FEM Based 3D Tumor Growth Prediction for Kidney Tumor View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Xinjian Chen , Ronald Summers , Jianhua Yao

ABSTRACT

It is important to predict the tumor growth so that appropriate treatment can be planned especially in the early stage. In this paper, we propose a finite element method (FEM) based 3D tumor growth prediction system using longitudinal kidney tumor images. To the best of our knowledge, this is the first kidney tumor growth prediction system. The kidney tissues are classified into three types: renal cortex, renal medulla and renal pelvis. The reaction-diffusion model is applied as the tumor growth model. Different diffusion properties are considered in the model: the diffusion for renal medulla is considered as anisotropic, while those of renal cortex and renal pelvis are considered as isotropic. The FEM is employed to simulate the diffusion model. Automated estimation of the model parameters is performed via optimization of an objective function reflecting overlap accuracy, which is optimized in parallel via HOPSPACK (hybrid optimization parallel search). An exponential curve fitting based on the non-linear least squares method is used for multi-time point model parameters prediction. The proposed method was tested on the seven time points longitudinal kidney tumor CT studies from two patients with five tumors. The experimental results showed the feasibility and efficacy of the proposed method. More... »

PAGES

159-168

References to SciGraph publications

  • 2007. A Coupled Finite Element Model of Tumor Growth and Vascularization in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2007
  • 1964-09. Dynamics of Tumor Growth in BRITISH JOURNAL OF CANCER
  • 2008-06. An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects in JOURNAL OF MATHEMATICAL BIOLOGY
  • 2001-10. A general model for ontogenetic growth in NATURE
  • 2005. Finite Element Modeling of Brain Tumor Mass-Effect from 3D Medical Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2005
  • Book

    TITLE

    Medical Imaging and Augmented Reality

    ISBN

    978-3-642-15698-4
    978-3-642-15699-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15699-1_17

