Hybrid iterative reconstruction technique for liver CT scans for image noise reduction and image quality improvement: evaluation of the optimal ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-03

AUTHORS

Ji Soo Song, Jeong Min Lee, Ji Young Sohn, Jeong-Hee Yoon, Joon Koo Han, Byung Ihn Choi

ABSTRACT

PURPOSE: This study sought to investigate the effect of the hybrid iterative reconstruction (IR) algorithm (iDose, Philips Healthcare) on the improvement of image quality of computed tomography (CT) scans of the liver and determine the appropriate level of IR strength for clinical use. MATERIALS AND METHODS: A total of 75 patients (41 men and 34 women; mean age, 59.5 years) with a primary abdominal malignancy who underwent two-phase liver CT scans for the work-up of their liver metastases, were included in this study. The CT images during the portal phase were reconstructed using either filtered back projection (FBP) or the hybrid IR algorithm with six different levels of IR strengths. The signal-to-noise ratio of the liver (SNR(liver)) and the contrast-to-noise ratio of the portal vein to muscle (CNR(pv to m)) were measured. For qualitative analysis, image noise, visibility of small intrahepatic vascular structures, beam-hardening artefact, lesion conspicuity, and overall image quality were graded by two radiologists. RESULTS: Quantitative analysis demonstrated that image noise was significantly reduced along with the increasing level of iDose and that the values of SNR(liver) and CNR(pv to m) were significantly better with iDose than those of FBP images. Qualitative assessment also showed significantly better results with iDose compared with FBP (p < 0.05) and the parameters for subjective image quality were highest with iDose level 4. CONCLUSIONS: The hybrid IR technique is able to reduce image noise and to provide better image quality than FBP, and an intermediate strength of iDose (level 4) provided the highest quality images. More... »

PAGES

259-267

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11547-014-0441-9

DOI

http://dx.doi.org/10.1007/s11547-014-0441-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020235718

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25168773


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/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged, 80 and over", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Hemangioma", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Liver Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Predictive Value of Tests", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Prospective Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiation Dosage", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiographic Image Enhancement", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiographic Image Interpretation, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Signal-To-Noise Ratio", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Chonbuk National University", 
          "id": "https://www.grid.ac/institutes/grid.411545.0", 
          "name": [
            "Department of Radiology, Research Institute of Radiation Medicine, Chonbuk National University Medical School and Hospital, 634-18 Keumam-Dong, Deokjin-gu, 561-712, Chonju, Chonbuk, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Song", 
        "givenName": "Ji Soo", 
        "id": "sg:person.01322174344.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322174344.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Jeong Min", 
        "id": "sg:person.01266602714.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01266602714.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sohn", 
        "givenName": "Ji Young", 
        "id": "sg:person.0774253372.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774253372.96"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yoon", 
        "givenName": "Jeong-Hee", 
        "id": "sg:person.01020423064.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01020423064.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Han", 
        "givenName": "Joon Koo", 
        "id": "sg:person.0647723014.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0647723014.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Choi", 
        "givenName": "Byung Ihn", 
        "id": "sg:person.0716036214.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0716036214.08"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1056/nejmra072149", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001923893"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-010-1990-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001978590", 
          "https://doi.org/10.1007/s00330-010-1990-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-010-1991-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008525729", 
          "https://doi.org/10.1007/s00330-010-1991-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.09090094", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011314087"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejmp.2011.12.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015415271"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-011-2361-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020465888", 
          "https://doi.org/10.1007/s00330-011-2361-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e31821fee94", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020697584"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e31821fee94", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020697584"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/rct.0b013e31821fee94", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020697584"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2521081554", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027195707"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2221010190", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030831317"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejmp.2011.03.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037899712"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2373041655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039803118"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.acra.2009.02.021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043011293"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-011-2056-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043799075", 
          "https://doi.org/10.1007/s00330-011-2056-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/archinternmed.2009.440", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047591515"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiol.2243011188", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050342838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-011-2271-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051991976", 
          "https://doi.org/10.1007/s00330-011-2271-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/52/14/003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059026652"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.09.2397", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069300081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.09.2953", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069300281"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.09.2989", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069300298"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.10.4285", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069300818"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.11.6907", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069301801"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-03", 
    "datePublishedReg": "2015-03-01", 
    "description": "PURPOSE: This study sought to investigate the effect of the hybrid iterative reconstruction (IR) algorithm (iDose, Philips Healthcare) on the improvement of image quality of computed tomography (CT) scans of the liver and determine the appropriate level of IR strength for clinical use.\nMATERIALS AND METHODS: A total of 75 patients (41 men and 34 women; mean age, 59.5 years) with a primary abdominal malignancy who underwent two-phase liver CT scans for the work-up of their liver metastases, were included in this study. The CT images during the portal phase were reconstructed using either filtered back projection (FBP) or the hybrid IR algorithm with six different levels of IR strengths. The signal-to-noise ratio of the liver (SNR(liver)) and the contrast-to-noise ratio of the portal vein to muscle (CNR(pv to m)) were measured. For qualitative analysis, image noise, visibility of small intrahepatic vascular structures, beam-hardening artefact, lesion conspicuity, and overall image quality were graded by two radiologists.\nRESULTS: Quantitative analysis demonstrated that image noise was significantly reduced along with the increasing level of iDose and that the values of SNR(liver) and CNR(pv to m) were significantly better with iDose than those of FBP images. Qualitative assessment also showed significantly better results with iDose compared with FBP (p < 0.05) and the parameters for subjective image quality were highest with iDose level 4.\nCONCLUSIONS: The hybrid IR technique is able to reduce image noise and to provide better image quality than FBP, and an intermediate strength of iDose (level 4) provided the highest quality images.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11547-014-0441-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1357346", 
        "issn": [
          "0026-4962", 
          "1826-6983"
        ], 
        "name": "La radiologia medica", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "120"
      }
    ], 
    "name": "Hybrid iterative reconstruction technique for liver CT scans for image noise reduction and image quality improvement: evaluation of the optimal iterative reconstruction strengths", 
    "pagination": "259-267", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "606adad983a61c333b3a561262090395372233bd68efacac0fe4aa173f9038d5"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "25168773"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0177625"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11547-014-0441-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1020235718"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11547-014-0441-9", 
      "https://app.dimensions.ai/details/publication/pub.1020235718"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T17:34", 
    "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/v90/0000000001_0000000264/records_8672_00000521.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11547-014-0441-9"
  }
]
 

