Reduced-iodine-dose dual-energy coronary CT angiography: qualitative and quantitative comparison between virtual monochromatic and polychromatic CT images View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-03-19

AUTHORS

David C. Rotzinger, Salim A. Si-Mohamed, Jérôme Yerly, Sara Boccalini, Fabio Becce, Loïc Boussel, Reto A. Meuli, Salah D. Qanadli, Philippe C. Douek

ABSTRACT

ObjectivesTo quantitatively evaluate the impact of virtual monochromatic images (VMI) on reduced-iodine-dose dual-energy coronary computed tomography angiography (CCTA) in terms of coronary lumen segmentation in vitro, and secondly to assess the image quality in vivo, compared with conventional CT obtained with regular iodine dose.Materials and methodsA phantom simulating regular and reduced iodine injection was used to determine the accuracy and precision of lumen area segmentation for various VMI energy levels. We retrospectively included 203 patients from December 2017 to August 2018 (mean age, 51.7 ± 16.8 years) who underwent CCTA using either standard (group A, n = 103) or reduced (group B, n = 100) iodine doses. Conventional images (group A) were qualitatively and quantitatively compared with 55-keV VMI (group B). We recorded the location of venous catheters.ResultsIn vitro, VMI outperformed conventional CT, with a segmentation accuracy of 0.998 vs. 1.684 mm2, respectively (p < 0.001), and a precision of 0.982 vs. 1.229 mm2, respectively (p < 0.001), in simulated overweight adult subjects. In vivo, the rate of diagnostic CCTA in groups A and B was 88.4% (n = 91/103) vs. 89% (n = 89/100), respectively, and noninferiority of protocol B was inferred. Contrast-to-noise ratios (CNR) of lumen versus fat and muscle were higher in group B (p < 0.001) and comparable for lumen versus calcium (p = 0.423). Venous catheters were more often placed on the forearm or hand in group B (p < 0.001).ConclusionIn vitro, low-keV VMI improve vessel area segmentation. In vivo, low-keV VMI allows for a 40% iodine dose and injection rate reduction while maintaining diagnostic image quality and improves the CNR between lumen versus fat and muscle.Key Points• Dual-energy coronary CT angiography is becoming increasingly available and might help improve patient management.• Compared with regular-iodine-dose coronary CT angiography, reduced-iodine-dose dual-energy CT with low-keV monochromatic image reconstructions performed better in phantom-based vessel cross-sectional segmentation and proved to be noninferior in vivo.• Patients receiving reduced-iodine-dose dual-energy coronary CT angiography often had the venous catheter placed on the forearm or wrist without compromising image quality. More... »

PAGES

7132-7142

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00330-021-07809-w

DOI

http://dx.doi.org/10.1007/s00330-021-07809-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1136528171

