Automatic Mapping of CT Scan Locations on Computational Human Phantoms for Organ Dose Estimation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02

AUTHORS

Choonsik Lee, Gleb A. Kuzmin, Jinyong Bae, Jianhua Yao, Elizabeth Mosher, Les R. Folio

ABSTRACT

To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1 cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9 cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies. More... »

PAGES

175-182

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10278-018-0119-2

DOI

http://dx.doi.org/10.1007/s10278-018-0119-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106679637

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30187315


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Institutes of Health", 
          "id": "https://www.grid.ac/institutes/grid.94365.3d", 
          "name": [
            "Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA", 
            "Radiation Epidemiology Branch/DCEG/NCI/NIH, 9609 Medical Center Drive, 20850, Rockville, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Choonsik", 
        "id": "sg:person.07562745017.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07562745017.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institutes of Health", 
          "id": "https://www.grid.ac/institutes/grid.94365.3d", 
          "name": [
            "Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kuzmin", 
        "givenName": "Gleb A.", 
        "id": "sg:person.010732372311.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010732372311.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Kansas City University of Medicine and Biosciences", 
          "id": "https://www.grid.ac/institutes/grid.258405.e", 
          "name": [
            "Kansas City University of Medicine and Bioscience, Kansas City, KS, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bae", 
        "givenName": "Jinyong", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institutes of Health", 
          "id": "https://www.grid.ac/institutes/grid.94365.3d", 
          "name": [
            "Radiology and Imaging Sciences Clinical Center, National Institutes of Health, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yao", 
        "givenName": "Jianhua", 
        "id": "sg:person.012366760067.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012366760067.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institutes of Health", 
          "id": "https://www.grid.ac/institutes/grid.94365.3d", 
          "name": [
            "Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mosher", 
        "givenName": "Elizabeth", 
        "id": "sg:person.011075745365.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011075745365.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institutes of Health", 
          "id": "https://www.grid.ac/institutes/grid.94365.3d", 
          "name": [
            "Radiology and Imaging Sciences Clinical Center, National Institutes of Health, Bethesda, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Folio", 
        "givenName": "Les R.", 
        "id": "sg:person.01023065435.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023065435.03"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1118/1.3544658", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000848869"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/rpd/ncr429", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003417624"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003300050709", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006110305", 
          "https://doi.org/10.1007/s003300050709"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.3693052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009362447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.4883778", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015554538"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.2723885", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019882356"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1118/1.3245881", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031701644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jamapediatrics.2013.311", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033154670"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-015-4157-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035138554", 
          "https://doi.org/10.1007/s00330-015-4157-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.2012.5960", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039374598"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00247-014-3117-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041459291", 
          "https://doi.org/10.1007/s00247-014-3117-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.11111032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041593555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1055/s-2002-35937", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057418506"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/55/2/002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059028373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/55/2/002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059028373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/59/18/r233", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059030459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/60/14/5601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059030876"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/60/14/5601", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059030876"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/61/11/4168", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059031375"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0952-4746/35/4/891", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059112856"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1667/rr3385.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068196497"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.14.14135", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069304157"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3238/arztebl.2016.0721", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079357285"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.16.16979", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084394687"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1667/rr14999.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103154956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1667/rr14999.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103154956"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-02", 
    "datePublishedReg": "2019-02-01", 
    "description": "To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1\u00a0cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9\u00a0cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10278-018-0119-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1100894", 
        "issn": [
          "0897-1889", 
          "1618-727X"
        ], 
        "name": "Journal of Digital Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "32"
      }
    ], 
    "name": "Automatic Mapping of CT Scan Locations on Computational Human Phantoms for Organ Dose Estimation", 
    "pagination": "175-182", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "db2169aad364db349b65f1d1f9526eecaded286ec62f5efaf2e9b684b686685e"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30187315"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9100529"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10278-018-0119-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106679637"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10278-018-0119-2", 
      "https://app.dimensions.ai/details/publication/pub.1106679637"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:23", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000355_0000000355/records_52988_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10278-018-0119-2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10278-018-0119-2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10278-018-0119-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10278-018-0119-2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10278-018-0119-2'


 

This table displays all metadata directly associated to this object as RDF triples.