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/s11547-014-0441-9'

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/s11547-014-0441-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11547-014-0441-9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11547-014-0441-9'


 

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

257 TRIPLES      21 PREDICATES      69 URIs      39 LITERALS      27 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11547-014-0441-9 schema:about N06a8fd2e76844d62938d03a31a8620f6
2 N0f29b4e2c3174729ac58813ed91d295f
3 N1b0e93f1f1de44e896f071e31cb5700c
4 N1c71b4f3e98d48c2935e78595a05c73d
5 N21fae8aee08f42b58f9ab373f17e8dec
6 N22036e2a85bc4906b81a9cdfeb1e00da
7 N2b1b3f6e5a8a4c299662b0acd90c7fda
8 N47a77ba6b0484bce85041c81d3085f08
9 N58f8e95894d548bdbc3303a17c6fd873
10 N6c1a4472380f47d69cca84f46f567249
11 N88fa08b85b1c44529e48103c8ba3a90d
12 N98ceb67598b14dd1a713e1de1bb04fdf
13 N9b4b2b83cae5486d9a61a723b7afdff9
14 N9d41a373414b438c9e21bf15db833a1e
15 Na97029d26b4742379ef67104d50c941e
16 Ncece1e6de72942e28a51f485cdc4baa3
17 Nd100357588fa49d6b33cf47050df5437
18 Nd83c3e1cc06944bcbdd628d64156d8ac
19 anzsrc-for:11
20 anzsrc-for:1103
21 schema:author N4e4a087fd12b4e8882fafc281cd4f2c8
22 schema:citation sg:pub.10.1007/s00330-010-1990-5
23 sg:pub.10.1007/s00330-010-1991-4
24 sg:pub.10.1007/s00330-011-2056-z
25 sg:pub.10.1007/s00330-011-2271-7
26 sg:pub.10.1007/s00330-011-2361-6
27 https://doi.org/10.1001/archinternmed.2009.440
28 https://doi.org/10.1016/j.acra.2009.02.021
29 https://doi.org/10.1016/j.ejmp.2011.03.003
30 https://doi.org/10.1016/j.ejmp.2011.12.004
31 https://doi.org/10.1056/nejmra072149
32 https://doi.org/10.1088/0031-9155/52/14/003
33 https://doi.org/10.1097/rct.0b013e31821fee94
34 https://doi.org/10.1148/radiol.09090094
35 https://doi.org/10.1148/radiol.2221010190
36 https://doi.org/10.1148/radiol.2243011188
37 https://doi.org/10.1148/radiol.2373041655
38 https://doi.org/10.1148/radiol.2521081554
39 https://doi.org/10.2214/ajr.09.2397
40 https://doi.org/10.2214/ajr.09.2953
41 https://doi.org/10.2214/ajr.09.2989
42 https://doi.org/10.2214/ajr.10.4285
43 https://doi.org/10.2214/ajr.11.6907
44 schema:datePublished 2015-03
45 schema:datePublishedReg 2015-03-01
46 schema:description PURPOSE: This study sought to investigate the effect of the hybrid iterative reconstruction (IR) algorithm (iDose, Philips Healthcare) on the improvement of image quality of computed tomography (CT) scans of the liver and determine the appropriate level of IR strength for clinical use. MATERIALS AND METHODS: A total of 75 patients (41 men and 34 women; mean age, 59.5 years) with a primary abdominal malignancy who underwent two-phase liver CT scans for the work-up of their liver metastases, were included in this study. The CT images during the portal phase were reconstructed using either filtered back projection (FBP) or the hybrid IR algorithm with six different levels of IR strengths. The signal-to-noise ratio of the liver (SNR(liver)) and the contrast-to-noise ratio of the portal vein to muscle (CNR(pv to m)) were measured. For qualitative analysis, image noise, visibility of small intrahepatic vascular structures, beam-hardening artefact, lesion conspicuity, and overall image quality were graded by two radiologists. RESULTS: Quantitative analysis demonstrated that image noise was significantly reduced along with the increasing level of iDose and that the values of SNR(liver) and CNR(pv to m) were significantly better with iDose than those of FBP images. Qualitative assessment also showed significantly better results with iDose compared with FBP (p < 0.05) and the parameters for subjective image quality were highest with iDose level 4. CONCLUSIONS: The hybrid IR technique is able to reduce image noise and to provide better image quality than FBP, and an intermediate strength of iDose (level 4) provided the highest quality images.
