Ontology type: schema:Chapter Open Access: True
2009
AUTHORSDime Vitanovski , Razvan Ioan Ionasec , Bogdan Georgescu , Martin Huber , Andrew Mayall Taylor , Joachim Hornegger , Dorin Comaniciu
ABSTRACTPulmonary valve disease affects a significant portion of the global population and often occurs in conjunction with other heart dysfunctions. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. As minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. In this paper, we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. An abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. A hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric CT scans. The algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. The estimated personalized model enables for efficient and precise quantification of function and morphology. This ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. Experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. An initial clinical validation yielded a significant correlation between model-based and expert measurements. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from CT data. More... »
PAGES17-25
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009
ISBN
978-3-642-04267-6
978-3-642-04268-3
http://scigraph.springernature.com/pub.10.1007/978-3-642-04268-3_3
DOIhttp://dx.doi.org/10.1007/978-3-642-04268-3_3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1011317037
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/20425966
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/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing 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"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1102",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Cardiorespiratory Medicine and Haematology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Angiography",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Computer Simulation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Heart Valve Diseases",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Heart Valve Prosthesis Implantation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Cardiovascular",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Preoperative Care",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Pulmonary Valve",
"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": "Surgery, Computer-Assisted",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Tomography, X-Ray Computed",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Chair of Pattern Recognition, Friedrich-Alexander-University, Erlangen, Germany",
"id": "http://www.grid.ac/institutes/grid.5330.5",
"name": [
"Software and Engineering, Siemens Corporate Technology, Erlangen, Germany",
"Chair of Pattern Recognition, Friedrich-Alexander-University, Erlangen, Germany"
],
"type": "Organization"
},
"familyName": "Vitanovski",
"givenName": "Dime",
"id": "sg:person.01242456111.33",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01242456111.33"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Computer Aided Medical Procedures, Technical University Munich, Germany",
"id": "http://www.grid.ac/institutes/grid.6936.a",
"name": [
"Integrated Data Systems, Siemens Corporate Research, Princeton, USA",
"Computer Aided Medical Procedures, Technical University Munich, Germany"
],
"type": "Organization"
},
"familyName": "Ionasec",
"givenName": "Razvan Ioan",
"id": "sg:person.01010560470.38",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010560470.38"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Integrated Data Systems, Siemens Corporate Research, Princeton, USA",
"id": "http://www.grid.ac/institutes/grid.419233.e",
"name": [
"Integrated Data Systems, Siemens Corporate Research, Princeton, USA"
],
"type": "Organization"
},
"familyName": "Georgescu",
"givenName": "Bogdan",
"id": "sg:person.0703547214.37",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0703547214.37"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Software and Engineering, Siemens Corporate Technology, Erlangen, Germany",
"id": "http://www.grid.ac/institutes/grid.5406.7",
"name": [
"Software and Engineering, Siemens Corporate Technology, Erlangen, Germany"
],
"type": "Organization"
},
"familyName": "Huber",
"givenName": "Martin",
"id": "sg:person.0756423211.71",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0756423211.71"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "UCL Institute of Child Health & Great Ormond Street Hospital for Children, UK",
"id": "http://www.grid.ac/institutes/None",
"name": [
"UCL Institute of Child Health & Great Ormond Street Hospital for Children, UK"
],
"type": "Organization"
},
"familyName": "Taylor",
"givenName": "Andrew Mayall",
"id": "sg:person.01120223741.15",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01120223741.15"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Chair of Pattern Recognition, Friedrich-Alexander-University, Erlangen, Germany",
"id": "http://www.grid.ac/institutes/grid.5330.5",
"name": [
"Chair of Pattern Recognition, Friedrich-Alexander-University, Erlangen, Germany"
],
"type": "Organization"
},
"familyName": "Hornegger",
"givenName": "Joachim",
"id": "sg:person.01322323610.92",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322323610.92"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Integrated Data Systems, Siemens Corporate Research, Princeton, USA",
