Assessment of CT-to-physical density table for multiple image reconstruction functions with a large-bore scanner for radiotherapy treatment planning

•Comprehensive investigation of HU accuracy for a large-bore CT for RT planning.•Marginal impact on CT values with deep learning-based reconstruction.•Minimal difference in dose calculation among multiple reconstruction algorithms.•CT values remain stable as long as the subject fits within the scan...

Full description

Saved in:
Bibliographic Details
Published inPhysica medica Vol. 133; p. 104970
Main Authors Okumura, Takuro, Koganezawa, Akito S., Nakashima, Takeo, Ochi, Yusuke, Tsubouchi, Kento, Murakami, Yuji
Format Journal Article
LanguageEnglish
Published Italy Elsevier Ltd 01.05.2025
Subjects
Online AccessGet full text
ISSN1120-1797
1724-191X
1724-191X
DOI10.1016/j.ejmp.2025.104970

Cover

Abstract •Comprehensive investigation of HU accuracy for a large-bore CT for RT planning.•Marginal impact on CT values with deep learning-based reconstruction.•Minimal difference in dose calculation among multiple reconstruction algorithms.•CT values remain stable as long as the subject fits within the scan FOV of 70 cm. To evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of different combinations of the image reconstruction function (IRF) (AiCE and AIDR) and scan parameters on the CT-to-physical density (CT-PD) table and radiation dose in the phantom, and the effect of different object positions on CT values. To investigate IRF’s influence on each material, we calculated CT values by varying tube current, pitch, field of view (FOV), and phantom position for each IRF, comparing them with reference values using filtered back projection (FBP). Furthermore, we evaluated changes in depth dose values due to IRF differences using a solid phantom. In the combinations of changes in IRF and scan parameters the change in CT value (ΔHU) of each material was within ±10 HU, except for most conditions. The change in physical density (ΔPD) was within ±0.02 g/cm3 for all combinations. For changes in phantom position, ΔHU was within ±25 HU for changes within the scan FOV, except for Bone 200 mg/cc and 1250 mg/cc. In areas outside the scan FOV with an expanded FOV, ΔHU was significantly larger than within the scan FOV. Variations in depth dose for different IRFs using solid phantoms were within ±0.5 %, except at material boundaries. Our evaluations of the CT values and dose calculations suggested no need to change the CT-PD table, even with multiple IRFs.
AbstractList •Comprehensive investigation of HU accuracy for a large-bore CT for RT planning.•Marginal impact on CT values with deep learning-based reconstruction.•Minimal difference in dose calculation among multiple reconstruction algorithms.•CT values remain stable as long as the subject fits within the scan FOV of 70 cm. To evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of different combinations of the image reconstruction function (IRF) (AiCE and AIDR) and scan parameters on the CT-to-physical density (CT-PD) table and radiation dose in the phantom, and the effect of different object positions on CT values. To investigate IRF’s influence on each material, we calculated CT values by varying tube current, pitch, field of view (FOV), and phantom position for each IRF, comparing them with reference values using filtered back projection (FBP). Furthermore, we evaluated changes in depth dose values due to IRF differences using a solid phantom. In the combinations of changes in IRF and scan parameters the change in CT value (ΔHU) of each material was within ±10 HU, except for most conditions. The change in physical density (ΔPD) was within ±0.02 g/cm3 for all combinations. For changes in phantom position, ΔHU was within ±25 HU for changes within the scan FOV, except for Bone 200 mg/cc and 1250 mg/cc. In areas outside the scan FOV with an expanded FOV, ΔHU was significantly larger than within the scan FOV. Variations in depth dose for different IRFs using solid phantoms were within ±0.5 %, except at material boundaries. Our evaluations of the CT values and dose calculations suggested no need to change the CT-PD table, even with multiple IRFs.
To evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of different combinations of the image reconstruction function (IRF) (AiCE and AIDR) and scan parameters on the CT-to-physical density (CT-PD) table and radiation dose in the phantom, and the effect of different object positions on CT values. To investigate IRF's influence on each material, we calculated CT values by varying tube current, pitch, field of view (FOV), and phantom position for each IRF, comparing them with reference values using filtered back projection (FBP). Furthermore, we evaluated changes in depth dose values due to IRF differences using a solid phantom. In the combinations of changes in IRF and scan parameters the change in CT value (ΔHU) of each material was within ±10 HU, except for most conditions. The change in physical density (ΔPD) was within ±0.02 g/cm for all combinations. For changes in phantom position, ΔHU was within ±25 HU for changes within the scan FOV, except for Bone 200 mg/cc and 1250 mg/cc. In areas outside the scan FOV with an expanded FOV, ΔHU was significantly larger than within the scan FOV. Variations in depth dose for different IRFs using solid phantoms were within ±0.5 %, except at material boundaries. Our evaluations of the CT values and dose calculations suggested no need to change the CT-PD table, even with multiple IRFs.
To evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of different combinations of the image reconstruction function (IRF) (AiCE and AIDR) and scan parameters on the CT-to-physical density (CT-PD) table and radiation dose in the phantom, and the effect of different object positions on CT values.PURPOSETo evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of different combinations of the image reconstruction function (IRF) (AiCE and AIDR) and scan parameters on the CT-to-physical density (CT-PD) table and radiation dose in the phantom, and the effect of different object positions on CT values.To investigate IRF's influence on each material, we calculated CT values by varying tube current, pitch, field of view (FOV), and phantom position for each IRF, comparing them with reference values using filtered back projection (FBP). Furthermore, we evaluated changes in depth dose values due to IRF differences using a solid phantom.METHODSTo investigate IRF's influence on each material, we calculated CT values by varying tube current, pitch, field of view (FOV), and phantom position for each IRF, comparing them with reference values using filtered back projection (FBP). Furthermore, we evaluated changes in depth dose values due to IRF differences using a solid phantom.In the combinations of changes in IRF and scan parameters the change in CT value (ΔHU) of each material was within ±10 HU, except for most conditions. The change in physical density (ΔPD) was within ±0.02 g/cm3 for all combinations. For changes in phantom position, ΔHU was within ±25 HU for changes within the scan FOV, except for Bone 200 mg/cc and 1250 mg/cc. In areas outside the scan FOV with an expanded FOV, ΔHU was significantly larger than within the scan FOV. Variations in depth dose for different IRFs using solid phantoms were within ±0.5 %, except at material boundaries.RESULTSIn the combinations of changes in IRF and scan parameters the change in CT value (ΔHU) of each material was within ±10 HU, except for most conditions. The change in physical density (ΔPD) was within ±0.02 g/cm3 for all combinations. For changes in phantom position, ΔHU was within ±25 HU for changes within the scan FOV, except for Bone 200 mg/cc and 1250 mg/cc. In areas outside the scan FOV with an expanded FOV, ΔHU was significantly larger than within the scan FOV. Variations in depth dose for different IRFs using solid phantoms were within ±0.5 %, except at material boundaries.Our evaluations of the CT values and dose calculations suggested no need to change the CT-PD table, even with multiple IRFs.CONCLUSIONOur evaluations of the CT values and dose calculations suggested no need to change the CT-PD table, even with multiple IRFs.
ArticleNumber 104970
Author Okumura, Takuro
Koganezawa, Akito S.
Ochi, Yusuke
Murakami, Yuji
Tsubouchi, Kento
Nakashima, Takeo
Author_xml – sequence: 1
  givenname: Takuro
  orcidid: 0009-0005-7102-328X
  surname: Okumura
  fullname: Okumura, Takuro
  organization: Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
– sequence: 2
  givenname: Akito S.
  orcidid: 0000-0002-7823-5884
  surname: Koganezawa
  fullname: Koganezawa, Akito S.
  email: koganezawa.akito.ow@teikyo-u.ac.jp
  organization: Department of Information and Electronic Engineering, Faculty of Science and Engineering, Teikyo University, Tochigi 320-8551, Japan
– sequence: 3
  givenname: Takeo
  surname: Nakashima
  fullname: Nakashima, Takeo
  organization: Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
– sequence: 4
  givenname: Yusuke
  surname: Ochi
  fullname: Ochi, Yusuke
  organization: Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
– sequence: 5
  givenname: Kento
  surname: Tsubouchi
  fullname: Tsubouchi, Kento
  organization: Department of Clinical Practice and Support, Hiroshima University Hospital, Hiroshima 734-8551, Japan
– sequence: 6
  givenname: Yuji
  orcidid: 0000-0003-3596-3010
  surname: Murakami
  fullname: Murakami, Yuji
  organization: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan
BackLink https://www.ncbi.nlm.nih.gov/pubmed/40187130$$D View this record in MEDLINE/PubMed
BookMark eNqNkc1u1DAUhS1URH_gBVggL9lkasfOOEFsqhF_UiU2RWJnOc7NjAfHDrZDlffggXEmhUUXwMrX1vmOjs-9RGfOO0DoJSUbSuj2-riB4zBuSlJW-YE3gjxBF1SUvKAN_XqWZ1qSgopGnKPLGI-EsLKsqmfonBNaC8rIBfp5EyPEOIBL2Pd4d1ckX4yHORqtLO7ARZNmnFRrAfc-4GGyyYz5Yga1BxxAexdTmHQy3uF-cqch4nuTDlhhq8IeitYHwFEr5yCcXILqjE8HCGrM5gFUOgUYbZYYt3-OnvbKRnjxcF6hL-_f3e0-FrefP3za3dwWmvM6FbqrqSZto1TLK15DSXrSKcJVKXjTbHsA3mrKtGBc9KxVfdMxoTnbAq06YDW7Qmz1ndyo5ntlrRxD_liYJSVy6Vge5dKxXDqWa8eZer1SY_DfJ4hJDiZqsDk8-ClKRuutqJmoF-mrB-nUDtD9cf_dfxaUq0AHH2OA_v8CvF0hyN38MBBk1Aachs7kfSTZefN3_M0jXFvjln1_g_lf8C-82MWP
Cites_doi 10.1007/s00330-014-3333-4
10.1016/j.phro.2020.03.004
10.1016/j.acra.2019.09.008
10.1148/ryai.2019180011
10.2214/AJR.19.22332
10.1016/j.mri.2012.06.010
10.1088/0031-9155/58/12/4255
10.1007/s00330-019-06183-y
10.1148/radiol.2015132766
10.1007/s00330-019-06170-3
10.1007/BF03178591
10.1148/radiol.14140676
10.1259/bjr.20160406
10.1016/j.ejrad.2020.109349
10.1118/1.4918919
10.1118/1.2219332
10.1002/mp.15180
10.1007/s00330-020-06724-w
10.1002/mp.13299
10.1148/radiol.2421052066
10.2214/AJR.14.13402
10.1002/acm2.12601
10.1148/radiol.2020202317
10.1088/0031-9155/47/17/402
10.1016/j.ejmp.2020.12.005
10.2214/AJR.19.21809
10.1002/acm2.12226
10.1118/1.3246352
10.1002/mp.14319
10.1016/j.ejmp.2020.06.004
ContentType Journal Article
Copyright 2025 Associazione Italiana di Fisica Medica e Sanitaria
Copyright © 2025 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
Copyright_xml – notice: 2025 Associazione Italiana di Fisica Medica e Sanitaria
– notice: Copyright © 2025 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
DBID 6I.