47 schema:genre research_article
48 schema:inLanguage en
49 schema:isAccessibleForFree false
50 schema:isPartOf N468cc506965745c1b45646b7d3544905
51 Nef22c9f2da25473daef7fc5ba76db339
52 sg:journal.1357346
53 schema:name Hybrid iterative reconstruction technique for liver CT scans for image noise reduction and image quality improvement: evaluation of the optimal iterative reconstruction strengths
54 schema:pagination 259-267
55 schema:productId N127cb0a0774742d68a06d98ac38a63ea
56 N159a7ef7ae1c4f69981d36d6089db0a8
57 N3273bf89f9ec4f0494fbda604a67224c
58 N82a81deaf4e74803a321514c43dfde20
59 Nd175dc8ab38e4667a4f5e371d2bca987
60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020235718
61 https://doi.org/10.1007/s11547-014-0441-9
62 schema:sdDatePublished 2019-04-10T17:34
63 schema:sdLicense https://scigraph.springernature.com/explorer/license/
64 schema:sdPublisher Nb744185f6ccf41aa8d54fbf73dc2aff4
65 schema:url http://link.springer.com/10.1007%2Fs11547-014-0441-9
66 sgo:license sg:explorer/license/
67 sgo:sdDataset articles
68 rdf:type schema:ScholarlyArticle
69 N06a8fd2e76844d62938d03a31a8620f6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
70 schema:name Liver Neoplasms
71 rdf:type schema:DefinedTerm
72 N0f29b4e2c3174729ac58813ed91d295f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
73 schema:name Hemangioma
74 rdf:type schema:DefinedTerm
75 N127cb0a0774742d68a06d98ac38a63ea schema:name readcube_id
76 schema:value 606adad983a61c333b3a561262090395372233bd68efacac0fe4aa173f9038d5
77 rdf:type schema:PropertyValue
78 N159a7ef7ae1c4f69981d36d6089db0a8 schema:name doi
79 schema:value 10.1007/s11547-014-0441-9
80 rdf:type schema:PropertyValue
81 N1b0e93f1f1de44e896f071e31cb5700c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
82 schema:name Radiation Dosage
83 rdf:type schema:DefinedTerm
84 N1c71b4f3e98d48c2935e78595a05c73d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Middle Aged
86 rdf:type schema:DefinedTerm
87 N21fae8aee08f42b58f9ab373f17e8dec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
88 schema:name Aged, 80 and over
89 rdf:type schema:DefinedTerm
90 N22036e2a85bc4906b81a9cdfeb1e00da schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
91 schema:name Male
92 rdf:type schema:DefinedTerm
93 N262a20de1aa84f7599be6f46e209b067 rdf:first sg:person.0647723014.95
94 rdf:rest N75e8519fccf1403eb118912d467b7202
95 N2b1b3f6e5a8a4c299662b0acd90c7fda schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Radiographic Image Enhancement
97 rdf:type schema:DefinedTerm
98 N3273bf89f9ec4f0494fbda604a67224c schema:name nlm_unique_id
99 schema:value 0177625
100 rdf:type schema:PropertyValue
101 N468cc506965745c1b45646b7d3544905 schema:issueNumber 3
102 rdf:type schema:PublicationIssue
103 N46fb892600ec472e8512acbc9e88d0c3 schema:name Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea
104 rdf:type schema:Organization
105 N47a77ba6b0484bce85041c81d3085f08 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
106 schema:name Predictive Value of Tests
107 rdf:type schema:DefinedTerm
108 N4e4a087fd12b4e8882fafc281cd4f2c8 rdf:first sg:person.01322174344.03
109 rdf:rest N7a39c58f4e524793addc942eb1dcdc5d
110 N58f8e95894d548bdbc3303a17c6fd873 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Radiographic Image Interpretation, Computer-Assisted
112 rdf:type schema:DefinedTerm
113 N6c1a4472380f47d69cca84f46f567249 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Prospective Studies
115 rdf:type schema:DefinedTerm
116 N75e8519fccf1403eb118912d467b7202 rdf:first sg:person.0716036214.08
117 rdf:rest rdf:nil
118 N7a158397c0dc4735bafa77f694bf6c8e schema:name Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea
119 rdf:type schema:Organization
120 N7a39c58f4e524793addc942eb1dcdc5d rdf:first sg:person.01266602714.81
121 rdf:rest N88058caf7aac4a1ea2fb1a54a2407445
