Enhancement in the kV Portal Image Contrast Using Depth Normalization for Accurate Patient Localization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-02

AUTHORS

Hosang Jeon, Hanbean Youn, Jiho Nam, Jayoung Lee, Juhye Lee, Dahl Park, Wontaek Kim, Yongkan Ki, Donghyun Kim, Ho Kyung Kim

ABSTRACT

The X-ray imaging modality has played an important role in the daily verification of a patient’s position for accurate radiation treatment (RT), especially in modern RT techniques that require highly precise patient localization. Although cone-beam computer tomography (CBCT) has already been introduced to the RT field, two-dimensional (2D) X-ray portal imagers have been more widely used than CBCT for the daily check of patient localization owing to the lower patient exposure and time consumption. However, the 2D imager practically provide a lower bone-tissue contrast than CBCT. Thus, we propose a method that enhances the image contrast of daily acquired 2D images by using just one CBCT scan during an entire RT course. This method operates on the basis of the depth normalization (DN) technique and requires no artificial data manipulation such as image post-processing filters. We implemented the algorithm for the portal images of a cylindrical phantom and three patients. From DN results, the image contrast for the phantom and the patients increased by factors of 26.3 and 13.4 on average, and their contrast-to-noise ratios were maintained at differences of 8.6% and 7.7% on average, respectively. Moreover, the DN method provided a stronger contrast enhancement at lower doses that could suppress the imaging exposure and thus improve the patient’s safety. Therefore, the verification of RT patient localization is expected to be performed more efficiently and accurately by using the DN method. More... »

