Ontology type: schema:Chapter Open Access: True
2010
AUTHORSDime Vitanovski , Alexey Tsymbal , Razvan Ioan Ionasec , Bogdan Georgescu , Martin Huber , Andrew Taylor , Silvia Schievano , Shaohua Kevin Zhou , Joachim Hornegger , Dorin Comaniciu
ABSTRACTCongenital heart defect is the primary cause of death in newborns, due to typically complex malformation of the cardiac system. The pulmonary valve and trunk are often affected and require complex clinical management and in most cases surgical or interventional treatment. While minimal invasive methods are emerging, non-invasive imaging-based assessment tools become crucial components in the clinical setting. For advanced evaluation and therapy planning purposes, cardiac Computed Tomography (CT) and cardiac Magnetic Resonance Imaging (cMRI) are important non-invasive investigation techniques with complementary properties. Although, characterized by high temporal resolution, cMRI does not cover the full motion of the pulmonary trunk. The sparse cMRI data acquired in this context include only one 3D scan of the heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. In this paper we present a cross-modality framework for the evaluation of the pulmonary trunk, which combines the advantages of both, cardiac CT and cMRI. A patient-specific model is estimated from both modalities using hierarchical learning-based techniques. The pulmonary trunk model is exploited within a novel dynamic regression-based reconstruction to infer the incomplete cMRI temporal information. Extensive experiments performed on 72 cardiac CT and 74 cMRI sequences demonstrated the average speed of 110 seconds and accuracy of 1.4mm for the proposed approach. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from sparse 4D cMRI data. More... »
PAGES460-467
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010
ISBN
978-3-642-15704-2
978-3-642-15705-9
http://scigraph.springernature.com/pub.10.1007/978-3-642-15705-9_56
DOIhttp://dx.doi.org/10.1007/978-3-642-15705-9_56
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1003071427
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/20879263
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": "Algorithms",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Heart Defects, Congenital",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Image Enhancement",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Image Interpretation, Computer-Assisted",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Magnetic Resonance Imaging",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Pattern Recognition, Automated",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Pulmonary Artery",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Reproducibility of Results",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Sensitivity and Specificity",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Subtraction Technique",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Tomography, X-Ray Computed",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Pattern Recognition Lab, Friedrich-Alexander-University, Erlangen-Nuremberg, Germany",
"id": "http://www.grid.ac/institutes/grid.5330.5",
"name": [
"Integrated Data Systems, Siemens Corporate Research, Princeton, USA",
"Pattern Recognition Lab, Friedrich-Alexander-University, Erlangen-Nuremberg, 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": "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": "Tsymbal",
"givenName": "Alexey",
"id": "sg:person.015310157176.53",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015310157176.53"
],
"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": "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": "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": "Huber",
"givenName": "Martin",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Great Ormond Street Hospital for Children, London, England",
"id": "http://www.grid.ac/institutes/grid.420468.c",
"name": [
"Great Ormond Street Hospital for Children, London, England"
],
"type": "Organization"
},
"familyName": "Taylor",
"givenName": "Andrew",
"id": "sg:person.01120223741.15",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01120223741.15"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Great Ormond Street Hospital for Children, London, England",
"id": "http://www.grid.ac/institutes/grid.420468.c",
"name": [
"Great Ormond Street Hospital for Children, London, England"
],
"type": "Organization"
},
"familyName": "Schievano",
"givenName": "Silvia",
"id": "sg:person.01116702706.22",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01116702706.22"
],
"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": "Zhou",
"givenName": "Shaohua Kevin",
"id": "sg:person.01372425362.30",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372425362.30"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Pattern Recognition Lab, Friedrich-Alexander-University, Erlangen-Nuremberg, Germany",
"id": "http://www.grid.ac/institutes/grid.5330.5",
"name": [
"Pattern Recognition Lab, Friedrich-Alexander-University, Erlangen-Nuremberg, 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": "2010",
"datePublishedReg": "2010-01-01",