122 N7a94aecb676a4001ae08d706e06d9654 rdf:first sg:person.01020423064.40
123 rdf:rest N262a20de1aa84f7599be6f46e209b067
124 N82a81deaf4e74803a321514c43dfde20 schema:name dimensions_id
125 schema:value pub.1020235718
126 rdf:type schema:PropertyValue
127 N879c2f989d724b09840fd6b79187cccd schema:name Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea
128 rdf:type schema:Organization
129 N88058caf7aac4a1ea2fb1a54a2407445 rdf:first sg:person.0774253372.96
130 rdf:rest N7a94aecb676a4001ae08d706e06d9654
131 N88fa08b85b1c44529e48103c8ba3a90d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Reproducibility of Results
133 rdf:type schema:DefinedTerm
134 N98ceb67598b14dd1a713e1de1bb04fdf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Adult
136 rdf:type schema:DefinedTerm
137 N9b4b2b83cae5486d9a61a723b7afdff9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
138 schema:name Female
139 rdf:type schema:DefinedTerm
140 N9d41a373414b438c9e21bf15db833a1e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
141 schema:name Algorithms
142 rdf:type schema:DefinedTerm
143 Na97029d26b4742379ef67104d50c941e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Humans
145 rdf:type schema:DefinedTerm
146 Nb744185f6ccf41aa8d54fbf73dc2aff4 schema:name Springer Nature - SN SciGraph project
147 rdf:type schema:Organization
148 Nbb9a6167384a4b41a525b9b4b196a41f schema:name Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea
149 rdf:type schema:Organization
150 Ncece1e6de72942e28a51f485cdc4baa3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Tomography, X-Ray Computed
152 rdf:type schema:DefinedTerm
153 Nd100357588fa49d6b33cf47050df5437 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
154 schema:name Signal-To-Noise Ratio
155 rdf:type schema:DefinedTerm
156 Nd175dc8ab38e4667a4f5e371d2bca987 schema:name pubmed_id
157 schema:value 25168773
158 rdf:type schema:PropertyValue
159 Nd83c3e1cc06944bcbdd628d64156d8ac schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
160 schema:name Aged
161 rdf:type schema:DefinedTerm
162 Nef22c9f2da25473daef7fc5ba76db339 schema:volumeNumber 120
163 rdf:type schema:PublicationVolume
164 Nfba6195db28a4b17a17d7c0f8f3e5870 schema:name Department of Radiology, Research Institute of Radiation Medicine, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, 110-744, Seoul, South Korea
165 rdf:type schema:Organization
166 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
167 schema:name Medical and Health Sciences
168 rdf:type schema:DefinedTerm
169 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
170 schema:name Clinical Sciences
171 rdf:type schema:DefinedTerm
172 sg:journal.1357346 schema:issn 0026-4962
173 1826-6983
174 schema:name La radiologia medica
175 rdf:type schema:Periodical
176 sg:person.01020423064.40 schema:affiliation N879c2f989d724b09840fd6b79187cccd
177 schema:familyName Yoon
178 schema:givenName Jeong-Hee
179 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01020423064.40
180 rdf:type schema:Person
181 sg:person.01266602714.81 schema:affiliation N46fb892600ec472e8512acbc9e88d0c3
182 schema:familyName Lee
183 schema:givenName Jeong Min
184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01266602714.81
185 rdf:type schema:Person
186 sg:person.01322174344.03 schema:affiliation https://www.grid.ac/institutes/grid.411545.0
187 schema:familyName Song
188 schema:givenName Ji Soo
189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322174344.03
190 rdf:type schema:Person
191 sg:person.0647723014.95 schema:affiliation N7a158397c0dc4735bafa77f694bf6c8e
192 schema:familyName Han
193 schema:givenName Joon Koo
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0647723014.95
195 rdf:type schema:Person
196 sg:person.0716036214.08 schema:affiliation Nfba6195db28a4b17a17d7c0f8f3e5870
197 schema:familyName Choi
198 schema:givenName Byung Ihn
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0716036214.08
200 rdf:type schema:Person