PAGES

539-544

Identifiers

URI

http://scigraph.springernature.com/pub.10.3938/jkps.72.539

DOI

http://dx.doi.org/10.3938/jkps.72.539

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101260131


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/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0299", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Physical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412591.a", 
          "name": [
            "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jeon", 
        "givenName": "Hosang", 
        "id": "sg:person.01372655617.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372655617.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412591.a", 
          "name": [
            "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Youn", 
        "givenName": "Hanbean", 
        "id": "sg:person.016174522476.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016174522476.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412591.a", 
          "name": [
            "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nam", 
        "givenName": "Jiho", 
        "id": "sg:person.01256427217.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01256427217.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412591.a", 
          "name": [
            "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Jayoung", 
        "id": "sg:person.01033106731.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01033106731.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412591.a", 
          "name": [
            "Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Juhye", 
        "id": "sg:person.0750024435.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750024435.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412588.2", 
          "name": [
            "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Park", 
        "givenName": "Dahl", 
        "id": "sg:person.0705240470.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0705240470.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412588.2", 
          "name": [
            "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Wontaek", 
        "id": "sg:person.011323064634.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011323064634.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412588.2", 
          "name": [
            "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ki", 
        "givenName": "Yongkan", 
        "id": "sg:person.0747512570.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0747512570.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.412588.2", 
          "name": [
            "Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Donghyun", 
        "id": "sg:person.01314416170.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01314416170.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Mechanical Engineering and Center for Advanced Medical Engineering Research, Pusan National University, 46241, Busan, Korea", 
          "id": "http://www.grid.ac/institutes/grid.262229.f", 
          "name": [
            "School of Mechanical Engineering and Center for Advanced Medical Engineering Research, Pusan National University, 46241, Busan, Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Ho Kyung", 
        "id": "sg:person.01145251100.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145251100.42"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2018-02", 
    "datePublishedReg": "2018-02-01", 
    "description": "The X-ray imaging modality has played an important role in the daily verification of a patient\u2019s position for accurate radiation treatment (RT), especially in modern RT techniques that require highly precise patient localization. Although cone-beam computer tomography (CBCT) has already been introduced to the RT field, two-dimensional (2D) X-ray portal imagers have been more widely used than CBCT for the daily check of patient localization owing to the lower patient exposure and time consumption. However, the 2D imager practically provide a lower bone-tissue contrast than CBCT. Thus, we propose a method that enhances the image contrast of daily acquired 2D images by using just one CBCT scan during an entire RT course. This method operates on the basis of the depth normalization (DN) technique and requires no artificial data manipulation such as image post-processing filters. We implemented the algorithm for the portal images of a cylindrical phantom and three patients. From DN results, the image contrast for the phantom and the patients increased by factors of 26.3 and 13.4 on average, and their contrast-to-noise ratios were maintained at differences of 8.6% and 7.7% on average, respectively. Moreover, the DN method provided a stronger contrast enhancement at lower doses that could suppress the imaging exposure and thus improve the patient\u2019s safety. Therefore, the verification of RT patient localization is expected to be performed more efficiently and accurately by using the DN method.", 
    "genre": "article", 
    "id": "sg:pub.10.3938/jkps.72.539", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1042000", 
        "issn": [
          "0374-4884", 
          "1976-8524"
        ], 
        "name": "Journal of the Korean Physical Society", 
        "publisher": "Korean Physical Society", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "72"
      }
    ], 
    "keywords": [
      "cone-beam computer tomography", 
      "image contrast", 
      "ray imaging modalities", 
      "patient localization", 
      "accurate radiation treatment", 
      "portal imager", 
      "depth normalization", 
      "lower patient exposure", 
      "cylindrical phantom", 
      "portal images", 
      "imager", 
      "daily checks", 
      "noise ratio", 
      "strong contrast enhancement", 
      "daily verification", 
      "phantom", 
      "imaging modalities", 
      "radiation treatment", 
      "enhancement", 
      "modern RT techniques", 
      "field", 
      "contrast enhancement", 
      "images", 
      "RT course", 
      "RT field", 
      "technique", 
      "low doses", 
      "patient exposure", 
      "RT techniques", 
      "patient position", 
      "CBCT scans", 
      "patient safety", 
      "position", 
      "computer tomography", 
      "patients", 
      "contrast", 
      "tomography", 
      "method", 
      "manipulation", 
      "localization", 
      "verification", 
      "filter", 
      "exposure", 
      "DNS results", 
      "safety", 
      "ratio", 
      "doses", 
      "important role", 
      "daily", 
      "DNS method", 
      "scans", 
      "modalities", 
      "treatment", 
      "post-processing filter", 
      "results", 
      "normalization technique", 
      "course", 
      "normalization", 
      "basis", 
      "factors", 
      "differences", 
      "role", 
      "data manipulation", 
      "check", 
      "consumption", 
      "time consumption", 
      "algorithm", 
      "precise patient localization", 
      "ray portal imagers", 
      "lower bone-tissue contrast", 
      "bone-tissue contrast", 
      "entire RT course", 
      "depth normalization (DN) technique", 
      "artificial data manipulation", 
      "image post-processing filters", 
      "RT patient localization", 
      "kV Portal Image Contrast", 
      "Portal Image Contrast", 
      "Accurate Patient Localization"
    ], 
    "name": "Enhancement in the kV Portal Image Contrast Using Depth Normalization for Accurate Patient Localization", 
    "pagination": "539-544", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1101260131"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.3938/jkps.72.539"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.3938/jkps.72.539", 
      "https://app.dimensions.ai/details/publication/pub.1101260131"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:50", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_784.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.3938/jkps.72.539"
  }
]
 

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.10.3938/jkps.72.539'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.3938/jkps.72.539'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.3938/jkps.72.539'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.3938/jkps.72.539'


 