AAFTH
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
ADTOC
UNPAY
DOI 10.1016/j.ejmp.2025.104970
DatabaseName ScienceDirect Open Access Titles
Elsevier:ScienceDirect:Open Access
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
Unpaywall for CDI: Periodical Content
Unpaywall
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList
MEDLINE
MEDLINE - Academic

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
– sequence: 3
  dbid: UNPAY
  name: Unpaywall
  url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/
  sourceTypes: Open Access Repository
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 1724-191X
ExternalDocumentID 10.1016/j.ejmp.2025.104970
40187130
10_1016_j_ejmp_2025_104970
S1120179725000808
Genre Journal Article
GroupedDBID ---
--K
--M
-QF
.1-
.FO
.~1
0R~
123
1B1
1P~
1~.
1~5
3J0
4.4
457
4G.
53G
5VS
7-5
71M
8P~
AAEDT
AAEDW
AAIKJ
AAKOC
AALRI
AAOAW
AAQFI
AATTM
AAXKI
AAXUO
AAYWO
ABBQC
ABFNM
ABFRF
ABJNI
ABMAC
ABMZM
ABNEU
ABXDB
ACDAQ
ACFVG
ACGFS
ACIEU
ACLOT
ACNNM
ACRLP
ACXCU
ADBBV
ADEZE
ADVLN
AEBSH
AEFWE
AEIPS
AEKER
AEVXI
AFJKZ
AFRHN
AFTJW
AFXIZ
AGHFR
AGUBO
AGYEJ
AIEXJ
AIIUN
AIKHN
AITUG
AIVDX
AJRQY
AJUYK
ALMA_UNASSIGNED_HOLDINGS
AMRAJ
ANKPU
ANZVX
APXCP
AXJTR
BKOJK
BLXMC
BNPGV
CS3
DC1
DU5
EBS
EFJIC
EFKBS
EFLBG
EJD
EO8
EO9
EP2
EP3
F5P
FDB
FEDTE
FIRID
FNPLU
FYGXN
GBLVA
HVGLF
HZ~
IHE
J1W
KOM
M41
MO0
N9A
O-L
O9-
OAUVE
OGIMB
OI~
OU0
OZT
P-8
P-9
PC.
Q38
RLW
ROL
RPZ
SDF
SDG
SEL
SES
SJN
SPC
SPCBC
SSH
SSQ
SSZ
T5K
UNMZH
Z5R
~G-
~HD
6I.
AAFTH
AFCTW
AGCQF
AGRNS
~XS
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
ADTOC
UNPAY
ID FETCH-LOGICAL-c448t-cd81c0b9aab4548e20f0da04a274996fee4bc13c7347f3baf9d37c436e15de383
IEDL.DBID .~1
ISSN 1120-1797
1724-191X
IngestDate Tue Aug 19 23:40:08 EDT 2025
Fri Oct 03 00:32:58 EDT 2025
Mon Jul 21 06:07:19 EDT 2025
Wed Oct 01 06:31:20 EDT 2025
Sat Jun 07 17:00:40 EDT 2025
Tue Oct 14 19:40:56 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Large-bore CT
CT number
Dose calculation
Treatment planning
Image reconstruction algorithm
Language English
License This is an open access article under the CC BY license.
Copyright © 2025 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
cc-by
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c448t-cd81c0b9aab4548e20f0da04a274996fee4bc13c7347f3baf9d37c436e15de383
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0002-7823-5884
0009-0005-7102-328X
0000-0003-3596-3010
OpenAccessLink https://www.sciencedirect.com/science/article/pii/S1120179725000808
PMID 40187130
PQID 3186783780
PQPubID 23479
ParticipantIDs unpaywall_primary_10_1016_j_ejmp_2025_104970
proquest_miscellaneous_3186783780
pubmed_primary_40187130
crossref_primary_10_1016_j_ejmp_2025_104970
elsevier_sciencedirect_doi_10_1016_j_ejmp_2025_104970
elsevier_clinicalkey_doi_10_1016_j_ejmp_2025_104970
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2025-05-01
PublicationDateYYYYMMDD 2025-05-01
PublicationDate_xml – month: 05
  year: 2025
  text: 2025-05-01
  day: 01
PublicationDecade 2020
PublicationPlace Italy
PublicationPlace_xml – name: Italy
PublicationTitle Physica medica
PublicationTitleAlternate Phys Med
PublicationYear 2025
Publisher Elsevier Ltd
Publisher_xml – name: Elsevier Ltd
References Kuo, Lin, Lee, Lin, Chiou, Guo (b0050) 2016; 95
Solomon, Lyu, Marin, Samei (b0075) 2020; 47
Guan H, Kim JH. Accuracy of inhomogeneity correction in photon radiotherapy from CT scans with different settings Institute of Physics Publishing Physics In Medicine Accuracy of inhomogeneity correction in photon radiotherapy from CT scans with different settings. vol. 47. 2002.
