Ontology type: schema:ScholarlyArticle
2015-03
AUTHORSJi Soo Song, Jeong Min Lee, Ji Young Sohn, Jeong-Hee Yoon, Joon Koo Han, Byung Ihn Choi
ABSTRACTPURPOSE: 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... »
PAGES259-267
http://scigraph.springernature.com/pub.10.1007/s11547-014-0441-9
DOIhttp://dx.doi.org/10.1007/s11547-014-0441-9
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1020235718
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/25168773
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
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