180 TRIPLES      21 PREDICATES      52 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10278-018-0119-2 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N21c2149d8bc9425294112bb06df8ded6
4 schema:citation sg:pub.10.1007/s00247-014-3117-7
5 sg:pub.10.1007/s00330-015-4157-6
6 sg:pub.10.1007/s003300050709
7 https://doi.org/10.1001/jama.2012.5960
8 https://doi.org/10.1001/jamapediatrics.2013.311
9 https://doi.org/10.1055/s-2002-35937
10 https://doi.org/10.1088/0031-9155/55/2/002
11 https://doi.org/10.1088/0031-9155/59/18/r233
12 https://doi.org/10.1088/0031-9155/60/14/5601
13 https://doi.org/10.1088/0031-9155/61/11/4168
14 https://doi.org/10.1088/0952-4746/35/4/891
15 https://doi.org/10.1093/rpd/ncr429
16 https://doi.org/10.1118/1.2723885
17 https://doi.org/10.1118/1.3245881
18 https://doi.org/10.1118/1.3544658
19 https://doi.org/10.1118/1.3693052
20 https://doi.org/10.1118/1.4883778
21 https://doi.org/10.1148/radiol.11111032
22 https://doi.org/10.1667/rr14999.1
23 https://doi.org/10.1667/rr3385.1
24 https://doi.org/10.2214/ajr.14.14135
25 https://doi.org/10.2214/ajr.16.16979
26 https://doi.org/10.3238/arztebl.2016.0721
27 schema:datePublished 2019-02
28 schema:datePublishedReg 2019-02-01
29 schema:description To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1 cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9 cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies.
30 schema:genre research_article
31 schema:inLanguage en
32 schema:isAccessibleForFree false
33 schema:isPartOf N53c11c09aab940d488c08e0172e6e8a3
34 N629ed0f5de7b4699ba8fea030b90fc29
35 sg:journal.1100894
36 schema:name Automatic Mapping of CT Scan Locations on Computational Human Phantoms for Organ Dose Estimation
37 schema:pagination 175-182
38 schema:productId N6e0304a7b81248de87d67dc6c425c2f0
39 N7f3ecc3533ff4a9c9fc605e1d5f64766
40 N8b8183b8f1fe4c34927837866eba5566
41 Nace555ac6ced452f865cf0409819d0db
42 Ncdda1b3b399f4bd89d8f5cafd777ba13
43 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106679637
44 https://doi.org/10.1007/s10278-018-0119-2
45 schema:sdDatePublished 2019-04-11T11:23
46 schema:sdLicense https://scigraph.springernature.com/explorer/license/
47 schema:sdPublisher N9bc3fc9e859c413eb9688b7591938c02
48 schema:url https://link.springer.com/10.1007%2Fs10278-018-0119-2
49 sgo:license sg:explorer/license/
50 sgo:sdDataset articles
51 rdf:type schema:ScholarlyArticle
52 N089b4db758204638aad9fdc419347c83 rdf:first sg:person.011075745365.15
53 rdf:rest N3610d788c7804ea1878af52076363712
54 N21c2149d8bc9425294112bb06df8ded6 rdf:first sg:person.07562745017.17
55 rdf:rest N4fce74814b8c46acb48b4ce4d978d67c
56 N3610d788c7804ea1878af52076363712 rdf:first sg:person.01023065435.03
57 rdf:rest rdf:nil
58 N48b9707abfa14ed2ae63f103c4a05996 rdf:first N74a58e0972b041dcbdc5edf9f84ac0da
59 rdf:rest Nd20a093feb7f4d20b7a7427a10a100b2
60 N4fce74814b8c46acb48b4ce4d978d67c rdf:first sg:person.010732372311.25
61 rdf:rest N48b9707abfa14ed2ae63f103c4a05996
62 N53c11c09aab940d488c08e0172e6e8a3 schema:issueNumber 1
63 rdf:type schema:PublicationIssue
64 N629ed0f5de7b4699ba8fea030b90fc29 schema:volumeNumber 32
65 rdf:type schema:PublicationVolume
66 N6e0304a7b81248de87d67dc6c425c2f0 schema:name nlm_unique_id
67 schema:value 9100529
68 rdf:type schema:PropertyValue
69 N74a58e0972b041dcbdc5edf9f84ac0da schema:affiliation https://www.grid.ac/institutes/grid.258405.e
70 schema:familyName Bae
71 schema:givenName Jinyong
72 rdf:type schema:Person
73 N7f3ecc3533ff4a9c9fc605e1d5f64766 schema:name doi
74 schema:value 10.1007/s10278-018-0119-2
75 rdf:type schema:PropertyValue
76 N8b8183b8f1fe4c34927837866eba5566 schema:name readcube_id
77 schema:value db2169aad364db349b65f1d1f9526eecaded286ec62f5efaf2e9b684b686685e
78 rdf:type schema:PropertyValue
79 N9bc3fc9e859c413eb9688b7591938c02 schema:name Springer Nature - SN SciGraph project
80 rdf:type schema:Organization
81 Nace555ac6ced452f865cf0409819d0db schema:name dimensions_id
82 schema:value pub.1106679637
83 rdf:type schema:PropertyValue
84 Ncdda1b3b399f4bd89d8f5cafd777ba13 schema:name pubmed_id
85 schema:value 30187315
86 rdf:type schema:PropertyValue
87 Nd20a093feb7f4d20b7a7427a10a100b2 rdf:first sg:person.012366760067.46
88 rdf:rest N089b4db758204638aad9fdc419347c83
89 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
90 schema:name Information and Computing Sciences
91 rdf:type schema:DefinedTerm
92 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
93 schema:name Artificial Intelligence and Image Processing
94 rdf:type schema:DefinedTerm
95 sg:journal.1100894 schema:issn 0897-1889
96 1618-727X
97 schema:name Journal of Digital Imaging
98 rdf:type schema:Periodical
99 sg:person.01023065435.03 schema:affiliation https://www.grid.ac/institutes/grid.94365.3d
100 schema:familyName Folio
101 schema:givenName Les R.
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023065435.03
103 rdf:type schema:Person