"description": "Congenital heart defect is the primary cause of death in newborns, due to typically complex malformation of the cardiac system. The pulmonary valve and trunk are often affected and require complex clinical management and in most cases surgical or interventional treatment. While minimal invasive methods are emerging, non-invasive imaging-based assessment tools become crucial components in the clinical setting. For advanced evaluation and therapy planning purposes, cardiac Computed Tomography (CT) and cardiac Magnetic Resonance Imaging (cMRI) are important non-invasive investigation techniques with complementary properties. Although, characterized by high temporal resolution, cMRI does not cover the full motion of the pulmonary trunk. The sparse cMRI data acquired in this context include only one 3D scan of the heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. In this paper we present a cross-modality framework for the evaluation of the pulmonary trunk, which combines the advantages of both, cardiac CT and cMRI. A patient-specific model is estimated from both modalities using hierarchical learning-based techniques. The pulmonary trunk model is exploited within a novel dynamic regression-based reconstruction to infer the incomplete cMRI temporal information. Extensive experiments performed on 72 cardiac CT and 74 cMRI sequences demonstrated the average speed of 110 seconds and accuracy of 1.4mm for the proposed approach. To the best of our knowledge this is the first dynamic model of the pulmonary trunk and right ventricle outflow track estimated from sparse 4D cMRI data.",
"editor": [
{
"familyName": "Jiang",
"givenName": "Tianzi",
"type": "Person"
},
{
"familyName": "Navab",
"givenName": "Nassir",
"type": "Person"
},
{
"familyName": "Pluim",
"givenName": "Josien P. W.",
"type": "Person"
},
{
"familyName": "Viergever",
"givenName": "Max A.",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-642-15705-9_56",
"inLanguage": "en",
"isAccessibleForFree": true,
"isPartOf": {
"isbn": [
"978-3-642-15704-2",
"978-3-642-15705-9"
],
"name": "Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2010",
"type": "Book"
},
"keywords": [
"cardiac magnetic resonance imaging",
"cardiac computed tomography",
"learning-based techniques",
"cardiac CT",
"CMRI data",
"pulmonary trunk",
"non-invasive investigation technique",
"Extensive experiments",
"temporal information",
"first dynamic model",
"patient-specific models",
"congenital heart defects",
"complex clinical management",
"minimal invasive method",
"magnetic resonance imaging",
"complementary properties",
"pulmonary valve",
"interventional treatment",
"clinical management",
"complex malformations",
"outflow track",
"end-diastolic phase",
"computed tomography",
"heart defects",
"resonance imaging",
"MRI imaging",
"invasive method",
"Modality Assessment",
"regression-based reconstruction",
"clinical setting",
"planning purposes",
"trunk",
"whole cardiac cycle",
"cardiac cycle",
"cardiac system",
"primary cause",
"assessment tool",
"full motion",
"advanced evaluation",
"crucial component",
"treatment",
"average speed",
"high temporal resolution",
"dynamic model",
"most cases",
"imaging",
"technique",
"framework",
"accuracy",
"newborns",
"model",
"information",
"trunk model",
"malformations",
"planning",
"tool",
"death",
"tomography",
"temporal resolution",
"data",
"scans",
"modalities",
"heart",
"evaluation",
"cause",
"system",
"advantages",
"speed",
"track",
"seconds",
"reconstruction",
"valve",
"context",
"knowledge",
"setting",
"assessment",
"management",
"investigation techniques",
"method",
"experiments",
"cases",
"defects",
"motion",
"sequence",
"purpose",
"components",
"resolution",
"cycle",
"plane",
"approach",
"phase",
"properties",
"paper",
"non-invasive imaging-based assessment tools",
"imaging-based assessment tools",
"therapy planning purposes",
"important non-invasive investigation techniques",
"sparse cMRI data",
"cross-modality framework",
"hierarchical learning-based techniques",
"pulmonary trunk model",
"novel dynamic regression-based reconstruction",
"dynamic regression-based reconstruction",
"incomplete cMRI temporal information",
"cMRI temporal information",
"cMRI sequences",
"right ventricle outflow track",
"ventricle outflow track",
"sparse 4D cMRI data",
"Pulmonary Trunk Treatment",
"Trunk Treatment"
],
"name": "Cross-Modality Assessment and Planning for Pulmonary Trunk Treatment Using CT and MRI Imaging",
"pagination": "460-467",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1003071427"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-642-15705-9_56"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"20879263"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-642-15705-9_56",
"https://app.dimensions.ai/details/publication/pub.1003071427"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-01-01T19:27",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_73.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-642-15705-9_56"
}
]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.10.1007/978-3-642-15705-9_56'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15705-9_56'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15705-9_56'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15705-9_56'
This table displays all metadata directly associated to this object as RDF triples.
315 TRIPLES
23 PREDICATES
152 URIs
143 LITERALS
20 BLANK NODES