"id": "http://www.grid.ac/institutes/grid.419233.e",
"name": [
"Integrated Data Systems, Siemens Corporate Research, Princeton, USA"
],
"type": "Organization"
},
"familyName": "Comaniciu",
"givenName": "Dorin",
"id": "sg:person.01066111014.77",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066111014.77"
],
"type": "Person"
}
],
"datePublished": "2009",
"datePublishedReg": "2009-01-01",
"description": "Pulmonary valve disease affects a significant portion of the global population and often occurs in conjunction with other heart dysfunctions. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. As minimal invasive procedures become common practice, imaging and non-invasive assessment techniques turn into key clinical tools. In this paper, we propose a novel approach for intervention planning as well as morphological and functional quantification of the pulmonary trunk and valve. An abstraction of the anatomic structures is represented through a four-dimensional, physiological model able to capture large pathological variation. A hierarchical estimation, based on robust learning methods, is applied to identify the patient-specific model parameters from volumetric CT scans. The algorithm involves detection of piecewise affine parameters, fast centre-line computation and local surface delineation. The estimated personalized model enables for efficient and precise quantification of function and morphology. This ability may have impact on the assessment and surgical interventions of the pulmonary valve and trunk. Experiments performed on 50 cardiac computer tomography sequences demonstrated the average speed of 202 seconds and accuracy of 2.2mm for the proposed approach. An initial clinical validation yielded a significant correlation between model-based and expert measurements. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from CT data.",
"editor": [
{
"familyName": "Yang",
"givenName": "Guang-Zhong",
"type": "Person"
},
{
"familyName": "Hawkes",
"givenName": "David",
"type": "Person"
},
{
"familyName": "Rueckert",
"givenName": "Daniel",
"type": "Person"
},
{
"familyName": "Noble",
"givenName": "Alison",
"type": "Person"
},
{
"familyName": "Taylor",
"givenName": "Chris",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-642-04268-3_3",
"inLanguage": "en",
"isAccessibleForFree": true,
"isPartOf": {
"isbn": [
"978-3-642-04267-6",
"978-3-642-04268-3"
],
"name": "Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2009",
"type": "Book"
},
"keywords": [
"pulmonary trunk",
"percutaneous pulmonary valve implantation",
"pulmonary valve implantation",
"key clinical tool",
"non-invasive assessment techniques",
"minimal invasive procedure",
"initial clinical validation",
"intervention planning",
"valve implantation",
"surgical intervention",
"pulmonary valve",
"heart surgery",
"heart dysfunction",
"robust learning method",
"outflow track",
"invasive procedures",
"CT scan",
"interventional methods",
"valve assessment",
"clinical tool",
"volumetric CT scans",
"clinical validation",
"patient-specific model parameters",
"CT data",
"learning method",
"functional quantification",
"anatomic structures",
"significant correlation",
"hierarchical estimation",
"trunk",
"pathological variations",
"expert measurements",
"first dynamic model",
"tomography sequences",
"affine parameters",
"novel approach",
"surgery",
"dysfunction",
"model enables",
"disease",
"physiological model",
"model parameters",
"assessment",
"intervention",
"global population",
"scans",
"implantation",
"dynamic model",
"imaging",
"algorithm",
"abstraction",
"planning",
"computation",
"population",
"valve",
"average speed",
"assessment techniques",
"precise quantification",
"common practice",
"data",
"accuracy",
"significant portion",
"quantification",
"correlation",
"delineation",
"procedure",
"tool",
"parameters",
"enables",
"model",
"method",
"modeling",
"detection",
"practice",
"estimation",
"function",
"ability",
"speed",
"technique",
"track",
"portion",
"seconds",
"validation",
"knowledge",
"alternative",
"approach",
"impact",
"conjunction",
"experiments",
"sequence",
"measurements",
"structure",
"morphology",
"variation",
"paper"
],
"name": "Personalized Pulmonary Trunk Modeling for Intervention Planning and Valve Assessment Estimated from CT Data",
"pagination": "17-25",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1011317037"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-642-04268-3_3"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"20425966"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-642-04268-3_3",
"https://app.dimensions.ai/details/publication/pub.1011317037"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-05-20T07:47",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/chapter/chapter_38.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-642-04268-3_3"
}
]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.10.1007/978-3-642-04268-3_3'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-04268-3_3'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-04268-3_3'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-04268-3_3'
This table displays all metadata directly associated to this object as RDF triples.
287 TRIPLES
23 PREDICATES
135 URIs
126 LITERALS
19 BLANK NODES