Coolens, Breen, Purdie, Owrangi, Publicover, Bartolac (b0150) 2009; 36
Patino, Fuentes, Singh, Hahn, Sahani (b0055) 2015; 205
Franck, Zhang, Deak, Zanca (b0120) 2021; 81
Geyer, Schoepf, Meinel, Nance, Bastarrika, Leipsic (b0125) 2015; 276
Singh, Digumarthy, Muse, Kambadakone, Blake, Tabari (b0105) 2020; 214
Wu, Williamson, Sahoo, Nguyen, Ikner, Liu (b0135) 2020; 13
De Marzi, Lesven, Ferrand, Sage, Boulé, Mazal (b0145) 2013; 58
Goodsitt, Chan, Way, Larson, Christodoulou, Kim (b0040) 2006; 33
Young, Kim, Ko, Ko, Flores, McNitt-Gray (b0035) 2015; 42
Shuman, Chan, Busey, Mitsumori, Choi, Koprowicz (b0045) 2014; 273
Cheung, Shugard, Mistry, Pouliot, Chen (b0130) 2019; 46
Davis, Palmer, Nisbet (b0005) 2017; 90
Birnbaum, Hindman, Lee, Babb (b0020) 2007; 242
Kumar, Gu, Basu, Berglund, Eschrich, Schabath (b0030) 2012; 30
Jensen, Martinsen, Tingberg, Aaløkken, Fosse (b0060) 2014; 24
Nakamura, Higaki, Tatsugami, Zhou, Yu, Akino (b0095) 2019; 1
Tatsugami, Higaki, Nakamura, Yu, Zhou, Lu (b0100) 2019; 29
Ebert MA, Lambert J, Greer PB. CT-ED conversion on a GE Lightspeed-RT scanner: influence of scanner settings*. vol. 31. 2008.
Higaki, Nakamura, Zhou, Yu, Nemoto, Tatsugami (b0080) 2020; 27
Nakao, Ozawa, Yamada, Yogo, Hosono, Hayata (b0010) 2018; 19
Akagi, Nakamura, Higaki, Narita, Honda, Awai (b0090) 2020; 133
Brady, Trout, Somasundaram, Anton, Li, Dillman (b0115) 2021; 298
Racine, Becce, Viry, Monnin, Thomsen, Verdun (b0070) 2020; 76
Nakao, Ozawa, Yogo, Miura, Yamada, Hosono (b0015) 2019; 20
Greffier, Dabli, Frandon, Hamard, Belaouni, Akessoul (b0155) 2021; 48
Jensen, Liu, Tamm, Chandler, Sun, Morani (b0110) 2020; 215
Greffier, Hamard, Pereira, Barrau, Pasquier, Beregi (b0065) 2020; 30
Akagi, Nakamura, Higaki, Narita, Honda, Zhou (b0085) 2019; 29
Davis (10.1016/j.ejmp.2025.104970_b0005) 2017; 90
Wu (10.1016/j.ejmp.2025.104970_b0135) 2020; 13
Akagi (10.1016/j.ejmp.2025.104970_b0085) 2019; 29
Nakao (10.1016/j.ejmp.2025.104970_b0010) 2018; 19
Kumar (10.1016/j.ejmp.2025.104970_b0030) 2012; 30
Greffier (10.1016/j.ejmp.2025.104970_b0155) 2021; 48
Nakamura (10.1016/j.ejmp.2025.104970_b0095) 2019; 1
Geyer (10.1016/j.ejmp.2025.104970_b0125) 2015; 276
De Marzi (10.1016/j.ejmp.2025.104970_b0145) 2013; 58
Racine (10.1016/j.ejmp.2025.104970_b0070) 2020; 76
10.1016/j.ejmp.2025.104970_b0140
Young (10.1016/j.ejmp.2025.104970_b0035) 2015; 42
Higaki (10.1016/j.ejmp.2025.104970_b0080) 2020; 27
Shuman (10.1016/j.ejmp.2025.104970_b0045) 2014; 273
Kuo (10.1016/j.ejmp.2025.104970_b0050) 2016; 95
Tatsugami (10.1016/j.ejmp.2025.104970_b0100) 2019; 29
Greffier (10.1016/j.ejmp.2025.104970_b0065) 2020; 30
Cheung (10.1016/j.ejmp.2025.104970_b0130) 2019; 46
Patino (10.1016/j.ejmp.2025.104970_b0055) 2015; 205
10.1016/j.ejmp.2025.104970_b0025
Birnbaum (10.1016/j.ejmp.2025.104970_b0020) 2007; 242
Nakao (10.1016/j.ejmp.2025.104970_b0015) 2019; 20
Solomon (10.1016/j.ejmp.2025.104970_b0075) 2020; 47
Jensen (10.1016/j.ejmp.2025.104970_b0060) 2014; 24
Brady (10.1016/j.ejmp.2025.104970_b0115) 2021; 298
Akagi (10.1016/j.ejmp.2025.104970_b0090) 2020; 133
Coolens (10.1016/j.ejmp.2025.104970_b0150) 2009; 36
Jensen (10.1016/j.ejmp.2025.104970_b0110) 2020; 215
Goodsitt (10.1016/j.ejmp.2025.104970_b0040) 2006; 33
Franck (10.1016/j.ejmp.2025.104970_b0120) 2021; 81
Singh (10.1016/j.ejmp.2025.104970_b0105) 2020; 214
References_xml – volume: 30
  start-page: 1234
  year: 2012
  end-page: 1248
  ident: b0030
  article-title: Radiomics: The process and the challenges
  publication-title: Magn Reson Imaging
– volume: 20
  start-page: 45
  year: 2019
  end-page: 52
  ident: b0015
  article-title: Tolerance levels of mass density for CT number calibration in photon radiation therapy
  publication-title: J Appl Clin Med Phys
– volume: 46
  start-page: 892
  year: 2019
  end-page: 901
  ident: b0130
  article-title: Evaluating the impact of extended field-of-view CT reconstructions on CT values and dosimetric accuracy for radiation therapy
  publication-title: Med Phys
– volume: 76
  start-page: 28
  year: 2020
  end-page: 37
  ident: b0070
  article-title: Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study
  publication-title: Phys Med
– volume: 214
  start-page: 566
  year: 2020
  end-page: 573
  ident: b0105
  article-title: Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT
  publication-title: Am J Roentgenol
– volume: 90
  year: 2017
  ident: b0005
  article-title: Can CT scan protocols used for radiotherapy treatment planning be adjusted to optimize image quality and patient dose? A systematic review
  publication-title: Br J Radiol
– reference: Guan H, Kim JH. Accuracy of inhomogeneity correction in photon radiotherapy from CT scans with different settings Institute of Physics Publishing Physics In Medicine Accuracy of inhomogeneity correction in photon radiotherapy from CT scans with different settings. vol. 47. 2002.
