Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2009-07

AUTHORS

N. Boussion, C. Cheze Le Rest, M. Hatt, D. Visvikis

ABSTRACT

PURPOSE: Partial volume effects (PVEs) are consequences of the limited resolution of emission tomography. The aim of the present study was to compare two new voxel-wise PVE correction algorithms based on deconvolution and wavelet-based denoising. MATERIALS AND METHODS: Deconvolution was performed using the Lucy-Richardson and the Van-Cittert algorithms. Both of these methods were tested using simulated and real FDG PET images. Wavelet-based denoising was incorporated into the process in order to eliminate the noise observed in classical deconvolution methods. RESULTS: Both deconvolution approaches led to significant intensity recovery, but the Van-Cittert algorithm provided images of inferior qualitative appearance. Furthermore, this method added massive levels of noise, even with the associated use of wavelet-denoising. On the other hand, the Lucy-Richardson algorithm combined with the same denoising process gave the best compromise between intensity recovery, noise attenuation and qualitative aspect of the images. CONCLUSION: The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography. More... »

PAGES

1064-1075

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00259-009-1065-5

DOI

http://dx.doi.org/10.1007/s00259-009-1065-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052639217

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19224209


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/2008/0801", 
        "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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fluorodeoxyglucose F18", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Image Processing, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Positron-Emission Tomography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Whole Body Imaging", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "INSERM, U650, Laboratoire de Traitement de l\u2019Information M\u00e9dicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boussion", 
        "givenName": "N.", 
        "id": "sg:person.0721543407.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0721543407.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "INSERM, U650, Laboratoire de Traitement de l\u2019Information M\u00e9dicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cheze Le Rest", 
        "givenName": "C.", 
        "id": "sg:person.012576527021.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012576527021.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "INSERM, U650, Laboratoire de Traitement de l\u2019Information M\u00e9dicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hatt", 
        "givenName": "M.", 
        "id": "sg:person.01202724075.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01202724075.78"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "INSERM, U650, Laboratoire de Traitement de l\u2019Information M\u00e9dicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Visvikis", 
        "givenName": "D.", 
        "id": "sg:person.01255045106.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01255045106.49"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00259-007-0454-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003502621", 
          "https://doi.org/10.1007/s00259-007-0454-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2007.10.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005425955"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2967/jnumed.106.035774", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005435708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/81.3.425", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016957463"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2967/jnumed.107.046136", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017943586"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2006.03.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026254228"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2007.04.048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035621842"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2967/jnumed.108.050401", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037285767"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neurobiolaging.2007.05.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037329759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01391351", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044785439", 
          "https://doi.org/10.1007/bf01391351"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2967/jnumed.107.048330", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048520989"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/111605", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058450184"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/51/7/016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059026484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/51/7/016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059026484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0031-9155/53/10/009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059027096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/18.382009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061099553"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.192463", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061155760"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/78.157290", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061228108"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/83.862633", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061240184"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2006.887733", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061641634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2008.2001404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061641931"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2003.809691", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061694404"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1364/josa.62.000055", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065152408"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077412763", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083263153", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511564352", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098709070"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-07", 
    "datePublishedReg": "2009-07-01", 
    "description": "PURPOSE: Partial volume effects (PVEs) are consequences of the limited resolution of emission tomography. The aim of the present study was to compare two new voxel-wise PVE correction algorithms based on deconvolution and wavelet-based denoising.\nMATERIALS AND METHODS: Deconvolution was performed using the Lucy-Richardson and the Van-Cittert algorithms. Both of these methods were tested using simulated and real FDG PET images. Wavelet-based denoising was incorporated into the process in order to eliminate the noise observed in classical deconvolution methods.\nRESULTS: Both deconvolution approaches led to significant intensity recovery, but the Van-Cittert algorithm provided images of inferior qualitative appearance. Furthermore, this method added massive levels of noise, even with the associated use of wavelet-denoising. On the other hand, the Lucy-Richardson algorithm combined with the same denoising process gave the best compromise between intensity recovery, noise attenuation and qualitative aspect of the images.\nCONCLUSION: The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00259-009-1065-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297401", 
        "issn": [
          "1619-7070", 
          "1619-7089"
        ], 
        "name": "European Journal of Nuclear Medicine and Molecular Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "36"
      }
    ], 
    "name": "Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging", 
    "pagination": "1064-1075", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "fabcb23d13660d5d96631f44c609a019e6a3ac8c2b29783b702ab26bd4c5d534"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "19224209"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101140988"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00259-009-1065-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1052639217"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00259-009-1065-5", 
      "https://app.dimensions.ai/details/publication/pub.1052639217"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:30", 
    "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/v90/0000000373_0000000373/records_13090_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs00259-009-1065-5"
  }
]
 