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/33740093


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/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "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": "Computed Tomography Angiography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Contrast Media", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Dose-Response Relationship, Drug", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Iodine", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "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": "Radiography, Dual-Energy Scanned Projection", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Retrospective Studies", 
        "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": "Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.9851.5", 
          "name": [
            "Department of Diagnostic and Interventional Radiology, Division of Cardiothoracic and Vascular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland", 
            "Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rotzinger", 
        "givenName": "David C.", 
        "id": "sg:person.01145600560.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145600560.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France", 
          "id": "http://www.grid.ac/institutes/grid.15399.37", 
          "name": [
            "Radiology Department, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500, Bron, France", 
            "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Si-Mohamed", 
        "givenName": "Salim A.", 
        "id": "sg:person.01010056261.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010056261.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Diagnostic and Interventional Radiology, Center for Biomedical Imaging (CIBM), Lausanne University Hospital (CHUV), Lausanne, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.8515.9", 
          "name": [
            "Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland", 
            "Department of Diagnostic and Interventional Radiology, Center for Biomedical Imaging (CIBM), Lausanne University Hospital (CHUV), Lausanne, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yerly", 
        "givenName": "J\u00e9r\u00f4me", 
        "id": "sg:person.013024660511.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013024660511.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France", 
          "id": "http://www.grid.ac/institutes/grid.15399.37", 
          "name": [
            "Radiology Department, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500, Bron, France", 
            "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boccalini", 
        "givenName": "Sara", 
        "id": "sg:person.010475754223.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010475754223.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.8515.9", 
          "name": [
            "Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland", 
            "Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Becce", 
        "givenName": "Fabio", 
        "id": "sg:person.0753711663.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0753711663.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France", 
          "id": "http://www.grid.ac/institutes/grid.15399.37", 
          "name": [
            "Radiology Department, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500, Bron, France", 
            "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boussel", 
        "givenName": "Lo\u00efc", 
        "id": "sg:person.01113450041.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01113450041.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.8515.9", 
          "name": [
            "Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland", 
            "Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Meuli", 
        "givenName": "Reto A.", 
        "id": "sg:person.0752552617.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0752552617.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.9851.5", 
          "name": [
            "Department of Diagnostic and Interventional Radiology, Division of Cardiothoracic and Vascular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland", 
            "Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Qanadli", 
        "givenName": "Salah D.", 
        "id": "sg:person.0665540063.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665540063.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France", 
          "id": "http://www.grid.ac/institutes/grid.15399.37", 
          "name": [
            "Radiology Department, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500, Bron, France", 
            "University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Douek", 
        "givenName": "Philippe C.", 
        "id": "sg:person.01211025760.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01211025760.77"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/s41598-018-36045-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110346463", 
          "https://doi.org/10.1038/s41598-018-36045-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-016-4437-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049861943", 
          "https://doi.org/10.1007/s00330-016-4437-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-018-1329-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101374448", 
          "https://doi.org/10.1007/s10554-018-1329-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-018-1452-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106893994", 
          "https://doi.org/10.1007/s10554-018-1452-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12968-019-0521-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1111954566", 