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

206 TRIPLES      21 PREDICATES      105 URIs      97 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.3938/jkps.72.539 schema:about anzsrc-for:02
2 anzsrc-for:0299
3 schema:author N1a6e232bff114b84b540f62d59319fa4
4 schema:datePublished 2018-02
5 schema:datePublishedReg 2018-02-01
6 schema:description The X-ray imaging modality has played an important role in the daily verification of a patient’s position for accurate radiation treatment (RT), especially in modern RT techniques that require highly precise patient localization. Although cone-beam computer tomography (CBCT) has already been introduced to the RT field, two-dimensional (2D) X-ray portal imagers have been more widely used than CBCT for the daily check of patient localization owing to the lower patient exposure and time consumption. However, the 2D imager practically provide a lower bone-tissue contrast than CBCT. Thus, we propose a method that enhances the image contrast of daily acquired 2D images by using just one CBCT scan during an entire RT course. This method operates on the basis of the depth normalization (DN) technique and requires no artificial data manipulation such as image post-processing filters. We implemented the algorithm for the portal images of a cylindrical phantom and three patients. From DN results, the image contrast for the phantom and the patients increased by factors of 26.3 and 13.4 on average, and their contrast-to-noise ratios were maintained at differences of 8.6% and 7.7% on average, respectively. Moreover, the DN method provided a stronger contrast enhancement at lower doses that could suppress the imaging exposure and thus improve the patient’s safety. Therefore, the verification of RT patient localization is expected to be performed more efficiently and accurately by using the DN method.
7 schema:genre article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N5ad87cc06f534f5f93c89c097a576eff
11 N74b397e8b8e645deaea24e5a2a789f60
12 sg:journal.1042000
13 schema:keywords Accurate Patient Localization
14 CBCT scans
15 DNS method
16 DNS results
17 Portal Image Contrast
18 RT course
19 RT field
20 RT patient localization
21 RT techniques
22 accurate radiation treatment
23 algorithm
24 artificial data manipulation
25 basis
26 bone-tissue contrast
27 check
28 computer tomography
29 cone-beam computer tomography
30 consumption
31 contrast
32 contrast enhancement
33 course
34 cylindrical phantom
35 daily
36 daily checks
37 daily verification
38 data manipulation
39 depth normalization
40 depth normalization (DN) technique
41 differences
42 doses
43 enhancement
44 entire RT course
45 exposure
46 factors
47 field
48 filter
49 image contrast
50 image post-processing filters
51 imager
52 images
53 imaging modalities
54 important role
55 kV Portal Image Contrast
56 localization
57 low doses
58 lower bone-tissue contrast
59 lower patient exposure
60 manipulation
61 method
62 modalities
63 modern RT techniques
64 noise ratio
65 normalization
66 normalization technique
67 patient exposure
68 patient localization
69 patient position
70 patient safety
71 patients
72 phantom
73 portal imager
74 portal images
75 position
76 post-processing filter
77 precise patient localization
78 radiation treatment
79 ratio
80 ray imaging modalities
81 ray portal imagers
82 results
83 role
84 safety
85 scans
86 strong contrast enhancement
87 technique
88 time consumption
89 tomography
90 treatment
91 verification
92 schema:name Enhancement in the kV Portal Image Contrast Using Depth Normalization for Accurate Patient Localization
93 schema:pagination 539-544
94 schema:productId N361da5d5d27e4d39a7556350997e4441
95 Nd4fb4a33405e490b919f7a4feaed44aa
96 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101260131
97 https://doi.org/10.3938/jkps.72.539
98 schema:sdDatePublished 2022-01-01T18:50
99 schema:sdLicense https://scigraph.springernature.com/explorer/license/
100 schema:sdPublisher Nb5c1162ef3d74749ab5c00c91fe117f3
101 schema:url https://doi.org/10.3938/jkps.72.539
102 sgo:license sg:explorer/license/
103 sgo:sdDataset articles
104 rdf:type schema:ScholarlyArticle
105 N0e8b8baa06214c5fbe621986ae3df953 rdf:first sg:person.01033106731.33
106 rdf:rest Ndeb14a27ea2943feb8bc4ba6cea76e3d
107 N1a6e232bff114b84b540f62d59319fa4 rdf:first sg:person.01372655617.76
108 rdf:rest N4702e8db77c54ae1b80aa1fcf4716221
109 N27aee3b5bfa04a4d8002b28559d27f7b rdf:first sg:person.0705240470.44
110 rdf:rest N7e61463778ae4bd999c79ad64f85dba8
111 N361da5d5d27e4d39a7556350997e4441 schema:name dimensions_id
112 schema:value pub.1101260131
113 rdf:type schema:PropertyValue
114 N4702e8db77c54ae1b80aa1fcf4716221 rdf:first sg:person.016174522476.01
115 rdf:rest Nf68f3488091c49779353f40d22e0be04
116 N5ad87cc06f534f5f93c89c097a576eff schema:volumeNumber 72
