Ontology type: schema:ScholarlyArticle
2019-03-19
AUTHORSLucian Beer, Michael Toepker, Ahmed Ba-Ssalamah, Christian Schestak, Anja Dutschke, Martin Schindl, Alexander Wressnegger, Helmut Ringl, Paul Apfaltrer
ABSTRACTOBJECTIVES: The aim of this study was to assess the objective and subjective image characteristics of monoenergetic images (MEI[+]), using a noise-optimized algorithm at different kiloelectron volts (keV) compared to polyenergetic images (PEI), in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: This retrospective, institutional review board-approved study included 45 patients (18 male, 27 female; mean age 66 years; range, 42-96 years) with PDAC who had undergone a dual-energy CT (DECT) of the abdomen for staging. One standard polyenergetic image (PEI) and five MEI(+) images in 10-keV intervals, ranging from 40 to 80 keV, were reconstructed. Line-density profile analysis, as well as the contrast-to-noise ratio (CNR) of the tumor, the signal-to-noise ratio (SNR) of the regular pancreas parenchyma and the tumor, and the CNR of the three main peripancreatic vessels, was calculated. For subjective quality assessment, two readers independently assessed the images using a 5-point Likert scale. Reader reliability was evaluated using an intraclass correlation coefficient. RESULTS: Line-density profile analysis revealed the largest gradient in attenuation between PDAC and regular tissue in MEI(+) at 40 keV. Low-keV MEI(+)reconstructions at 40 and 50 keV increased CNR and SNR compared to PEI (40 keV: CNR 46.8 vs. 7.5; SNRPankreas 32.5 vs. 15.7; SNRLesion 13.5 vs. 8.6; p < 0.001). MEI(+) at 40 keV and 50 keV were consistently preferred by the observers (p < 0.05), showing a high intra-observer 0.937 (0.92-0.95) and inter-observer 0.911 (0.89-0.93) agreement. CONCLUSION: MEI(+) reconstructions at 40 keV and 50 keV provide better objective and subjective image quality compared to conventional PEI of DECT in patients with PDAC. KEY POINTS: • Low-keV MEI(+) reconstructions at 40 and 50 keV increase tumor-to-pancreas contrast compared to PEI. • Low-keV MEI(+) reconstructions improve objective and subjective image quality parameters compared to PEI. • Dual-energy post-processing might be a valuable tool in the diagnostic workup of patients with PDAC. More... »
PAGES1-9
http://scigraph.springernature.com/pub.10.1007/s00330-019-06116-9
DOIhttp://dx.doi.org/10.1007/s00330-019-06116-9
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1112860946
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30888484
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"
}
],
"author": [
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Beer",
"givenName": "Lucian",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Toepker",
"givenName": "Michael",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Ba-Ssalamah",
"givenName": "Ahmed",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Schestak",
"givenName": "Christian",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Dutschke",
"givenName": "Anja",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Surgery, Medical University of Vienna, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Schindl",
"givenName": "Martin",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Wressnegger",
"givenName": "Alexander",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Medical University of Vienna",
"id": "https://www.grid.ac/institutes/grid.22937.3d",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Ringl",
"givenName": "Helmut",
"type": "Person"
},
{
"affiliation": {
"alternateName": "University Medical Centre Mannheim",
"id": "https://www.grid.ac/institutes/grid.411778.c",
"name": [
"Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria",
"Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany"
],
"type": "Organization"
},
"familyName": "Apfaltrer",
"givenName": "Paul",
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s00330-016-4708-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002739230",
"https://doi.org/10.1007/s00330-016-4708-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-016-4708-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002739230",
"https://doi.org/10.1007/s00330-016-4708-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ejrad.2013.04.040",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003796211"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1148/radiol.11101189",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006236867"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ejrad.2015.07.020",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010225054"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijrobp.2010.04.058",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013279770"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-015-3970-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026304837",
"https://doi.org/10.1007/s00330-015-3970-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-014-0274-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029968972",
"https://doi.org/10.1007/s00261-014-0274-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-015-3997-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034203331",
"https://doi.org/10.1007/s00330-015-3997-4"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/rct.0000000000000492",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040562783"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/rct.0000000000000492",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040562783"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/rli.0000000000000060",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049691233"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/rli.0000000000000060",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049691233"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.oraloncology.2013.12.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049714670"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/rli.0b013e31818c3d4b",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051071380"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1097/rli.0b013e31818c3d4b",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051071380"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1148/radiol.2015151560",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1079219457"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-017-1151-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084906944",
"https://doi.org/10.1007/s00261-017-1151-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-017-1151-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084906944",
