributor:
    fullname: The Society for Pediatric Radiology
– volume: 30
  start-page: 46
  year: 2015
  ident: 1384_CR4
  publication-title: J Thorac Imaging
  doi: 10.1097/RTI.0000000000000127
  contributor:
    fullname: B Trotman-Dickenson
– volume: 59
  start-page: 134
  year: 2017
  ident: 1384_CR1
  publication-title: Pediatr Int
  doi: 10.1111/ped.13101
  contributor:
    fullname: T Hasan
– volume: 29
  start-page: S86
  year: 2011
  ident: 1384_CR20
  publication-title: Clin Exp Rheumatol
  contributor:
    fullname: D Haner
– volume: 69
  start-page: 390
  year: 2018
  ident: 1384_CR21
  publication-title: Can Assoc Radiol J
  doi: 10.1016/j.carj.2018.05.004
  contributor:
    fullname: M Takanori
– volume: 9
  start-page: 05
  year: 2015
  ident: 1384_CR3
  publication-title: J Clin Diagn Res
  contributor:
    fullname: T Basheer
– volume: 9
  start-page: 6163
  year: 2019
  ident: 1384_CR16
  publication-title: Eur Radiol
  doi: 10.1007/s00330-019-06170-3
  contributor:
    fullname: M Akagi
– ident: 1384_CR15
– volume: 46
  start-page: 1114
  year: 2016
  ident: 1384_CR10
  publication-title: Pediatr Radiol
  doi: 10.1007/s00247-016-3588-9
  contributor:
    fullname: RD MacDougall
– volume: 13
  start-page: 30464
  year: 2020
  ident: 1384_CR18
  publication-title: J Cardiovasc Comput Tomogr
  contributor:
    fullname: C Benz
– volume: 40
  start-page: 109
  year: 2016
  ident: 1384_CR25
  publication-title: J Comput Assist Tomogr
  doi: 10.1097/RCT.0000000000000329
  contributor:
    fullname: LJ Zhang
– year: 2020
  ident: 1384_CR24
  publication-title: Eur Radiol
  doi: 10.1007/s00330-020-07358-8
  contributor:
    fullname: P Chankue
– volume: 99
  start-page: e19347
  year: 2020
  ident: 1384_CR5
  publication-title: Medicine (Baltimore)
  doi: 10.1097/MD.0000000000019347
  contributor:
    fullname: JK Han
– volume: 37
  start-page: 80
  year: 2021
  ident: 1384_CR7
  publication-title: Paediatr Respir Rev
  contributor:
    fullname: MH Sergei
SSID ssj0040109
Score 2.4483726
Snippet Background Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium...
BackgroundChest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium...
BACKGROUNDChest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium...
SourceID proquest
crossref
springer
SourceType Aggregation Database
Publisher
StartPage 1181
SubjectTerms Acceptable noise levels
Algorithms
Angiography
Aorta
Chest
Chest Radiology
Computed tomography
Coronary vessels
Deep learning
Diagnostic Radiology
Electric potential
Feasibility studies
Image contrast
Image enhancement
Image quality
Image reconstruction
Imaging
Interventional Radiology
Machine learning
Medical imaging
Medicine
Medicine & Public Health
Muscles
Neuroradiology
Noise
Performance evaluation
Radiation
Radiation dosage
Radiology
Ultrasound
Voltage
Title Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in “double low” chest CTA in children: a feasibility study
URI https://link.springer.com/article/10.1007/s11547-021-01384-2