          "https://doi.org/10.1186/s12968-019-0521-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-018-5308-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101243031", 
          "https://doi.org/10.1007/s00330-018-5308-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10554-011-9848-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020030597", 
          "https://doi.org/10.1007/s10554-011-9848-8"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2021-03-19", 
    "datePublishedReg": "2021-03-19", 
    "description": "ObjectivesTo quantitatively evaluate the impact of virtual monochromatic images (VMI) on reduced-iodine-dose dual-energy coronary computed tomography angiography (CCTA) in terms of coronary lumen segmentation in vitro, and secondly to assess the image quality in vivo, compared with conventional CT obtained with regular iodine dose.Materials and methodsA phantom simulating regular and reduced iodine injection was used to determine the accuracy and precision of lumen area segmentation for various VMI energy levels. We retrospectively included 203 patients from December 2017 to August 2018 (mean age, 51.7 \u00b1 16.8 years) who underwent CCTA using either standard (group A, n = 103) or reduced (group B, n = 100) iodine doses. Conventional images (group A) were qualitatively and quantitatively compared with 55-keV VMI (group B). We recorded the location of venous catheters.ResultsIn vitro, VMI outperformed conventional CT, with a segmentation accuracy of 0.998 vs. 1.684 mm2, respectively (p < 0.001), and a precision of 0.982 vs. 1.229 mm2, respectively (p < 0.001), in simulated overweight adult subjects. In vivo, the rate of diagnostic CCTA in groups A and B was 88.4% (n = 91/103) vs. 89% (n = 89/100), respectively, and noninferiority of protocol B was inferred. Contrast-to-noise ratios (CNR) of lumen versus fat and muscle were higher in group B (p < 0.001) and comparable for lumen versus calcium (p = 0.423). Venous catheters were more often placed on the forearm or hand in group B (p < 0.001).ConclusionIn vitro, low-keV VMI improve vessel area segmentation. In vivo, low-keV VMI allows for a 40% iodine dose and injection rate reduction while maintaining diagnostic image quality and improves the CNR between lumen versus fat and muscle.Key Points\u2022 Dual-energy coronary CT angiography is becoming increasingly available and might help improve patient management.\u2022 Compared with regular-iodine-dose coronary CT angiography, reduced-iodine-dose dual-energy CT with low-keV monochromatic image reconstructions performed better in phantom-based vessel cross-sectional segmentation and proved to be noninferior in vivo.\u2022 Patients receiving reduced-iodine-dose dual-energy coronary CT angiography often had the venous catheter placed on the forearm or wrist without compromising image quality.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00330-021-07809-w", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1289120", 
        "issn": [
          "0938-7994", 
          "1432-1084"
        ], 
        "name": "European Radiology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "9", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "31"
      }
    ], 
    "keywords": [
      "coronary CT angiography", 
      "keV virtual monochromatic images", 
      "dual-energy coronary CT angiography", 
      "venous catheters", 
      "CT angiography", 
      "iodine dose", 
      "group B", 
      "overweight adult subjects", 
      "conventional CT", 
      "virtual monochromatic images", 
      "diagnostic CCTA", 
      "diagnostic image quality", 
      "tomography angiography", 
      "patient management", 
      "group A", 
      "protocol B", 
      "angiography", 
      "iodine doses", 
      "adult subjects", 
      "catheter", 
      "dual-energy CT", 
      "methodsA phantom", 
      "CT", 
      "patients", 
      "monochromatic image reconstructions", 
      "CCTA", 
      "vivo", 
      "forearm", 
      "dose", 
      "lumen", 
      "cross-sectional segmentation", 
      "muscle", 
      "iodine injection", 
      "VMI energy levels", 
      "CT images", 
      "fat", 
      "coronary lumen segmentation", 
      "coronaries", 
      "noninferiority", 
      "ConclusionIn", 
      "image quality", 
      "ObjectivesTo", 
      "ResultsIn", 
      "doses", 
      "wrist", 
      "injection", 
      "vitro", 
      "rate reduction", 
      "subjects", 
      "calcium", 
      "polychromatic CT images", 
      "quality", 
      "conventional images", 
      "management", 
      "levels", 
      "CNR", 
      "rate", 
      "contrast", 
      "reduction", 
      "reconstruction", 
      "monochromatic images", 
      "lumen segmentation", 
      "mm2", 
      "ratio", 
      "standards", 
      "impact", 
      "hand", 
      "comparison", 
      "injection rate reductions", 
      "images", 
      "phantom", 
      "location", 
      "quantitative comparison", 
      "accuracy", 
      "materials", 
      "area segmentation", 
      "terms", 
      "precision", 
      "noise ratio", 
      "segmentation", 
      "image reconstruction", 
      "segmentation accuracy", 
      "energy levels"
    ], 
    "name": "Reduced-iodine-dose dual-energy coronary CT angiography: qualitative and quantitative comparison between virtual monochromatic and polychromatic CT images", 
    "pagination": "7132-7142", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1136528171"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00330-021-07809-w"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "33740093"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00330-021-07809-w", 
      "https://app.dimensions.ai/details/publication/pub.1136528171"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:50", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_922.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00330-021-07809-w"
  }
]
 