201 sg:person.0774253372.96 schema:affiliation Nbb9a6167384a4b41a525b9b4b196a41f
202 schema:familyName Sohn
203 schema:givenName Ji Young
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774253372.96
205 rdf:type schema:Person
206 sg:pub.10.1007/s00330-010-1990-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001978590
207 https://doi.org/10.1007/s00330-010-1990-5
208 rdf:type schema:CreativeWork
209 sg:pub.10.1007/s00330-010-1991-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008525729
210 https://doi.org/10.1007/s00330-010-1991-4
211 rdf:type schema:CreativeWork
212 sg:pub.10.1007/s00330-011-2056-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1043799075
213 https://doi.org/10.1007/s00330-011-2056-z
214 rdf:type schema:CreativeWork
215 sg:pub.10.1007/s00330-011-2271-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051991976
216 https://doi.org/10.1007/s00330-011-2271-7
217 rdf:type schema:CreativeWork
218 sg:pub.10.1007/s00330-011-2361-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020465888
219 https://doi.org/10.1007/s00330-011-2361-6
220 rdf:type schema:CreativeWork
221 https://doi.org/10.1001/archinternmed.2009.440 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047591515
222 rdf:type schema:CreativeWork
223 https://doi.org/10.1016/j.acra.2009.02.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043011293
224 rdf:type schema:CreativeWork
225 https://doi.org/10.1016/j.ejmp.2011.03.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037899712
226 rdf:type schema:CreativeWork
227 https://doi.org/10.1016/j.ejmp.2011.12.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015415271
228 rdf:type schema:CreativeWork
229 https://doi.org/10.1056/nejmra072149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001923893
230 rdf:type schema:CreativeWork
231 https://doi.org/10.1088/0031-9155/52/14/003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059026652
232 rdf:type schema:CreativeWork
233 https://doi.org/10.1097/rct.0b013e31821fee94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020697584
234 rdf:type schema:CreativeWork
235 https://doi.org/10.1148/radiol.09090094 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011314087
236 rdf:type schema:CreativeWork
237 https://doi.org/10.1148/radiol.2221010190 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030831317
238 rdf:type schema:CreativeWork
239 https://doi.org/10.1148/radiol.2243011188 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050342838
240 rdf:type schema:CreativeWork
241 https://doi.org/10.1148/radiol.2373041655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039803118
242 rdf:type schema:CreativeWork
243 https://doi.org/10.1148/radiol.2521081554 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027195707
244 rdf:type schema:CreativeWork
245 https://doi.org/10.2214/ajr.09.2397 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069300081
246 rdf:type schema:CreativeWork
247 https://doi.org/10.2214/ajr.09.2953 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069300281
248 rdf:type schema:CreativeWork
249 https://doi.org/10.2214/ajr.09.2989 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069300298
250 rdf:type schema:CreativeWork
251 https://doi.org/10.2214/ajr.10.4285 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069300818
252 rdf:type schema:CreativeWork
253 https://doi.org/10.2214/ajr.11.6907 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069301801
254 rdf:type schema:CreativeWork
255 https://www.grid.ac/institutes/grid.411545.0 schema:alternateName Chonbuk National University
256 schema:name Department of Radiology, Research Institute of Radiation Medicine, Chonbuk National University Medical School and Hospital, 634-18 Keumam-Dong, Deokjin-gu, 561-712, Chonju, Chonbuk, South Korea
257 rdf:type schema:Organization
 




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


...