– volume: 19
  start-page: 271
  year: 2018
  end-page: 275
  ident: b0010
  article-title: Tolerance levels of CT number to electron density table for photon beam in radiotherapy treatment planning system
  publication-title: J Appl Clin Med Phys
– volume: 42
  year: 2015
  ident: b0035
  article-title: Variability in CT lung-nodule volumetry: Effects of dose reduction and reconstruction methods
  publication-title: Med Phys
– volume: 30
  start-page: 3951
  year: 2020
  end-page: 3959
  ident: b0065
  article-title: Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study
  publication-title: Eur Radiol
– volume: 133
  year: 2020
  ident: b0090
  article-title: Deep learning reconstruction of equilibrium phase CT images in obese patients
  publication-title: Eur J Radiol
– volume: 29
  start-page: 5322
  year: 2019
  end-page: 5329
  ident: b0100
  article-title: Deep learning–based image restoration algorithm for coronary CT angiography
  publication-title: Eur Radiol
– volume: 205
  start-page: W19
  year: 2015
  end-page: W31
  ident: b0055
  article-title: Iterative reconstruction techniques in abdominopelvic CT: Technical concepts and clinical implementation
  publication-title: Am J Roentgenol
– volume: 47
  start-page: 3961
  year: 2020
  end-page: 3971
  ident: b0075
  article-title: Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm
  publication-title: Med Phys
– volume: 33
  start-page: 3006
  year: 2006
  end-page: 3017
  ident: b0040
  article-title: Accuracy of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners
  publication-title: Med Phys
– volume: 273
  start-page: 793
  year: 2014
  end-page: 800
  ident: b0045
  article-title: Standard and reduced radiation dose liver CT images: Adaptive statistical iterative reconstruction versus model-based iterative reconstruction-comparison of findings and image quality
  publication-title: Radiology
– volume: 81
  start-page: 86
  year: 2021
  end-page: 93
  ident: b0120
  article-title: Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study
  publication-title: Phys Med
– volume: 27
  start-page: 82
  year: 2020
  end-page: 87
  ident: b0080
  article-title: Deep learning reconstruction at CT: phantom study of the image characteristics
  publication-title: Acad Radiol
– volume: 29
  start-page: 6163
  year: 2019
  end-page: 6171
  ident: b0085
  article-title: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
  publication-title: Eur Radiol
– volume: 298
  start-page: 180
  year: 2021
  end-page: 188
  ident: b0115
  article-title: Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction
  publication-title: Radiology
– volume: 276
  start-page: 339
  year: 2015
  end-page: 357
  ident: b0125
  article-title: State of the Art: Iterative CT reconstruction techniques1
  publication-title: Radiology
– volume: 58
  start-page: 4255
  year: 2013
  end-page: 4276
  ident: b0145
  article-title: Calibration of CT Hounsfield units for proton therapy treatment planning: Use of kilovoltage and megavoltage images and comparison of parameterized methods
  publication-title: Phys Med Biol
– volume: 24
  start-page: 2989
  year: 2014
  end-page: 3002
  ident: b0060
  article-title: Comparing five different iterative reconstruction algorithms for computed tomography in an ROC study
  publication-title: Eur Radiol
– volume: 215
  start-page: 50
  year: 2020
  end-page: 57
  ident: b0110
  article-title: Image quality assessment of abdominal CT by use of new deep learning image reconstruction: Initial experience
  publication-title: Am J Roentgenol
– volume: 242
  start-page: 109
  year: 2007
  end-page: 119
  ident: b0020
  article-title: Multi-detector row CT attenuation measurements: Assessment of intra- and interscanner variability with an anthropomorphic body CT phantom
  publication-title: Radiology
– volume: 1
  year: 2019
  ident: b0095
  article-title: Deep learning-based CT image reconstruction: Initial evaluation targeting hypovascular hepatic metastases
  publication-title: Radiol Artif Intell
– volume: 13
  start-page: 44
  year: 2020
  end-page: 49
  ident: b0135
  article-title: Evaluation of the high definition field of view option of a large-bore computed tomography scanner for radiation therapy simulation
  publication-title: Phys Imaging Radiat Oncol
– volume: 95
  year: 2016
  ident: b0050
  article-title: Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography
  publication-title: Medicine (United States)
– reference: Ebert MA, Lambert J, Greer PB. CT-ED conversion on a GE Lightspeed-RT scanner: influence of scanner settings*. vol. 31. 2008.