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/s00330-021-07809-w'

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/s00330-021-07809-w'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00330-021-07809-w'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00330-021-07809-w'


 

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

291 TRIPLES      21 PREDICATES      128 URIs      113 LITERALS      20 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00330-021-07809-w schema:about N21936f3041534e048963aab602c24981
2 N76dbf7c046494f22aa7e022240b12a41
3 N8a0a96fd45cf44bc9cc4b04c3ae2f2df
4 N9e916fc266c547198f274ea2f6c6ed48
5 Na90b4e89eecc4929b7e98454667e3e97
6 Nba8d54f63154410688e0f5189bf8f69e
7 Nbe004bd7d7034e8db1347c88ce7e9869
8 Ncabb63d8f6134514af0c1d952747fc8c
9 Nd61ac8acc07f4c8e921bf04dd3d3e7fb
10 Ne7ca471abefc4fc5a568d0d354f9b252
11 Ne8d1f65c1d414f8693574f2decb7b4ff
12 Nef85f3b2345f437881de633b270cb278
13 Nf2cd773923b44ed89fb036af6f715442
14 anzsrc-for:11
15 anzsrc-for:1103
16 schema:author N5ab683bd1f9348ddb7a1809d279a3199
17 schema:citation sg:pub.10.1007/s00330-016-4437-9
18 sg:pub.10.1007/s00330-018-5308-3
19 sg:pub.10.1007/s10554-011-9848-8
20 sg:pub.10.1007/s10554-018-1329-x
21 sg:pub.10.1007/s10554-018-1452-8
22 sg:pub.10.1038/s41598-018-36045-4
23 sg:pub.10.1186/s12968-019-0521-z
24 schema:datePublished 2021-03-19
25 schema:datePublishedReg 2021-03-19
26 schema:description ObjectivesTo quantitatively evaluate the impact of virtual monochromatic images (VMI) on reduced-iodine-dose dual-energy coronary computed tomography angiography (CCTA) in terms of coronary lumen segmentation in vitro, and secondly to assess the image quality in vivo, compared with conventional CT obtained with regular iodine dose.Materials and methodsA phantom simulating regular and reduced iodine injection was used to determine the accuracy and precision of lumen area segmentation for various VMI energy levels. We retrospectively included 203 patients from December 2017 to August 2018 (mean age, 51.7 ± 16.8 years) who underwent CCTA using either standard (group A, n = 103) or reduced (group B, n = 100) iodine doses. Conventional images (group A) were qualitatively and quantitatively compared with 55-keV VMI (group B). We recorded the location of venous catheters.ResultsIn vitro, VMI outperformed conventional CT, with a segmentation accuracy of 0.998 vs. 1.684 mm2, respectively (p < 0.001), and a precision of 0.982 vs. 1.229 mm2, respectively (p < 0.001), in simulated overweight adult subjects. In vivo, the rate of diagnostic CCTA in groups A and B was 88.4% (n = 91/103) vs. 89% (n = 89/100), respectively, and noninferiority of protocol B was inferred. Contrast-to-noise ratios (CNR) of lumen versus fat and muscle were higher in group B (p < 0.001) and comparable for lumen versus calcium (p = 0.423). Venous catheters were more often placed on the forearm or hand in group B (p < 0.001).ConclusionIn vitro, low-keV VMI improve vessel area segmentation. In vivo, low-keV VMI allows for a 40% iodine dose and injection rate reduction while maintaining diagnostic image quality and improves the CNR between lumen versus fat and muscle.Key Points• Dual-energy coronary CT angiography is becoming increasingly available and might help improve patient management.• Compared with regular-iodine-dose coronary CT angiography, reduced-iodine-dose dual-energy CT with low-keV monochromatic image reconstructions performed better in phantom-based vessel cross-sectional segmentation and proved to be noninferior in vivo.• Patients receiving reduced-iodine-dose dual-energy coronary CT angiography often had the venous catheter placed on the forearm or wrist without compromising image quality.
27 schema:genre article
28 schema:isAccessibleForFree true
29 schema:isPartOf N249f3c06c1b44a17b247a32986e737e4
30 N38f1e05d35e141eeb21a8e1fc1075c6f
31 sg:journal.1289120
32 schema:keywords CCTA
33 CNR
34 CT
35 CT angiography
36 CT images
37 ConclusionIn
38 ObjectivesTo
39 ResultsIn
40 VMI energy levels
41 accuracy
42 adult subjects
43 angiography
44 area segmentation
45 calcium
46 catheter
47 comparison
48 contrast
49 conventional CT
50 conventional images
51 coronaries
52 coronary CT angiography
53 coronary lumen segmentation
54 cross-sectional segmentation
55 diagnostic CCTA
56 diagnostic image quality
57 dose
58 doses
59 dual-energy CT
60 dual-energy coronary CT angiography
61 energy levels
62 fat
63 forearm
64 group A
65 group B
66 hand
67 image quality
68 image reconstruction
69 images
70 impact
71 injection
72 injection rate reductions
73 iodine dose
74 iodine doses
75 iodine injection
76 keV virtual monochromatic images
77 levels
78 location
79 lumen
80 lumen segmentation
81 management
82 materials
83 methodsA phantom
84 mm2
85 monochromatic image reconstructions
86 monochromatic images
87 muscle
88 noise ratio
89 noninferiority
90 overweight adult subjects
91 patient management
92 patients
93 phantom
94 polychromatic CT images
95 precision