"https://doi.org/10.1007/s00261-017-1151-2"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ejrad.2017.08.035",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091409133"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1259/bjr.20170411",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091932051"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-017-1390-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092689580",
"https://doi.org/10.1007/s00261-017-1390-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-017-1390-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092689580",
"https://doi.org/10.1007/s00261-017-1390-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-017-5258-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100481784",
"https://doi.org/10.1007/s00330-017-5258-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-018-5313-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101110900",
"https://doi.org/10.1007/s00330-018-5313-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-018-5313-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101110900",
"https://doi.org/10.1007/s00330-018-5313-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-018-5338-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101110902",
"https://doi.org/10.1007/s00330-018-5338-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-018-5338-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1101110902",
"https://doi.org/10.1007/s00330-018-5338-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-018-5407-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1103247651",
"https://doi.org/10.1007/s00330-018-5407-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-018-5762-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1107711873",
"https://doi.org/10.1007/s00330-018-5762-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00330-018-5789-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1107923978",
"https://doi.org/10.1007/s00330-018-5789-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-018-1808-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1109808999",
"https://doi.org/10.1007/s00261-018-1808-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-018-1808-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1109808999",
"https://doi.org/10.1007/s00261-018-1808-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-018-1808-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1109808999",
"https://doi.org/10.1007/s00261-018-1808-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00261-018-1808-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1109808999",
"https://doi.org/10.1007/s00261-018-1808-5"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-03-19",
"datePublishedReg": "2019-03-19",
"description": "OBJECTIVES: The aim of this study was to assess the objective and subjective image characteristics of monoenergetic images (MEI[+]), using a noise-optimized algorithm at different kiloelectron volts (keV) compared to polyenergetic images (PEI), in patients with pancreatic ductal adenocarcinoma (PDAC).\nMETHODS: This retrospective, institutional review board-approved study included 45 patients (18 male, 27 female; mean age 66\u00a0years; range, 42-96\u00a0years) with PDAC who had undergone a dual-energy CT (DECT) of the abdomen for staging. One standard polyenergetic image (PEI) and five MEI(+) images in 10-keV intervals, ranging from 40 to 80\u00a0keV, were reconstructed. Line-density profile analysis, as well as the contrast-to-noise ratio (CNR) of the tumor, the signal-to-noise ratio (SNR) of the regular pancreas parenchyma and the tumor, and the CNR of the three main peripancreatic vessels, was calculated. For subjective quality assessment, two readers independently assessed the images using a 5-point Likert scale. Reader reliability was evaluated using an intraclass correlation coefficient.\nRESULTS: Line-density profile analysis revealed the largest gradient in attenuation between PDAC and regular tissue in MEI(+) at 40\u00a0keV. Low-keV MEI(+)reconstructions at 40 and 50\u00a0keV increased CNR and SNR compared to PEI (40\u00a0keV: CNR 46.8 vs. 7.5; SNRPankreas 32.5 vs. 15.7; SNRLesion 13.5 vs. 8.6; p\u2009<\u20090.001). MEI(+) at 40\u00a0keV and 50\u00a0keV were consistently preferred by the observers (p\u2009<\u20090.05), showing a high intra-observer 0.937 (0.92-0.95) and inter-observer 0.911 (0.89-0.93) agreement.\nCONCLUSION: MEI(+) reconstructions at 40\u00a0keV and 50\u00a0keV provide better objective and subjective image quality compared to conventional PEI of DECT in patients with PDAC.\nKEY POINTS: \u2022 Low-keV MEI(+) reconstructions at 40 and 50\u00a0keV increase tumor-to-pancreas contrast compared to PEI. \u2022 Low-keV MEI(+) reconstructions improve objective and subjective image quality parameters compared to PEI. \u2022 Dual-energy post-processing might be a valuable tool in the diagnostic workup of patients with PDAC.",
"genre": "research_article",
"id": "sg:pub.10.1007/s00330-019-06116-9",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1289120",
"issn": [
"0938-7994",
"1432-1084"
],
"name": "European Radiology",
"type": "Periodical"
}
],
"name": "Objective and subjective comparison of virtual monoenergetic vs. polychromatic images in patients with pancreatic ductal adenocarcinoma",
"pagination": "1-9",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"172bab3adf0d6d526379415fa118b9a7f5ae9a85978f8377dadab09ef8b7e740"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"30888484"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"9114774"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00330-019-06116-9"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1112860946"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00330-019-06116-9",
"https://app.dimensions.ai/details/publication/pub.1112860946"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T12:24",
"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/v90/0000000362_0000000362/records_87097_00000002.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs00330-019-06116-9"
}
]
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/s00330-019-06116-9'
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/s00330-019-06116-9'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-019-06116-9'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-019-06116-9'
This table displays all metadata directly associated to this object as RDF triples.
200 TRIPLES
21 PREDICATES
50 URIs
18 LITERALS
7 BLANK NODES