104 sg:person.010732372311.25 schema:affiliation https://www.grid.ac/institutes/grid.94365.3d
105 schema:familyName Kuzmin
106 schema:givenName Gleb A.
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010732372311.25
108 rdf:type schema:Person
109 sg:person.011075745365.15 schema:affiliation https://www.grid.ac/institutes/grid.94365.3d
110 schema:familyName Mosher
111 schema:givenName Elizabeth
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011075745365.15
113 rdf:type schema:Person
114 sg:person.012366760067.46 schema:affiliation https://www.grid.ac/institutes/grid.94365.3d
115 schema:familyName Yao
116 schema:givenName Jianhua
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012366760067.46
118 rdf:type schema:Person
119 sg:person.07562745017.17 schema:affiliation https://www.grid.ac/institutes/grid.94365.3d
120 schema:familyName Lee
121 schema:givenName Choonsik
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07562745017.17
123 rdf:type schema:Person
124 sg:pub.10.1007/s00247-014-3117-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041459291
125 https://doi.org/10.1007/s00247-014-3117-7
126 rdf:type schema:CreativeWork
127 sg:pub.10.1007/s00330-015-4157-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035138554
128 https://doi.org/10.1007/s00330-015-4157-6
129 rdf:type schema:CreativeWork
130 sg:pub.10.1007/s003300050709 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006110305
131 https://doi.org/10.1007/s003300050709
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1001/jama.2012.5960 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039374598
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1001/jamapediatrics.2013.311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033154670
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1055/s-2002-35937 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057418506
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1088/0031-9155/55/2/002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059028373
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1088/0031-9155/59/18/r233 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059030459
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1088/0031-9155/60/14/5601 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059030876
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1088/0031-9155/61/11/4168 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059031375
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1088/0952-4746/35/4/891 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059112856
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1093/rpd/ncr429 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003417624
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1118/1.2723885 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019882356
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1118/1.3245881 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031701644
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1118/1.3544658 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000848869
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1118/1.3693052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009362447
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1118/1.4883778 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015554538
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1148/radiol.11111032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041593555
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1667/rr14999.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103154956
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1667/rr3385.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068196497
166 rdf:type schema:CreativeWork
167 https://doi.org/10.2214/ajr.14.14135 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069304157
168 rdf:type schema:CreativeWork
169 https://doi.org/10.2214/ajr.16.16979 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084394687
170 rdf:type schema:CreativeWork
171 https://doi.org/10.3238/arztebl.2016.0721 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079357285
172 rdf:type schema:CreativeWork
173 https://www.grid.ac/institutes/grid.258405.e schema:alternateName Kansas City University of Medicine and Biosciences
174 schema:name Kansas City University of Medicine and Bioscience, Kansas City, KS, USA
175 rdf:type schema:Organization
176 https://www.grid.ac/institutes/grid.94365.3d schema:alternateName National Institutes of Health
177 schema:name Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
178 Radiation Epidemiology Branch/DCEG/NCI/NIH, 9609 Medical Center Drive, 20850, Rockville, MD, USA
179 Radiology and Imaging Sciences Clinical Center, National Institutes of Health, Bethesda, MD, USA
180 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...