96 protocol B
97 quality
98 quantitative comparison
99 rate
100 rate reduction
101 ratio
102 reconstruction
103 reduction
104 segmentation
105 segmentation accuracy
106 standards
107 subjects
108 terms
109 tomography angiography
110 venous catheters
111 virtual monochromatic images
112 vitro
113 vivo
114 wrist
115 schema:name Reduced-iodine-dose dual-energy coronary CT angiography: qualitative and quantitative comparison between virtual monochromatic and polychromatic CT images
116 schema:pagination 7132-7142
117 schema:productId N426e7d5c30a342db85e53d0c421ecbdc
118 N78da3269339e46cc8d88d3c6df047be1
119 Nd79092d9ddb648a097a43ad0740353d4
120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1136528171
121 https://doi.org/10.1007/s00330-021-07809-w
122 schema:sdDatePublished 2022-10-01T06:50
123 schema:sdLicense https://scigraph.springernature.com/explorer/license/
124 schema:sdPublisher Nb212977928e343399e46a5a18660b08a
125 schema:url https://doi.org/10.1007/s00330-021-07809-w
126 sgo:license sg:explorer/license/
127 sgo:sdDataset articles
128 rdf:type schema:ScholarlyArticle
129 N21936f3041534e048963aab602c24981 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Signal-To-Noise Ratio
131 rdf:type schema:DefinedTerm
132 N249f3c06c1b44a17b247a32986e737e4 schema:issueNumber 9
133 rdf:type schema:PublicationIssue
134 N38f1e05d35e141eeb21a8e1fc1075c6f schema:volumeNumber 31
135 rdf:type schema:PublicationVolume
136 N426e7d5c30a342db85e53d0c421ecbdc schema:name dimensions_id
137 schema:value pub.1136528171
138 rdf:type schema:PropertyValue
139 N4a792a8083e04ce4b6595d25684bbe7d rdf:first sg:person.01211025760.77
140 rdf:rest rdf:nil
141 N5ab683bd1f9348ddb7a1809d279a3199 rdf:first sg:person.01145600560.33
142 rdf:rest N77b582b9ffd343c49b6b25b485322aa1
143 N64619ac8d96d490ab395b628526140eb rdf:first sg:person.0665540063.53
144 rdf:rest N4a792a8083e04ce4b6595d25684bbe7d
145 N76dbf7c046494f22aa7e022240b12a41 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Retrospective Studies
147 rdf:type schema:DefinedTerm
148 N77b582b9ffd343c49b6b25b485322aa1 rdf:first sg:person.01010056261.39
149 rdf:rest Ndbc1d583a57a442da56cf429e33abe03
150 N78da3269339e46cc8d88d3c6df047be1 schema:name doi
151 schema:value 10.1007/s00330-021-07809-w
152 rdf:type schema:PropertyValue
153 N7af90afd716245df8d5fbf015b823087 rdf:first sg:person.0752552617.39
154 rdf:rest N64619ac8d96d490ab395b628526140eb
155 N8a0a96fd45cf44bc9cc4b04c3ae2f2df schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name Middle Aged
157 rdf:type schema:DefinedTerm
158 N9e916fc266c547198f274ea2f6c6ed48 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
159 schema:name Humans
160 rdf:type schema:DefinedTerm
161 Na90b4e89eecc4929b7e98454667e3e97 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Computed Tomography Angiography
163 rdf:type schema:DefinedTerm
164 Nb212977928e343399e46a5a18660b08a schema:name Springer Nature - SN SciGraph project
165 rdf:type schema:Organization
166 Nba8d54f63154410688e0f5189bf8f69e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Radiographic Image Interpretation, Computer-Assisted
168 rdf:type schema:DefinedTerm
169 Nbe004bd7d7034e8db1347c88ce7e9869 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
170 schema:name Contrast Media
171 rdf:type schema:DefinedTerm
172 Ncabb63d8f6134514af0c1d952747fc8c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
173 schema:name Aged
174 rdf:type schema:DefinedTerm
175 Nd1ac2cfb38db44539834112c7ceceaec rdf:first sg:person.0753711663.82
176 rdf:rest Nf1f69883940b411d8adcaa22d7efe653
177 Nd61ac8acc07f4c8e921bf04dd3d3e7fb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Tomography, X-Ray Computed
179 rdf:type schema:DefinedTerm
180 Nd79092d9ddb648a097a43ad0740353d4 schema:name pubmed_id
181 schema:value 33740093
182 rdf:type schema:PropertyValue
183 Ndbc1d583a57a442da56cf429e33abe03 rdf:first sg:person.013024660511.53
184 rdf:rest Nf418bf940b2742e3b684d54a8b6e1d2e
185 Ne7ca471abefc4fc5a568d0d354f9b252 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
186 schema:name Dose-Response Relationship, Drug
187 rdf:type schema:DefinedTerm
188 Ne8d1f65c1d414f8693574f2decb7b4ff schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
189 schema:name Radiography, Dual-Energy Scanned Projection
190 rdf:type schema:DefinedTerm
191 Nef85f3b2345f437881de633b270cb278 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
192 schema:name Iodine
193 rdf:type schema:DefinedTerm
194 Nf1f69883940b411d8adcaa22d7efe653 rdf:first sg:person.01113450041.51
195 rdf:rest N7af90afd716245df8d5fbf015b823087
196 Nf2cd773923b44ed89fb036af6f715442 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
197 schema:name Adult
198 rdf:type schema:DefinedTerm
199 Nf418bf940b2742e3b684d54a8b6e1d2e rdf:first sg:person.010475754223.32
200 rdf:rest Nd1ac2cfb38db44539834112c7ceceaec
201 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
202 schema:name Medical and Health Sciences
203 rdf:type schema:DefinedTerm