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/s00259-009-1065-5'

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/s00259-009-1065-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00259-009-1065-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00259-009-1065-5'


 

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

198 TRIPLES      21 PREDICATES      61 URIs      28 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00259-009-1065-5 schema:about N49fce3ed96534d5f8f1908842a35abfa
2 N655ed6279ddc4a929d21e28913f4e2f2
3 N7f19313b5b4b4b55b1c90ece84e2b3b9
4 N9be9006e8a6e4de3b5cc3e5a3e185c73
5 Na8bb6aedcf854d9b83ae45c176b5155e
6 Nc641c4f07f674bc3b18d3606c6e0da2e
7 Nefa8e9e637984f4c91accd53a27a0168
8 anzsrc-for:08
9 anzsrc-for:0801
10 schema:author N5883025b2401457a99e1a0c29930c5a8
11 schema:citation sg:pub.10.1007/bf01391351
12 sg:pub.10.1007/s00259-007-0454-x
13 https://app.dimensions.ai/details/publication/pub.1077412763
14 https://app.dimensions.ai/details/publication/pub.1083263153
15 https://doi.org/10.1016/j.neurobiolaging.2007.05.019
16 https://doi.org/10.1016/j.neuroimage.2006.03.002
17 https://doi.org/10.1016/j.neuroimage.2007.04.048
18 https://doi.org/10.1016/j.neuroimage.2007.10.038
19 https://doi.org/10.1017/cbo9780511564352
20 https://doi.org/10.1086/111605
21 https://doi.org/10.1088/0031-9155/51/7/016
22 https://doi.org/10.1088/0031-9155/53/10/009
23 https://doi.org/10.1093/biomet/81.3.425
24 https://doi.org/10.1109/18.382009
25 https://doi.org/10.1109/34.192463
26 https://doi.org/10.1109/78.157290
27 https://doi.org/10.1109/83.862633
28 https://doi.org/10.1109/tip.2006.887733
29 https://doi.org/10.1109/tip.2008.2001404
30 https://doi.org/10.1109/tmi.2003.809691
31 https://doi.org/10.1364/josa.62.000055
32 https://doi.org/10.2967/jnumed.106.035774
33 https://doi.org/10.2967/jnumed.107.046136
34 https://doi.org/10.2967/jnumed.107.048330
35 https://doi.org/10.2967/jnumed.108.050401
36 schema:datePublished 2009-07
37 schema:datePublishedReg 2009-07-01
38 schema:description PURPOSE: Partial volume effects (PVEs) are consequences of the limited resolution of emission tomography. The aim of the present study was to compare two new voxel-wise PVE correction algorithms based on deconvolution and wavelet-based denoising. MATERIALS AND METHODS: Deconvolution was performed using the Lucy-Richardson and the Van-Cittert algorithms. Both of these methods were tested using simulated and real FDG PET images. Wavelet-based denoising was incorporated into the process in order to eliminate the noise observed in classical deconvolution methods. RESULTS: Both deconvolution approaches led to significant intensity recovery, but the Van-Cittert algorithm provided images of inferior qualitative appearance. Furthermore, this method added massive levels of noise, even with the associated use of wavelet-denoising. On the other hand, the Lucy-Richardson algorithm combined with the same denoising process gave the best compromise between intensity recovery, noise attenuation and qualitative aspect of the images. CONCLUSION: The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree false
42 schema:isPartOf N51fa765cb3474bf9bcf83a6d01170864
43 N721757a79d1c478eafe1bd363581b155
44 sg:journal.1297401
45 schema:name Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging
46 schema:pagination 1064-1075
47 schema:productId N424507bf1e8f47f7824384f88806e1fd
48 N4abbfaf61513468cb113bb0db752b117
49 N97fdf05dbf584fe7994c19982ba24a2d
50 Na8f32b1f819244adb7863942081b920d
51 Nd71e6b913b5044ae92cee2416916b863
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052639217
53 https://doi.org/10.1007/s00259-009-1065-5
54 schema:sdDatePublished 2019-04-11T14:30
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher Nf027a794c9524f22bc301140b578dbe4
57 schema:url http://link.springer.com/10.1007%2Fs00259-009-1065-5
58 sgo:license sg:explorer/license/
59 sgo:sdDataset articles
60 rdf:type schema:ScholarlyArticle