    DOI

    http://dx.doi.org/10.1007/978-3-642-15699-1_17

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1023716526


    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/2008/1103", 
            "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": {
              "name": [
                "Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, 20814, Bethesda, MD, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chen", 
            "givenName": "Xinjian", 
            "id": "sg:person.01235623330.65", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235623330.65"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, 20814, Bethesda, MD, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Summers", 
            "givenName": "Ronald", 
            "id": "sg:person.011331054577.30", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011331054577.30"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, 20814, Bethesda, MD, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yao", 
            "givenName": "Jianhua", 
            "id": "sg:person.012366760067.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012366760067.46"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/1163641.1163647", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001835647"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1097/01.ju.0000102409.69570.f5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004400751"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1097/01.ju.0000102409.69570.f5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004400751"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jtbi.2005.08.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008434033"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jtbi.2005.08.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008434033"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1365-2184.1995.tb00036.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009163477"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jns.2003.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011026007"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00285-007-0139-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012292263", 
              "https://doi.org/10.1007/s00285-007-0139-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00285-007-0139-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012292263", 
              "https://doi.org/10.1007/s00285-007-0139-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1097/rli.0b013e31817d14e6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027437814"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1097/rli.0b013e31817d14e6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027437814"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11566465_50", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030322916", 
              "https://doi.org/10.1007/11566465_50"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11566465_50", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030322916", 
              "https://doi.org/10.1007/11566465_50"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0022-5193(03)00221-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030583333"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0022-5193(03)00221-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030583333"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/bjc.1964.55", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033603546", 
              "https://doi.org/10.1038/bjc.1964.55"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/bjc.1964.55", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033603546", 
              "https://doi.org/10.1038/bjc.1964.55"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/35098076", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041527409", 
              "https://doi.org/10.1038/35098076"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/35098076", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041527409", 
              "https://doi.org/10.1038/35098076"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.compmedimag.2005.12.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044421498"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cam.2003.12.035", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045529940"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-75759-7_106", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046070453", 
              "https://doi.org/10.1007/978-3-540-75759-7_106"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-75759-7_106", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046070453", 
              "https://doi.org/10.1007/978-3-540-75759-7_106"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/37401.37422", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051534960"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tbme.2008.925714", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061527596"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2005.857217", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061694777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2008.916954", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061695231"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9781611970920", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1098556262"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2010", 
        "datePublishedReg": "2010-01-01", 
        "description": "It is important to predict the tumor growth so that appropriate treatment can be planned especially in the early stage. In this paper, we propose a finite element method (FEM) based 3D tumor growth prediction system using longitudinal kidney tumor images. To the best of our knowledge, this is the first kidney tumor growth prediction system. The kidney tissues are classified into three types: renal cortex, renal medulla and renal pelvis. The reaction-diffusion model is applied as the tumor growth model. Different diffusion properties are considered in the model: the diffusion for renal medulla is considered as anisotropic, while those of renal cortex and renal pelvis are considered as isotropic. The FEM is employed to simulate the diffusion model. Automated estimation of the model parameters is performed via optimization of an objective function reflecting overlap accuracy, which is optimized in parallel via HOPSPACK (hybrid optimization parallel search). An exponential curve fitting based on the non-linear least squares method is used for multi-time point model parameters prediction. The proposed method was tested on the seven time points longitudinal kidney tumor CT studies from two patients with five tumors. The experimental results showed the feasibility and efficacy of the proposed method.", 
        "editor": [
          {
            "familyName": "Liao", 
            "givenName": "Hongen", 
            "type": "Person"
          }, 
          {
            "familyName": "Edwards", 
            "givenName": "P. J. \"Eddie\"", 
            "type": "Person"
          }, 
          {
            "familyName": "Pan", 
            "givenName": "Xiaochuan", 
            "type": "Person"
          }, 
          {
            "familyName": "Fan", 
            "givenName": "Yong", 
            "type": "Person"
          }, 
          {
            "familyName": "Yang", 
            "givenName": "Guang-Zhong", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-642-15699-1_17", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": {
          "isbn": [
            "978-3-642-15698-4", 
            "978-3-642-15699-1"
          ], 
          "name": "Medical Imaging and Augmented Reality", 
          "type": "Book"
        }, 
        "name": "FEM Based 3D Tumor Growth Prediction for Kidney Tumor", 
        "pagination": "159-168", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1023716526"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-642-15699-1_17"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "70fde062a4932f75fa4d313d57108af20136c0e777195859e7ce4fd7f754b3a2"
            ]
          }
        ], 
        "publisher": {
          "location": "Berlin, Heidelberg", 
          "name": "Springer Berlin Heidelberg", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-642-15699-1_17", 
          "https://app.dimensions.ai/details/publication/pub.1023716526"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T08:24", 
        "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/v90/0000000363_0000000363/records_70043_00000000.jsonl", 
        "type": "Chapter", 
        "url": "https://link.springer.com/10.1007%2F978-3-642-15699-1_17"
      }
    ]
     

    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.10.1007/978-3-642-15699-1_17'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15699-1_17'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15699-1_17'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-15699-1_17'


     