204 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
205 schema:name Clinical Sciences
206 rdf:type schema:DefinedTerm
207 sg:journal.1289120 schema:issn 0938-7994
208 1432-1084
209 schema:name European Radiology
210 schema:publisher Springer Nature
211 rdf:type schema:Periodical
212 sg:person.01010056261.39 schema:affiliation grid-institutes:grid.15399.37
213 schema:familyName Si-Mohamed
214 schema:givenName Salim A.
215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010056261.39
216 rdf:type schema:Person
217 sg:person.010475754223.32 schema:affiliation grid-institutes:grid.15399.37
218 schema:familyName Boccalini
219 schema:givenName Sara
220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010475754223.32
221 rdf:type schema:Person
222 sg:person.01113450041.51 schema:affiliation grid-institutes:grid.15399.37
223 schema:familyName Boussel
224 schema:givenName Loïc
225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01113450041.51
226 rdf:type schema:Person
227 sg:person.01145600560.33 schema:affiliation grid-institutes:grid.9851.5
228 schema:familyName Rotzinger
229 schema:givenName David C.
230 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145600560.33
231 rdf:type schema:Person
232 sg:person.01211025760.77 schema:affiliation grid-institutes:grid.15399.37
233 schema:familyName Douek
234 schema:givenName Philippe C.
235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01211025760.77
236 rdf:type schema:Person
237 sg:person.013024660511.53 schema:affiliation grid-institutes:grid.8515.9
238 schema:familyName Yerly
239 schema:givenName Jérôme
240 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013024660511.53
241 rdf:type schema:Person
242 sg:person.0665540063.53 schema:affiliation grid-institutes:grid.9851.5
243 schema:familyName Qanadli
244 schema:givenName Salah D.
245 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0665540063.53
246 rdf:type schema:Person
247 sg:person.0752552617.39 schema:affiliation grid-institutes:grid.8515.9
248 schema:familyName Meuli
249 schema:givenName Reto A.
250 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0752552617.39
251 rdf:type schema:Person
252 sg:person.0753711663.82 schema:affiliation grid-institutes:grid.8515.9
253 schema:familyName Becce
254 schema:givenName Fabio
255 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0753711663.82
256 rdf:type schema:Person
257 sg:pub.10.1007/s00330-016-4437-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049861943
258 https://doi.org/10.1007/s00330-016-4437-9
259 rdf:type schema:CreativeWork
260 sg:pub.10.1007/s00330-018-5308-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101243031
261 https://doi.org/10.1007/s00330-018-5308-3
262 rdf:type schema:CreativeWork
263 sg:pub.10.1007/s10554-011-9848-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020030597
264 https://doi.org/10.1007/s10554-011-9848-8
265 rdf:type schema:CreativeWork
266 sg:pub.10.1007/s10554-018-1329-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1101374448
267 https://doi.org/10.1007/s10554-018-1329-x
268 rdf:type schema:CreativeWork
269 sg:pub.10.1007/s10554-018-1452-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106893994
270 https://doi.org/10.1007/s10554-018-1452-8
271 rdf:type schema:CreativeWork
272 sg:pub.10.1038/s41598-018-36045-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110346463
273 https://doi.org/10.1038/s41598-018-36045-4
274 rdf:type schema:CreativeWork
275 sg:pub.10.1186/s12968-019-0521-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1111954566
276 https://doi.org/10.1186/s12968-019-0521-z
277 rdf:type schema:CreativeWork
278 grid-institutes:grid.15399.37 schema:alternateName University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France
279 schema:name Radiology Department, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500, Bron, France
280 University Claude Bernard Lyon 1, CREATIS, CNRS UMR 5220, INSERM U1206, INSA-Lyon, Lyon, France
281 rdf:type schema:Organization
282 grid-institutes:grid.8515.9 schema:alternateName Department of Diagnostic and Interventional Radiology, Center for Biomedical Imaging (CIBM), Lausanne University Hospital (CHUV), Lausanne, Switzerland
283 Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
284 schema:name Department of Diagnostic and Interventional Radiology, Center for Biomedical Imaging (CIBM), Lausanne University Hospital (CHUV), Lausanne, Switzerland
285 Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
286 Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
287 rdf:type schema:Organization
288 grid-institutes:grid.9851.5 schema:alternateName Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
289 schema:name Department of Diagnostic and Interventional Radiology, Division of Cardiothoracic and Vascular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland
290 Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
291 rdf:type schema:Organization
 




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


...