Implementation and evaluation of an ordered subsets reconstruction algorithm for transmission PET studies using median root prior and inter-update median ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-12-14

AUTHORS

V. Bettinardi, S. Alenius, P. Numminen, M. Teräs, M. Gilardi, F. Fazio, U. Ruotsalainen

ABSTRACT

. An ordered subsets (OS) reconstruction algorithm based on the median root prior (MRP) and inter-update median filtering was implemented for the reconstruction of low count statistics transmission (TR) scans. The OS-MRP-TR algorithm was evaluated using an experimental phantom, simulating positron emission tomography (PET) whole-body (WB) studies, as well as patient data. Various experimental conditions, in terms of TR scan time (from 1 h to 1 min), covering a wide range of TR count statistics were evaluated. The performance of the algorithm was assessed by comparing the mean value of the attenuation coefficient (MVAC) of known tissue types and the coefficient of variation (CV) for low-count TR images, reconstructed with the OS-MRP-TR algorithm, with reference values obtained from high-count TR images reconstructed with a filtered back-projection (FBP) algorithm. The reconstructed OS-MRP-TR images were then used for attenuation correction of the corresponding emission (EM) data. EM images reconstructed with attenuation correction generated by OS-MRP-TR images, of low count statistics, were compared with the EM images corrected for attenuation using reference (high statistics) TR data. In all the experimental situations considered, the OS-MRP-TR algorithm showed: (1) a tendency towards a stable solution in terms of MVAC; (2) a difference in the MVAC of within 5% for a TR scan of 1 min reconstructed with the OS-MRP-TR and a TR scan of 1 h reconstructed with the FBP algorithm; (3) effectiveness in noise reduction, particularly for low count statistics data [using a specific parameter configuration the TR images reconstructed with OS-MRP-TR(1 min) had a lower CV than the corresponding TR images of a 1-h scan reconstructed with the FBP algorithm]; (4) a difference of within 3% between the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1 min and the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1 h; (5) preservation of "good" image quality for both TR and EM reconstructed images. In conclusion, the OS-MRP-TR algorithm is particularly suitable for WB PET studies, allowing: (1) the acquisition of a very short TR scan (within 1 min), (2) the reconstruction of such TR data in low-noise TR images and (3) the use of the reconstructed OS-MRP-TR images for attenuation correction of corresponding EM data. More... »

PAGES

222-231

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00259-002-1046-4

DOI

http://dx.doi.org/10.1007/s00259-002-1046-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048628606