– volume: 48
  start-page: 5743
  year: 2021
  end-page: 5755
  ident: b0155
  article-title: Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study
  publication-title: Med Phys
– volume: 36
  start-page: 5120
  year: 2009
  end-page: 5127
  ident: b0150
  article-title: Implementation and characterization of a 320-slice volumetric CT scanner for simulation in radiation oncology
  publication-title: Med Phys
– volume: 24
  start-page: 2989
  year: 2014
  ident: 10.1016/j.ejmp.2025.104970_b0060
  article-title: Comparing five different iterative reconstruction algorithms for computed tomography in an ROC study
  publication-title: Eur Radiol
  doi: 10.1007/s00330-014-3333-4
– volume: 13
  start-page: 44
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0135
  article-title: Evaluation of the high definition field of view option of a large-bore computed tomography scanner for radiation therapy simulation
  publication-title: Phys Imaging Radiat Oncol
  doi: 10.1016/j.phro.2020.03.004
– volume: 27
  start-page: 82
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0080
  article-title: Deep learning reconstruction at CT: phantom study of the image characteristics
  publication-title: Acad Radiol
  doi: 10.1016/j.acra.2019.09.008
– volume: 1
  year: 2019
  ident: 10.1016/j.ejmp.2025.104970_b0095
  article-title: Deep learning-based CT image reconstruction: Initial evaluation targeting hypovascular hepatic metastases
  publication-title: Radiol Artif Intell
  doi: 10.1148/ryai.2019180011
– volume: 215
  start-page: 50
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0110
  article-title: Image quality assessment of abdominal CT by use of new deep learning image reconstruction: Initial experience
  publication-title: Am J Roentgenol
  doi: 10.2214/AJR.19.22332
– volume: 30
  start-page: 1234
  year: 2012
  ident: 10.1016/j.ejmp.2025.104970_b0030
  article-title: Radiomics: The process and the challenges
  publication-title: Magn Reson Imaging
  doi: 10.1016/j.mri.2012.06.010
– volume: 58
  start-page: 4255
  year: 2013
  ident: 10.1016/j.ejmp.2025.104970_b0145
  article-title: Calibration of CT Hounsfield units for proton therapy treatment planning: Use of kilovoltage and megavoltage images and comparison of parameterized methods
  publication-title: Phys Med Biol
  doi: 10.1088/0031-9155/58/12/4255
– volume: 29
  start-page: 5322
  year: 2019
  ident: 10.1016/j.ejmp.2025.104970_b0100
  article-title: Deep learning–based image restoration algorithm for coronary CT angiography
  publication-title: Eur Radiol
  doi: 10.1007/s00330-019-06183-y
– volume: 276
  start-page: 339
  year: 2015
  ident: 10.1016/j.ejmp.2025.104970_b0125
  article-title: State of the Art: Iterative CT reconstruction techniques1
  publication-title: Radiology
  doi: 10.1148/radiol.2015132766
– volume: 29
  start-page: 6163
  year: 2019
  ident: 10.1016/j.ejmp.2025.104970_b0085
  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
– ident: 10.1016/j.ejmp.2025.104970_b0025
  doi: 10.1007/BF03178591
– volume: 273
  start-page: 793
  year: 2014
  ident: 10.1016/j.ejmp.2025.104970_b0045
  article-title: Standard and reduced radiation dose liver CT images: Adaptive statistical iterative reconstruction versus model-based iterative reconstruction-comparison of findings and image quality
  publication-title: Radiology
  doi: 10.1148/radiol.14140676
– volume: 90
  year: 2017
  ident: 10.1016/j.ejmp.2025.104970_b0005
  article-title: Can CT scan protocols used for radiotherapy treatment planning be adjusted to optimize image quality and patient dose? A systematic review
  publication-title: Br J Radiol
  doi: 10.1259/bjr.20160406
– volume: 133
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0090
  article-title: Deep learning reconstruction of equilibrium phase CT images in obese patients
  publication-title: Eur J Radiol
  doi: 10.1016/j.ejrad.2020.109349
– volume: 42
  year: 2015
  ident: 10.1016/j.ejmp.2025.104970_b0035
  article-title: Variability in CT lung-nodule volumetry: Effects of dose reduction and reconstruction methods
  publication-title: Med Phys
  doi: 10.1118/1.4918919
– volume: 33
  start-page: 3006
  year: 2006
  ident: 10.1016/j.ejmp.2025.104970_b0040
  article-title: Accuracy of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners
  publication-title: Med Phys
  doi: 10.1118/1.2219332
– volume: 95
  year: 2016
  ident: 10.1016/j.ejmp.2025.104970_b0050
  article-title: Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography
  publication-title: Medicine (United States)
– volume: 48
  start-page: 5743
  year: 2021
  ident: 10.1016/j.ejmp.2025.104970_b0155
  article-title: Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study
  publication-title: Med Phys
  doi: 10.1002/mp.15180
– volume: 30
  start-page: 3951
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0065
  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
– volume: 46
  start-page: 892
  year: 2019
  ident: 10.1016/j.ejmp.2025.104970_b0130
  article-title: Evaluating the impact of extended field-of-view CT reconstructions on CT values and dosimetric accuracy for radiation therapy
  publication-title: Med Phys
  doi: 10.1002/mp.13299
– volume: 242
  start-page: 109
  year: 2007
  ident: 10.1016/j.ejmp.2025.104970_b0020
  article-title: Multi-detector row CT attenuation measurements: Assessment of intra- and interscanner variability with an anthropomorphic body CT phantom
  publication-title: Radiology
  doi: 10.1148/radiol.2421052066
– volume: 205
  start-page: W19
  year: 2015
  ident: 10.1016/j.ejmp.2025.104970_b0055
  article-title: Iterative reconstruction techniques in abdominopelvic CT: Technical concepts and clinical implementation
  publication-title: Am J Roentgenol
  doi: 10.2214/AJR.14.13402
– volume: 20
  start-page: 45
  year: 2019
  ident: 10.1016/j.ejmp.2025.104970_b0015
  article-title: Tolerance levels of mass density for CT number calibration in photon radiation therapy
  publication-title: J Appl Clin Med Phys
  doi: 10.1002/acm2.12601
– volume: 298
  start-page: 180
  year: 2021
  ident: 10.1016/j.ejmp.2025.104970_b0115
  article-title: Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction
  publication-title: Radiology
  doi: 10.1148/radiol.2020202317
– ident: 10.1016/j.ejmp.2025.104970_b0140
  doi: 10.1088/0031-9155/47/17/402
– volume: 81
  start-page: 86
  year: 2021
  ident: 10.1016/j.ejmp.2025.104970_b0120
  article-title: Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study
  publication-title: Phys Med
  doi: 10.1016/j.ejmp.2020.12.005
– volume: 214
  start-page: 566
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0105
  article-title: Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT
  publication-title: Am J Roentgenol
  doi: 10.2214/AJR.19.21809
– volume: 19
  start-page: 271
  year: 2018
  ident: 10.1016/j.ejmp.2025.104970_b0010
  article-title: Tolerance levels of CT number to electron density table for photon beam in radiotherapy treatment planning system
  publication-title: J Appl Clin Med Phys
  doi: 10.1002/acm2.12226
– volume: 36
  start-page: 5120
  year: 2009
  ident: 10.1016/j.ejmp.2025.104970_b0150
  article-title: Implementation and characterization of a 320-slice volumetric CT scanner for simulation in radiation oncology
  publication-title: Med Phys
  doi: 10.1118/1.3246352
– volume: 47
  start-page: 3961
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0075
  article-title: Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm
  publication-title: Med Phys
  doi: 10.1002/mp.14319
– volume: 76
  start-page: 28
  year: 2020
  ident: 10.1016/j.ejmp.2025.104970_b0070
  article-title: Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study
  publication-title: Phys Med
  doi: 10.1016/j.ejmp.2020.06.004
SSID ssj0032255
Score 2.3515785
Snippet •Comprehensive investigation of HU accuracy for a large-bore CT for RT planning.•Marginal impact on CT values with deep learning-based reconstruction.•Minimal...
To evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of...
SourceID unpaywall
proquest
pubmed
crossref
elsevier
SourceType Open Access Repository
Aggregation Database
Index Database
Publisher
StartPage 104970
SubjectTerms CT number
Dose calculation
Humans
Image Processing, Computer-Assisted - methods
Image reconstruction algorithm
Large-bore CT
Phantoms, Imaging
Radiation Dosage
Radiotherapy Planning, Computer-Assisted - instrumentation
Radiotherapy Planning, Computer-Assisted - methods
Tomography, X-Ray Computed - instrumentation
Treatment planning
SummonAdditionalLinks – databaseName: Unpaywall
  dbid: UNPAY
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS-QwFA4ygpcHL3vR0VWysG8a6TTp7XGQFREUHxwYn0KSJqA7dgang-jv8Ad7TpsWdV1XHwtNaHNOku9LzvkOIb-s7TnUOGGhCS0QlFQwrWE-xg4P3UIXBwoP9E_P4uOBOBlGQy-Tg7kwL-7vqzgse32DupJhhNeRWQL0fD6OAHd3yPzg7Lx_WVVPAQ4EnlVVUklCwYCEDH2GzNud_GsX-htlLpPFWTFR93dqNHq28xyt1iWMppVgIQac_DmYlfrAPLySc_zYT62RFQ9Aab_2mHUyZ4svZOHUX7F_JY_9VquTjh09vGDlmE28NWmO8e7lPS0x44oC4KVNRCK9uoGliVYEuxWlpbhtVp5N8cCXKjrCyHMGjmfpFKxa2Nuql1uVX_lcMOi8CX6nE19S6RsZHP2-ODxmvnQDM8D3SmbytGcCnSmlBXAiGwYuyFUgFJBgYFjOWqFNj5uEi8RxrVyW88QIHttelFtgzd9JpxgXdpNQk_EgzfLUcRMLG8RpqIVQkQHcligVJV2y15hSTmqFDtmErl1LHGiJAy3rge4S3lhbNrmnsFpKMNG7raK2lUcmNeL4b7ufjUNJmLZ4F6MKO55NJUchQRTzh3c2ak9rv15goUTAFl2y37reB35t63Ovb5MlfKqDN3-QDniG3QGAVepdP7OeACd2IFg
  priority: 102
  providerName: Unpaywall
Title Assessment of CT-to-physical density table for multiple image reconstruction functions with a large-bore scanner for radiotherapy treatment planning
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1120179725000808
https://dx.doi.org/10.1016/j.ejmp.2025.104970
https://www.ncbi.nlm.nih.gov/pubmed/40187130
https://www.proquest.com/docview/3186783780
https://doi.org/10.1016/j.ejmp.2025.104970
UnpaywallVersion publishedVersion
Volume 133
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVESC
  databaseName: Baden-Württemberg Complete Freedom Collection (Elsevier)
  customDbUrl:
  eissn: 1724-191X
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0032255
  issn: 1120-1797
  databaseCode: GBLVA
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://www.sciencedirect.com
  providerName: Elsevier
– providerCode: PRVESC
  databaseName: Elsevier SD Complete Freedom Collection [SCCMFC]
  customDbUrl:
  eissn: 1724-191X
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0032255
  issn: 1120-1797
  databaseCode: ACRLP
  dateStart: 20201101
  isFulltext: true
  titleUrlDefault: https://www.sciencedirect.com
  providerName: Elsevier
– providerCode: PRVESC
  databaseName: Elsevier SD Freedom Collection Journals [SCFCJ]
  customDbUrl:
  eissn: 1724-191X
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0032255
  issn: 1120-1797
  databaseCode: AIKHN
  dateStart: 20201101
  isFulltext: true
  titleUrlDefault: https://www.sciencedirect.com
  providerName: Elsevier
– providerCode: PRVESC
  databaseName: ScienceDirect (Elsevier)
  customDbUrl:
  eissn: 1724-191X
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0032255