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

    164 TRIPLES      23 PREDICATES      46 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-642-15699-1_17 schema:about anzsrc-for:11
    2 anzsrc-for:1103
    3 schema:author N5d4a8c4095dd4dbfa57b188256da9a54
    4 schema:citation sg:pub.10.1007/11566465_50
    5 sg:pub.10.1007/978-3-540-75759-7_106
    6 sg:pub.10.1007/s00285-007-0139-x
    7 sg:pub.10.1038/35098076
    8 sg:pub.10.1038/bjc.1964.55
    9 https://doi.org/10.1016/j.cam.2003.12.035
    10 https://doi.org/10.1016/j.compmedimag.2005.12.001
    11 https://doi.org/10.1016/j.jns.2003.06.001
    12 https://doi.org/10.1016/j.jtbi.2005.08.002
    13 https://doi.org/10.1016/s0022-5193(03)00221-2
    14 https://doi.org/10.1097/01.ju.0000102409.69570.f5
    15 https://doi.org/10.1097/rli.0b013e31817d14e6
    16 https://doi.org/10.1109/tbme.2008.925714
    17 https://doi.org/10.1109/tmi.2005.857217
    18 https://doi.org/10.1109/tmi.2008.916954
    19 https://doi.org/10.1111/j.1365-2184.1995.tb00036.x
    20 https://doi.org/10.1137/1.9781611970920
    21 https://doi.org/10.1145/1163641.1163647
    22 https://doi.org/10.1145/37401.37422
    23 schema:datePublished 2010
    24 schema:datePublishedReg 2010-01-01
    25 schema:description It is important to predict the tumor growth so that appropriate treatment can be planned especially in the early stage. In this paper, we propose a finite element method (FEM) based 3D tumor growth prediction system using longitudinal kidney tumor images. To the best of our knowledge, this is the first kidney tumor growth prediction system. The kidney tissues are classified into three types: renal cortex, renal medulla and renal pelvis. The reaction-diffusion model is applied as the tumor growth model. Different diffusion properties are considered in the model: the diffusion for renal medulla is considered as anisotropic, while those of renal cortex and renal pelvis are considered as isotropic. The FEM is employed to simulate the diffusion model. Automated estimation of the model parameters is performed via optimization of an objective function reflecting overlap accuracy, which is optimized in parallel via HOPSPACK (hybrid optimization parallel search). An exponential curve fitting based on the non-linear least squares method is used for multi-time point model parameters prediction. The proposed method was tested on the seven time points longitudinal kidney tumor CT studies from two patients with five tumors. The experimental results showed the feasibility and efficacy of the proposed method.
    26 schema:editor N05325f54502640cab88453df477e0006
    27 schema:genre chapter
    28 schema:inLanguage en
    29 schema:isAccessibleForFree true
    30 schema:isPartOf Na4eec3ff0c3642c681f1ce6f626ed082
    31 schema:name FEM Based 3D Tumor Growth Prediction for Kidney Tumor
    32 schema:pagination 159-168
    33 schema:productId N322907f027c54dbdaf6339dbb0ff67f3
    34 N7908a3dce1f64da5a678539fdd553f6b
    35 N7c11fa9d9b6f4e73a566f5bfca7fa9ab
    36 schema:publisher Na4fa45459ef5467d8fe7834f2e2ebf01
    37 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023716526
    38 https://doi.org/10.1007/978-3-642-15699-1_17
    39 schema:sdDatePublished 2019-04-16T08:24
    40 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    41 schema:sdPublisher N0624f2f00a1b4582baa8a065cec05c01
    42 schema:url https://link.springer.com/10.1007%2F978-3-642-15699-1_17
    43 sgo:license sg:explorer/license/
    44 sgo:sdDataset chapters
    45 rdf:type schema:Chapter
    46 N05325f54502640cab88453df477e0006 rdf:first Nbccb17686c7842b78aab8ed1f1425ecc
    47 rdf:rest N430279c4caa345739d80dfbe40ca6deb
    48 N0624f2f00a1b4582baa8a065cec05c01 schema:name Springer Nature - SN SciGraph project
    49 rdf:type schema:Organization
    50 N2187c3b014e145ecb917ea7abf114cf0 rdf:first N4cce4d8176e14c00b1ff862cdda2a8a4
    51 rdf:rest rdf:nil
    52 N322907f027c54dbdaf6339dbb0ff67f3 schema:name dimensions_id
    53 schema:value pub.1023716526
    54 rdf:type schema:PropertyValue
    55 N323ba9b2a1a44ab9b2df71cbcc044dc9 rdf:first Nfd76125ffaac4f2f817d6d830e6947be
    56 rdf:rest N9832e84daa25474b933c23221691078e
    57 N36dff84500994df69c8c1bc4884c425c rdf:first sg:person.012366760067.46
    58 rdf:rest rdf:nil
    59 N430279c4caa345739d80dfbe40ca6deb rdf:first Na14d712feaea4223b28453b90b0e648f
    60 rdf:rest N323ba9b2a1a44ab9b2df71cbcc044dc9
    61 N4cce4d8176e14c00b1ff862cdda2a8a4 schema:familyName Yang
    62 schema:givenName Guang-Zhong
    63 rdf:type schema:Person
    64 N5d4a8c4095dd4dbfa57b188256da9a54 rdf:first sg:person.01235623330.65
    65 rdf:rest N6b709110eb6943c191a80087c8ed8b8b
    66 N6b709110eb6943c191a80087c8ed8b8b rdf:first sg:person.011331054577.30
    67 rdf:rest N36dff84500994df69c8c1bc4884c425c
    68 N76fe4f0292f84703a1e4256afe7db053 schema:familyName Fan
    69 schema:givenName Yong
    70 rdf:type schema:Person
    71 N7908a3dce1f64da5a678539fdd553f6b schema:name doi
    72 schema:value 10.1007/978-3-642-15699-1_17
    73 rdf:type schema:PropertyValue
    74 N7c11fa9d9b6f4e73a566f5bfca7fa9ab schema:name readcube_id
    75 schema:value 70fde062a4932f75fa4d313d57108af20136c0e777195859e7ce4fd7f754b3a2
    76 rdf:type schema:PropertyValue