61 N0c83ada0e2c24e9e88d9a8f9b385d7e8 schema:name INSERM, U650, Laboratoire de Traitement de l’Information Médicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France
62 rdf:type schema:Organization
63 N1a2536b94b7f4d908e734cad195df0f2 schema:name INSERM, U650, Laboratoire de Traitement de l’Information Médicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France
64 rdf:type schema:Organization
65 N3fed3c187a1544daa475d474a22fae70 schema:name INSERM, U650, Laboratoire de Traitement de l’Information Médicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France
66 rdf:type schema:Organization
67 N424507bf1e8f47f7824384f88806e1fd schema:name pubmed_id
68 schema:value 19224209
69 rdf:type schema:PropertyValue
70 N49fce3ed96534d5f8f1908842a35abfa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
71 schema:name Positron-Emission Tomography
72 rdf:type schema:DefinedTerm
73 N4abbfaf61513468cb113bb0db752b117 schema:name nlm_unique_id
74 schema:value 101140988
75 rdf:type schema:PropertyValue
76 N51fa765cb3474bf9bcf83a6d01170864 schema:issueNumber 7
77 rdf:type schema:PublicationIssue
78 N550d4f0436e4469aaa74e4394c0b971f schema:name INSERM, U650, Laboratoire de Traitement de l’Information Médicale (LaTIM) CHU MORVAN, Bat 2bis (I3S), 5 avenue Foch, 29609, Brest, France
79 rdf:type schema:Organization
80 N5883025b2401457a99e1a0c29930c5a8 rdf:first sg:person.0721543407.12
81 rdf:rest Na62e6e4b284146e38adbb9b43802bbf5
82 N655ed6279ddc4a929d21e28913f4e2f2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Fluorodeoxyglucose F18
84 rdf:type schema:DefinedTerm
85 N721757a79d1c478eafe1bd363581b155 schema:volumeNumber 36
86 rdf:type schema:PublicationVolume
87 N7ae1f7511bb043028a03b6c1fd91404e rdf:first sg:person.01255045106.49
88 rdf:rest rdf:nil
89 N7f19313b5b4b4b55b1c90ece84e2b3b9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Algorithms
91 rdf:type schema:DefinedTerm
92 N97fdf05dbf584fe7994c19982ba24a2d schema:name readcube_id
93 schema:value fabcb23d13660d5d96631f44c609a019e6a3ac8c2b29783b702ab26bd4c5d534
94 rdf:type schema:PropertyValue
95 N9be9006e8a6e4de3b5cc3e5a3e185c73 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Sensitivity and Specificity
97 rdf:type schema:DefinedTerm
98 Na62e6e4b284146e38adbb9b43802bbf5 rdf:first sg:person.012576527021.82
99 rdf:rest Nfbc6eaa120134955bd2e23bc7328b84f
100 Na8bb6aedcf854d9b83ae45c176b5155e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Image Processing, Computer-Assisted
102 rdf:type schema:DefinedTerm
103 Na8f32b1f819244adb7863942081b920d schema:name doi
104 schema:value 10.1007/s00259-009-1065-5
105 rdf:type schema:PropertyValue
106 Nc641c4f07f674bc3b18d3606c6e0da2e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
107 schema:name Humans
108 rdf:type schema:DefinedTerm
109 Nd71e6b913b5044ae92cee2416916b863 schema:name dimensions_id
110 schema:value pub.1052639217
111 rdf:type schema:PropertyValue
112 Nefa8e9e637984f4c91accd53a27a0168 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Whole Body Imaging
114 rdf:type schema:DefinedTerm
115 Nf027a794c9524f22bc301140b578dbe4 schema:name Springer Nature - SN SciGraph project
116 rdf:type schema:Organization
117 Nfbc6eaa120134955bd2e23bc7328b84f rdf:first sg:person.01202724075.78
118 rdf:rest N7ae1f7511bb043028a03b6c1fd91404e
119 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
120 schema:name Information and Computing Sciences
121 rdf:type schema:DefinedTerm
122 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
123 schema:name Artificial Intelligence and Image Processing
124 rdf:type schema:DefinedTerm
125 sg:journal.1297401 schema:issn 1619-7070
126 1619-7089
127 schema:name European Journal of Nuclear Medicine and Molecular Imaging
128 rdf:type schema:Periodical
129 sg:person.01202724075.78 schema:affiliation N0c83ada0e2c24e9e88d9a8f9b385d7e8
130 schema:familyName Hatt
131 schema:givenName M.
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01202724075.78
133 rdf:type schema:Person
134 sg:person.01255045106.49 schema:affiliation N550d4f0436e4469aaa74e4394c0b971f
135 schema:familyName Visvikis