117 rdf:type schema:PublicationVolume
118 N74b397e8b8e645deaea24e5a2a789f60 schema:issueNumber 4
119 rdf:type schema:PublicationIssue
120 N7e61463778ae4bd999c79ad64f85dba8 rdf:first sg:person.011323064634.63
121 rdf:rest Nb51a1f7aeed24dc28b7e6f28c38072b3
122 N96ea00e2f839453c9933d6c1a0e3cce7 rdf:first sg:person.01145251100.42
123 rdf:rest rdf:nil
124 Nb51a1f7aeed24dc28b7e6f28c38072b3 rdf:first sg:person.0747512570.10
125 rdf:rest Nd7ba63de08f449a6b8d23e5d58865c34
126 Nb5c1162ef3d74749ab5c00c91fe117f3 schema:name Springer Nature - SN SciGraph project
127 rdf:type schema:Organization
128 Nd4fb4a33405e490b919f7a4feaed44aa schema:name doi
129 schema:value 10.3938/jkps.72.539
130 rdf:type schema:PropertyValue
131 Nd7ba63de08f449a6b8d23e5d58865c34 rdf:first sg:person.01314416170.25
132 rdf:rest N96ea00e2f839453c9933d6c1a0e3cce7
133 Ndeb14a27ea2943feb8bc4ba6cea76e3d rdf:first sg:person.0750024435.32
134 rdf:rest N27aee3b5bfa04a4d8002b28559d27f7b
135 Nf68f3488091c49779353f40d22e0be04 rdf:first sg:person.01256427217.26
136 rdf:rest N0e8b8baa06214c5fbe621986ae3df953
137 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
138 schema:name Physical Sciences
139 rdf:type schema:DefinedTerm
140 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
141 schema:name Other Physical Sciences
142 rdf:type schema:DefinedTerm
143 sg:journal.1042000 schema:issn 0374-4884
144 1976-8524
145 schema:name Journal of the Korean Physical Society
146 schema:publisher Korean Physical Society
147 rdf:type schema:Periodical
148 sg:person.01033106731.33 schema:affiliation grid-institutes:grid.412591.a
149 schema:familyName Lee
150 schema:givenName Jayoung
151 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01033106731.33
152 rdf:type schema:Person
153 sg:person.011323064634.63 schema:affiliation grid-institutes:grid.412588.2
154 schema:familyName Kim
155 schema:givenName Wontaek
156 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011323064634.63
157 rdf:type schema:Person
158 sg:person.01145251100.42 schema:affiliation grid-institutes:grid.262229.f
159 schema:familyName Kim
160 schema:givenName Ho Kyung
161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145251100.42
162 rdf:type schema:Person
163 sg:person.01256427217.26 schema:affiliation grid-institutes:grid.412591.a
164 schema:familyName Nam
165 schema:givenName Jiho
166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01256427217.26
167 rdf:type schema:Person
168 sg:person.01314416170.25 schema:affiliation grid-institutes:grid.412588.2
169 schema:familyName Kim
170 schema:givenName Donghyun
171 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01314416170.25
172 rdf:type schema:Person
173 sg:person.01372655617.76 schema:affiliation grid-institutes:grid.412591.a
174 schema:familyName Jeon
175 schema:givenName Hosang
176 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372655617.76
177 rdf:type schema:Person
178 sg:person.016174522476.01 schema:affiliation grid-institutes:grid.412591.a
179 schema:familyName Youn
180 schema:givenName Hanbean
181 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016174522476.01
182 rdf:type schema:Person
183 sg:person.0705240470.44 schema:affiliation grid-institutes:grid.412588.2
184 schema:familyName Park
185 schema:givenName Dahl
186 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0705240470.44
187 rdf:type schema:Person
188 sg:person.0747512570.10 schema:affiliation grid-institutes:grid.412588.2
189 schema:familyName Ki
190 schema:givenName Yongkan
191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0747512570.10
192 rdf:type schema:Person
193 sg:person.0750024435.32 schema:affiliation grid-institutes:grid.412591.a
194 schema:familyName Lee
195 schema:givenName Juhye
196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750024435.32
197 rdf:type schema:Person
198 grid-institutes:grid.262229.f schema:alternateName School of Mechanical Engineering and Center for Advanced Medical Engineering Research, Pusan National University, 46241, Busan, Korea
199 schema:name School of Mechanical Engineering and Center for Advanced Medical Engineering Research, Pusan National University, 46241, Busan, Korea
200 rdf:type schema:Organization
201 grid-institutes:grid.412588.2 schema:alternateName Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea
202 schema:name Department of Radiation Oncology, Pusan National University Hospital, 46241, Busan, Korea
203 rdf:type schema:Organization
204 grid-institutes:grid.412591.a schema:alternateName Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea
205 schema:name Department of Radiation Oncology and Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, 50612, Yangsan, Korea
206 rdf:type schema:Organization
 




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


...