    77 N9832e84daa25474b933c23221691078e rdf:first N76fe4f0292f84703a1e4256afe7db053
    78 rdf:rest N2187c3b014e145ecb917ea7abf114cf0
    79 Na0e095fa21be4aa4a0b95d22f6330976 schema:name Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, 20814, Bethesda, MD, USA
    80 rdf:type schema:Organization
    81 Na14d712feaea4223b28453b90b0e648f schema:familyName Edwards
    82 schema:givenName P. J. "Eddie"
    83 rdf:type schema:Person
    84 Na4eec3ff0c3642c681f1ce6f626ed082 schema:isbn 978-3-642-15698-4
    85 978-3-642-15699-1
    86 schema:name Medical Imaging and Augmented Reality
    87 rdf:type schema:Book
    88 Na4fa45459ef5467d8fe7834f2e2ebf01 schema:location Berlin, Heidelberg
    89 schema:name Springer Berlin Heidelberg
    90 rdf:type schema:Organisation
    91 Nbccb17686c7842b78aab8ed1f1425ecc schema:familyName Liao
    92 schema:givenName Hongen
    93 rdf:type schema:Person
    94 Nda6d6b3e970c4d65b6dcf86d87e30ea0 schema:name Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, 20814, Bethesda, MD, USA
    95 rdf:type schema:Organization
    96 Ne5e3b1e716d044a5a09fb82d648131a2 schema:name Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, 20814, Bethesda, MD, USA
    97 rdf:type schema:Organization
    98 Nfd76125ffaac4f2f817d6d830e6947be schema:familyName Pan
    99 schema:givenName Xiaochuan
    100 rdf:type schema:Person
    101 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    102 schema:name Medical and Health Sciences
    103 rdf:type schema:DefinedTerm
    104 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
    105 schema:name Clinical Sciences
    106 rdf:type schema:DefinedTerm
    107 sg:person.011331054577.30 schema:affiliation Na0e095fa21be4aa4a0b95d22f6330976
    108 schema:familyName Summers
    109 schema:givenName Ronald
    110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011331054577.30
    111 rdf:type schema:Person
    112 sg:person.01235623330.65 schema:affiliation Ne5e3b1e716d044a5a09fb82d648131a2
    113 schema:familyName Chen
    114 schema:givenName Xinjian
    115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235623330.65
    116 rdf:type schema:Person
    117 sg:person.012366760067.46 schema:affiliation Nda6d6b3e970c4d65b6dcf86d87e30ea0
    118 schema:familyName Yao
    119 schema:givenName Jianhua
    120 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012366760067.46
    121 rdf:type schema:Person
    122 sg:pub.10.1007/11566465_50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030322916
    123 https://doi.org/10.1007/11566465_50
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/978-3-540-75759-7_106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046070453
    126 https://doi.org/10.1007/978-3-540-75759-7_106
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1007/s00285-007-0139-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1012292263
    129 https://doi.org/10.1007/s00285-007-0139-x
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1038/35098076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041527409
    132 https://doi.org/10.1038/35098076
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1038/bjc.1964.55 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033603546
    135 https://doi.org/10.1038/bjc.1964.55
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1016/j.cam.2003.12.035 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045529940
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1016/j.compmedimag.2005.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044421498
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1016/j.jns.2003.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011026007
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1016/j.jtbi.2005.08.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008434033
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1016/s0022-5193(03)00221-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030583333
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1097/01.ju.0000102409.69570.f5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004400751
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1097/rli.0b013e31817d14e6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027437814
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1109/tbme.2008.925714 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061527596
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1109/tmi.2005.857217 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694777
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1109/tmi.2008.916954 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695231
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1111/j.1365-2184.1995.tb00036.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1009163477
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1137/1.9781611970920 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098556262
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1145/1163641.1163647 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001835647
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1145/37401.37422 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051534960
    164 rdf:type schema:CreativeWork
     




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


    ...