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/12552340


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/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical 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/2008/0299", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Physical 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": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artifacts", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Heart", 
        "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": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neoplasms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Phantoms, Imaging", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Quality Control", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stochastic Processes", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Thorax", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, Emission-Computed", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Whole-Body Counting", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bettinardi", 
        "givenName": "V.", 
        "id": "sg:person.0621001075.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621001075.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tampere University of Technology, Institute of Signal Processing, Finland", 
          "id": "http://www.grid.ac/institutes/grid.502801.e", 
          "name": [
            "Tampere University of Technology, Institute of Signal Processing, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Alenius", 
        "givenName": "S.", 
        "id": "sg:person.015426704241.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015426704241.73"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Turku University Central Hospital, Finland", 
          "id": "http://www.grid.ac/institutes/grid.410552.7", 
          "name": [
            "Turku University Central Hospital, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Numminen", 
        "givenName": "P.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Turku University Central Hospital, Finland", 
          "id": "http://www.grid.ac/institutes/grid.410552.7", 
          "name": [
            "Turku University Central Hospital, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ter\u00e4s", 
        "givenName": "M.", 
        "id": "sg:person.01101061462.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101061462.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gilardi", 
        "givenName": "M.", 
        "id": "sg:person.01262435052.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01262435052.90"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fazio", 
        "givenName": "F.", 
        "id": "sg:person.01234705530.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01234705530.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tampere University of Technology, Institute of Signal Processing, Finland", 
          "id": "http://www.grid.ac/institutes/grid.502801.e", 
          "name": [
            "Tampere University of Technology, Institute of Signal Processing, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ruotsalainen", 
        "givenName": "U.", 
        "id": "sg:person.01340666300.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01340666300.40"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s002590050410", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014377698", 
          "https://doi.org/10.1007/s002590050410"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s002590100651", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023839468", 
          "https://doi.org/10.1007/s002590100651"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s002590050219", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018061321", 
          "https://doi.org/10.1007/s002590050219"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2002-12-14", 
    "datePublishedReg": "2002-12-14", 
    "description": "Abstract. An ordered subsets (OS) reconstruction algorithm based on the median root prior (MRP) and inter-update median filtering was implemented for the reconstruction of low count statistics transmission (TR) scans. The OS-MRP-TR algorithm was evaluated using an experimental phantom, simulating positron emission tomography (PET) whole-body (WB) studies, as well as patient data. Various experimental conditions, in terms of TR scan time (from 1\u00a0h to 1\u00a0min), covering a wide range of TR count statistics were evaluated. The performance of the algorithm was assessed by comparing the mean value of the attenuation coefficient (MVAC) of known tissue types and the coefficient of variation (CV) for low-count TR images, reconstructed with the OS-MRP-TR algorithm, with reference values obtained from high-count TR images reconstructed with a filtered back-projection (FBP) algorithm. The reconstructed OS-MRP-TR images were then used for attenuation correction of the corresponding emission (EM) data. EM images reconstructed with attenuation correction generated by OS-MRP-TR images, of low count statistics, were compared with the EM images corrected for attenuation using reference (high statistics) TR data. In all the experimental situations considered, the OS-MRP-TR algorithm showed: (1) a tendency towards a stable solution in terms of MVAC; (2) a difference in the MVAC of within 5% for a TR scan of 1\u00a0min reconstructed with the OS-MRP-TR and a TR scan of 1\u00a0h reconstructed with the FBP algorithm; (3) effectiveness in noise reduction, particularly for low count statistics data [using a specific parameter configuration the TR images reconstructed with OS-MRP-TR(1\u00a0min) had a lower CV than the corresponding TR images of a 1-h scan reconstructed with the FBP algorithm]; (4) a difference of within 3% between the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1\u00a0min and the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1\u00a0h; (5) preservation of \"good\" image quality for both TR and EM reconstructed images. In conclusion, the OS-MRP-TR algorithm is particularly suitable for WB PET studies, allowing: (1) the acquisition of a very short TR scan (within 1\u00a0min), (2) the reconstruction of such TR data in low-noise TR images and (3) the use of the reconstructed OS-MRP-TR images for attenuation correction of corresponding EM data.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00259-002-1046-4", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297401", 
        "issn": [
          "1619-7070", 
          "1619-7089"
        ], 
        "name": "European Journal of Nuclear Medicine and Molecular Imaging", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "30"
      }
    ], 
    "keywords": [
      "subsets reconstruction algorithm", 
      "TR algorithm", 
      "reconstruction algorithm", 
      "median filtering", 
      "median root", 
      "back-projection algorithm", 
      "filtered back-projection algorithm", 
      "attenuation correction", 
      "EM images", 
      "FBP algorithm", 
      "algorithm", 
      "image quality", 
      "images", 
      "low count statistics", 
      "filtering", 
      "transmission scans", 
      "whole-body studies", 
      "patient data", 
      "count statistics", 
      "TR data", 
      "noise reduction", 
      "image attenuation", 
      "experimental phantom", 
      "scan time", 
      "EM data", 
      "reconstruction", 
      "implementation", 
      "data", 
      "performance", 
      "stable solutions", 
      "effectiveness", 
      "acquisition", 
      "phantom", 
      "terms", 
      "wide range", 
      "statistics", 
      "TR images", 
      "situation", 
      "solution", 
      "TR scans", 
      "statistics data", 
      "quality", 
      "PET studies", 
      "time", 
      "correction", 
      "emission data", 
      "mean counts", 
      "EM", 
      "short TR scans", 
      "evaluation", 
      "scans", 
      "coefficient of variation", 
      "MVAC", 
      "use", 
      "study", 
      "mean value", 
      "values", 
      "attenuation coefficient", 
      "coefficient", 
      "tissue types", 
      "types", 
      "reference values", 
      "min", 
      "counts", 
      "preservation", 
      "experimental conditions", 
      "range", 
      "attenuation", 
      "differences", 
      "reduction", 
      "conclusion", 
      "roots", 
      "conditions", 
      "variation", 
      "experimental situations", 
      "tendency", 
      "TR", 
      "inter-update median filtering", 
      "low count statistics transmission (TR) scans", 
      "count statistics transmission (TR) scans", 
      "statistics transmission (TR) scans", 
      "OS-MRP", 
      "positron emission tomography (PET) whole-body (WB) studies", 
      "emission tomography (PET) whole-body (WB) studies", 
      "tomography (PET) whole-body (WB) studies", 
      "TR scan time", 
      "TR count statistics", 
      "low-count TR images", 
      "high-count TR images", 
      "reconstructed OS-MRP", 
      "corresponding emission (EM) data", 
      "reference (high statistics) TR data", 
      "terms of MVAC", 
      "low count statistics data", 
      "count statistics data", 
      "EM images attenuation", 
      "WB PET studies", 
      "such TR data", 
      "low-noise TR images", 
      "transmission PET studies"
    ], 
    "name": "Implementation and evaluation of an ordered subsets reconstruction algorithm for transmission PET studies using median root prior and inter-update median filtering", 
    "pagination": "222-231", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1048628606"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00259-002-1046-4"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "12552340"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00259-002-1046-4", 
      "https://app.dimensions.ai/details/publication/pub.1048628606"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-12-01T19:13", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/article/article_352.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00259-002-1046-4"
  }
]
 