https://www.proquest.com/docview/2562249561
https://search.proquest.com/docview/2541789909
Volume 126
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NSgMxEA5iQbyIv1itZQQPiga6STbbeiu1_isiFvS07G6TWmh3i7aItz6BT6Av1ydxku66KHrwtpCQwH7J5Btm5htCdiTDN1i6DpWmrEdwIWnIHZc6XAQs4pFi2kR0r67laUuc37v3eR23TXbPIpLWUOe1bvjYe9RkFJjgmqBodwtIHoTxuFqsnplfYWI9Uy1GTqtontNKmd_X-P4a5RTzR1TUPjbHi2QhZYlQn8K6RGZUvEzmrtI4-Ap5u8nz_SEX7IZEQwBtpQaQdoPoQLePFgOs3_ulFQu7R5dnt3sQ9DrJU3f42IduDJPxezsZhT0FveRlMv4A20oLGnd1M5oVfR_iBloFaVLtK1h52lXSOm7eNU5p2lmBRki4hjTUSAOZ5m3XZLlJrmVVmTaZAsEK0GNS-CVFWNEqEh5iZ1TVkCdEVl7f4RFfI7NxEqt1ApGoOSJoe5XI40JLXUOjoSRHr1Ew7VXdItnP_rA_mApo-LlUssHDRzx8i4fPiqSUgeCnl-nZR1bGTIts6RTJ9tcwXgMT2whilYzMHOF46DtWakVykIGXL_H3jhv_m75J5pk9PybLrERmETi1hbRkGJZJoX7ycNEs2-P4CXH22Oc
link.rule.ids 315,783,787,27937,27938,41094,41536,42163,42605,52124,52247
linkProvider Springer Nature
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NSgMxEA6ioF7EX6y_I3hQNNBNstnWW1FL1VZEWvC27G6TWmh3i20Rbz6BT6Av55M4SXe7KHrwtpCQwH7JzBdm5htCDiVDHyxdh0pT1iO4kDTkjksdLgIW8UgxbSK6jVtZa4nrB_chLQobZtnuWUjSWuq82A29vUdNSoGJrgmKhnfO6KubRL4Wq2T2V5hgz0SMkdMS2ue0VOb3Nb67o5xj_giLWm9TXSZLKU2EygTXFTKj4lUy30gD4Wvk7S5P-IdcsRsSDQG0lRpA2g6iA90-mgywD9-pWCwcXdSv7o8h6HWSp-7osQ_dGD5f39vJOOwp6CXPn68fYHtpwXmzYkazqu8z3ECrIM2qfQGrT7tOWtXL5nmNpq0VaISMa0RDjTyQad52TZqb5FqWlOmTKRCtAJ9MCr-kCItaRcJD8IysGhKFyOrrOzziG2Q2TmK1SSASZUcEba8YeVxoqctoNZTk-GwUTHslt0BOsj_sDyYKGn6ulWzw8BEP3-LhswLZyUDw09s09JGWMdMjWzoFcjAdxntgghtBrJKxmSMcDx-PxXKBnGbg5Uv8vePW_6bvk4Vas1H361e3N9tkkdmzZFLOdsgsgqh2kaOMwj17JL8ANgjarg
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTtwwEB6hXQlxgZZSsUDLVOqhiBo2sePsclsBWyg_QhVI9BQljg0rlmRFs6raE0_AE8DL7ZMwzg9pUXuoeotky3Y84_nRzHwD8F66pIOl5zBpy3oEF5JF3PGYw0XoKq60a2xE9-hY7p2Jz-fe-S9V_Hm2exWSLGoaLEpTkm2OYrNZF76R5veZTS-wkTbBSAg3hUOuSgOavU9fD3YraSxs6KeAZuSsQ9K6LJz58yq_K6fa4nwWJM11T38OwurURcrJ1cY4izbUz2eAjv_zWy9gtjRMsVdw0kuY0sk8TB-VofdXcHdSlxhgjRGOqcEQY61HWDaguMDBNQkpzF3tJ3ha_LBzuP9lDcPhRXozyC6vcZDg5PY-TsfRUOMw_T65fcC8exdun_bsaFVnvkUbGB2Webw_MEfEXYCz_u7p9h4rmzkwRTZexiJDlqdreOzZxDrJjexo25lTEH-E5KRp-pIiahuthE_sYoHcyDRROaK_wxV_DY0kTfQioBJdR4Sx31Y-F0aaLskpLTlRX7jG73gtWK-oGIwKzI6gRme2VxzQFQf5FQduC1YqQgfl-_0WkCHo2q7c0mnBu6dhenk2nBImOh3bOcLxyV1td1vwsaJtvcTfd1z6t-mrMH2y0w8O948PlmHGzbnD5ritQINoqN-QUZRFb0u-fwTJEwCQ
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=Performance+evaluation+of+a+deep+learning+image+reconstruction+%28DLIR%29+algorithm+in+%E2%80%9Cdouble+low%E2%80%9D+chest+CTA+in+children%3A+a+feasibility+study&rft.jtitle=Radiologia+medica&rft.au=Sun%2C+Jihang&rft.au=Li%2C+Haoyan&rft.au=Gao%2C+Jun&rft.au=Li%2C+Jianying&rft.date=2021-09-01&rft.issn=0033-8362&rft.eissn=1826-6983&rft.volume=126&rft.issue=9&rft.spage=1181&rft.epage=1188&rft_id=info:doi/10.1007%2Fs11547-021-01384-2&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s11547_021_01384_2
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0033-8362&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0033-8362&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0033-8362&client=summon