  issn: 1120-1797
  databaseCode: .~1
  dateStart: 20050101
  isFulltext: true
  titleUrlDefault: https://www.sciencedirect.com
  providerName: Elsevier
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS-QwEB_EAz8eDj9O3fuQHPimve026dfjsiirh4sPLuhTSNMEVtZu0crhy_0V9wffTJuWE-UUn0pLUtLOL5OZ5DczAAfGDCzlOPECHRh0UBLhZRnOx8jSpltgI1_Rhv75JBpPxdlVeLUEozYWhmiVTvc3Or3W1u5J3_3Nfjmb9XEeBgSnOAhru4cCfoWIqYrBj98dzYPwGtYFVtBNotYucKbheJmbW8pZGYR01JlSweKXF6fnxuc6rD4UpXr8pebzfxakkw346CxJNmwGuwlLptiClXN3Vr4Nf4Zd0k22sGx06VULr3RiYTkR16tHVlHoFEPLlbXUQja7RR3Dak-5yy7LaP2rIcpo55YpNicKuYcIMuwexVOYu_otdyqfuaAufHnLYmelq430CaYnx5ejsedqMHgaHbfK03ky0H6WKpUJdG5M4Fs_V75Q6M2iq2SNEZkecB1zEVueKZvmPNaCR2YQ5gbd3x1YLhaF2QOmU-4naZ5YriNh_CgJMiFUqNEAi5UK4x4ctj9flk2qDdly0G4kiUqSqGQjqh7wVj6yDSJFtSdxJfhvr7Dr9QRmr_b73kJA4vyjQxVVmMXDveSUEZCy8mOb3QYb3egFVTxEI6EHRx1Y3vBpn985yC-wRncNHfMrLCNEzDc0mapsv54T-_BhePpzPMHrdHIxvP4Lbd4YcQ
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VIlE4oJbnAqVG4gbpZv3I41itqBba7Wkr9WY5ji1ttc1GbSrUC7-CH8xM4kRFIKh6TWLL8Xwez9jfzAB8dG7iKcdJxC136KBkMioKXI-Jp0M37pPY0IH-_CSZncpvZ-psA6Z9LAzRKoPu73R6q63Dk3GYzXG9XI5xHXKCU8pVa_dkD-ChVDwlD2z_x8DzIMCqtsIK-kn0eYic6Uhe7vyCklZyRXedOVUs_vvu9Kf1-QS2rqva3Hw3q9WtHelwG54GU5IddKPdgQ1XPYNH83BZ_hx-HgxZN9nas-kiatZRHeTCSmKuNzesodgphqYr67mFbHmBSoa1rvKQXpbRBthilNHRLTNsRRzyCCHk2BXKp3KXbS-XplyGqC7svKexszoUR3oBp4dfFtNZFIowRBY9tyayZTaxcZEbU0j0bhyPfVyaWBp0Z9FX8s7Jwk6ETYVMvSiMz0uRWikSN1GlQ__3JWxW68q9BmZzEWd5mXlhE-niJOOFlEZZtMBSY1Q6gk_95Ou6y7WhexLauSZRaRKV7kQ1AtHLR_dRpKj3NG4F_2ylhla_4ey_7T70ENC4AOlWxVRufX2lBaUEpLT8-M2rDhvD6CWVPEQrYQSfB7Dc4dfe3HOQe7A1W8yP9fHXk6O38JjedNzMd7CJcHG7aD81xft2ffwC-esYVg
linkToUnpaywall http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS-QwFA4ygpcHL3vR0VWysG8a6TTp7XGQFREUHxwYn0KSJqA7dgang-jv8Ad7TpsWdV1XHwtNaHNOku9LzvkOIb-s7TnUOGGhCS0QlFQwrWE-xg4P3UIXBwoP9E_P4uOBOBlGQy-Tg7kwL-7vqzgse32DupJhhNeRWQL0fD6OAHd3yPzg7Lx_WVVPAQ4EnlVVUklCwYCEDH2GzNud_GsX-htlLpPFWTFR93dqNHq28xyt1iWMppVgIQac_DmYlfrAPLySc_zYT62RFQ9Aab_2mHUyZ4svZOHUX7F_JY_9VquTjh09vGDlmE28NWmO8e7lPS0x44oC4KVNRCK9uoGliVYEuxWlpbhtVp5N8cCXKjrCyHMGjmfpFKxa2Nuql1uVX_lcMOi8CX6nE19S6RsZHP2-ODxmvnQDM8D3SmbytGcCnSmlBXAiGwYuyFUgFJBgYFjOWqFNj5uEi8RxrVyW88QIHttelFtgzd9JpxgXdpNQk_EgzfLUcRMLG8RpqIVQkQHcligVJV2y15hSTmqFDtmErl1LHGiJAy3rge4S3lhbNrmnsFpKMNG7raK2lUcmNeL4b7ufjUNJmLZ4F6MKO55NJUchQRTzh3c2ak9rv15goUTAFl2y37reB35t63Ovb5MlfKqDN3-QDniG3QGAVepdP7OeACd2IFg
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=Assessment+of+CT-to-physical+density+table+for+multiple+image+reconstruction+functions+with+a+large-bore+scanner+for+radiotherapy+treatment+planning&rft.jtitle=Physica+medica&rft.au=Okumura%2C+Takuro&rft.au=Koganezawa%2C+Akito+S.&rft.au=Nakashima%2C+Takeo&rft.au=Ochi%2C+Yusuke&rft.date=2025-05-01&rft.pub=Elsevier+Ltd&rft.issn=1120-1797&rft.volume=133&rft_id=info:doi/10.1016%2Fj.ejmp.2025.104970&rft.externalDocID=S1120179725000808
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1120-1797&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1120-1797&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1120-1797&client=summon