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/s00259-002-1046-4'

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/s00259-002-1046-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00259-002-1046-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00259-002-1046-4'


 

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

285 TRIPLES      22 PREDICATES      145 URIs      132 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00259-002-1046-4 schema:about N0179217057974ddda08e01f521af72e3
2 N0f321180b09443c79c7c376b4673312c
3 N1f9c0db59a574e418fc43697374dabbe
4 N35e3bcdba3854679a632a1a4432c7fd4
5 N4f74cae9b97b49f7a65e77e2d2538fc4
6 N510b8c3b05964b18b05e3fd324d47bcd
7 N6006014b8797471f92b6a75746ca7635
8 N7f28375cd066479bb1e0efd8829dfda2
9 Naad67b1d19794c8a842bfa454e4d0833
10 Nb1b3fe5d8abd4675baee1de86d02a7cc
11 Nc3e00897383749588ee413250bd2d590
12 Nd2fc0ebcc32946569d8dcd0c5d95b36d
13 Nd3011e48550645809ba7230d25dc49b9
14 Nf850aa013b764f2d90ba1ee8af541cf1
15 anzsrc-for:02
16 anzsrc-for:0299
17 anzsrc-for:11
18 anzsrc-for:1103
19 schema:author N4e1f19c94a964b86aeed6e1ccf2381f8
20 schema:citation sg:pub.10.1007/s002590050219
21 sg:pub.10.1007/s002590050410
22 sg:pub.10.1007/s002590100651
23 schema:datePublished 2002-12-14
24 schema:datePublishedReg 2002-12-14
25 schema:description Abstract. An ordered subsets (OS) reconstruction algorithm based on the median root prior (MRP) and inter-update median filtering was implemented for the reconstruction of low count statistics transmission (TR) scans. The OS-MRP-TR algorithm was evaluated using an experimental phantom, simulating positron emission tomography (PET) whole-body (WB) studies, as well as patient data. Various experimental conditions, in terms of TR scan time (from 1 h to 1 min), covering a wide range of TR count statistics were evaluated. The performance of the algorithm was assessed by comparing the mean value of the attenuation coefficient (MVAC) of known tissue types and the coefficient of variation (CV) for low-count TR images, reconstructed with the OS-MRP-TR algorithm, with reference values obtained from high-count TR images reconstructed with a filtered back-projection (FBP) algorithm. The reconstructed OS-MRP-TR images were then used for attenuation correction of the corresponding emission (EM) data. EM images reconstructed with attenuation correction generated by OS-MRP-TR images, of low count statistics, were compared with the EM images corrected for attenuation using reference (high statistics) TR data. In all the experimental situations considered, the OS-MRP-TR algorithm showed: (1) a tendency towards a stable solution in terms of MVAC; (2) a difference in the MVAC of within 5% for a TR scan of 1 min reconstructed with the OS-MRP-TR and a TR scan of 1 h reconstructed with the FBP algorithm; (3) effectiveness in noise reduction, particularly for low count statistics data [using a specific parameter configuration the TR images reconstructed with OS-MRP-TR(1 min) had a lower CV than the corresponding TR images of a 1-h scan reconstructed with the FBP algorithm]; (4) a difference of within 3% between the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1 min and the mean counts in the EM images attenuation corrected using the OS-MRP-TR images of 1 h; (5) preservation of "good" image quality for both TR and EM reconstructed images. In conclusion, the OS-MRP-TR algorithm is particularly suitable for WB PET studies, allowing: (1) the acquisition of a very short TR scan (within 1 min), (2) the reconstruction of such TR data in low-noise TR images and (3) the use of the reconstructed OS-MRP-TR images for attenuation correction of corresponding EM data.
26 schema:genre article
27 schema:inLanguage en
28 schema:isAccessibleForFree false
29 schema:isPartOf N5d95e2634ac946249267cacbb1acaf3d