136 schema:givenName D.
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01255045106.49
138 rdf:type schema:Person
139 sg:person.012576527021.82 schema:affiliation N1a2536b94b7f4d908e734cad195df0f2
140 schema:familyName Cheze Le Rest
141 schema:givenName C.
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012576527021.82
143 rdf:type schema:Person
144 sg:person.0721543407.12 schema:affiliation N3fed3c187a1544daa475d474a22fae70
145 schema:familyName Boussion
146 schema:givenName N.
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0721543407.12
148 rdf:type schema:Person
149 sg:pub.10.1007/bf01391351 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044785439
150 https://doi.org/10.1007/bf01391351
151 rdf:type schema:CreativeWork
152 sg:pub.10.1007/s00259-007-0454-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1003502621
153 https://doi.org/10.1007/s00259-007-0454-x
154 rdf:type schema:CreativeWork
155 https://app.dimensions.ai/details/publication/pub.1077412763 schema:CreativeWork
156 https://app.dimensions.ai/details/publication/pub.1083263153 schema:CreativeWork
157 https://doi.org/10.1016/j.neurobiolaging.2007.05.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037329759
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/j.neuroimage.2006.03.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026254228
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.neuroimage.2007.04.048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035621842
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.neuroimage.2007.10.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005425955
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1017/cbo9780511564352 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098709070
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1086/111605 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058450184
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1088/0031-9155/51/7/016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059026484
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1088/0031-9155/53/10/009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059027096
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1093/biomet/81.3.425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016957463
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1109/18.382009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061099553
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1109/34.192463 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061155760
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1109/78.157290 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061228108
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1109/83.862633 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061240184
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1109/tip.2006.887733 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061641634
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1109/tip.2008.2001404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061641931
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1109/tmi.2003.809691 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694404
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1364/josa.62.000055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065152408
190 rdf:type schema:CreativeWork
191 https://doi.org/10.2967/jnumed.106.035774 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005435708
192 rdf:type schema:CreativeWork
193 https://doi.org/10.2967/jnumed.107.046136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017943586
194 rdf:type schema:CreativeWork
195 https://doi.org/10.2967/jnumed.107.048330 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048520989
196 rdf:type schema:CreativeWork
197 https://doi.org/10.2967/jnumed.108.050401 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037285767
198 rdf:type schema:CreativeWork
 




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


...