30 Na9b0f45456544ed2bf0e911590afd7f5
31 sg:journal.1297401
32 schema:keywords EM
33 EM data
34 EM images
35 EM images attenuation
36 FBP algorithm
37 MVAC
38 OS-MRP
39 PET studies
40 TR
41 TR algorithm
42 TR count statistics
43 TR data
44 TR images
45 TR scan time
46 TR scans
47 WB PET studies
48 acquisition
49 algorithm
50 attenuation
51 attenuation coefficient
52 attenuation correction
53 back-projection algorithm
54 coefficient
55 coefficient of variation
56 conclusion
57 conditions
58 correction
59 corresponding emission (EM) data
60 count statistics
61 count statistics data
62 count statistics transmission (TR) scans
63 counts
64 data
65 differences
66 effectiveness
67 emission data
68 emission tomography (PET) whole-body (WB) studies
69 evaluation
70 experimental conditions
71 experimental phantom
72 experimental situations
73 filtered back-projection algorithm
74 filtering
75 high-count TR images
76 image attenuation
77 image quality
78 images
79 implementation
80 inter-update median filtering
81 low count statistics
82 low count statistics data
83 low count statistics transmission (TR) scans
84 low-count TR images
85 low-noise TR images
86 mean counts
87 mean value
88 median filtering
89 median root
90 min
91 noise reduction
92 patient data
93 performance
94 phantom
95 positron emission tomography (PET) whole-body (WB) studies
96 preservation
97 quality
98 range
99 reconstructed OS-MRP
100 reconstruction
101 reconstruction algorithm
102 reduction
103 reference (high statistics) TR data
104 reference values
105 roots
106 scan time
107 scans
108 short TR scans
109 situation
110 solution
111 stable solutions
112 statistics
113 statistics data
114 statistics transmission (TR) scans
115 study
116 subsets reconstruction algorithm
117 such TR data
118 tendency
119 terms
120 terms of MVAC
121 time
122 tissue types
123 tomography (PET) whole-body (WB) studies
124 transmission PET studies
125 transmission scans
126 types
127 use
128 values
129 variation
130 whole-body studies
131 wide range
132 schema:name Implementation and evaluation of an ordered subsets reconstruction algorithm for transmission PET studies using median root prior and inter-update median filtering
133 schema:pagination 222-231
134 schema:productId N5157adf2eb084d638d0719a3dac3fb5c
135 N941a01bcf6a1479c82e5d80022c2cb94
136 Nd0ded87311994ba78b4158afa225c351
137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048628606
138 https://doi.org/10.1007/s00259-002-1046-4
139 schema:sdDatePublished 2021-12-01T19:13
140 schema:sdLicense https://scigraph.springernature.com/explorer/license/
141 schema:sdPublisher N26499de5a40d4fb59904fd4cfb716a03
142 schema:url https://doi.org/10.1007/s00259-002-1046-4
143 sgo:license sg:explorer/license/
144 sgo:sdDataset articles
145 rdf:type schema:ScholarlyArticle
146 N0179217057974ddda08e01f521af72e3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
147 schema:name Phantoms, Imaging
148 rdf:type schema:DefinedTerm
149 N0f321180b09443c79c7c376b4673312c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Artifacts
151 rdf:type schema:DefinedTerm
152 N1f9c0db59a574e418fc43697374dabbe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
153 schema:name Sensitivity and Specificity
154 rdf:type schema:DefinedTerm
155 N26499de5a40d4fb59904fd4cfb716a03 schema:name Springer Nature - SN SciGraph project
156 rdf:type schema:Organization
157 N2820c282e2c748f7949d2b15326edaa0 rdf:first sg:person.01234705530.09
158 rdf:rest N508f1d7146b345ecaf032bf499e46815
159 N2ff97d77705e4cb19107c6239e0e1405 rdf:first Nf4757a89663642f3a39d1b8d5b7f0ebe
160 rdf:rest N58bcb2a8246e4f0eb020094f747d518e
161 N35e3bcdba3854679a632a1a4432c7fd4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Stochastic Processes
163 rdf:type schema:DefinedTerm
164 N4e1f19c94a964b86aeed6e1ccf2381f8 rdf:first sg:person.0621001075.97
165 rdf:rest Nf27e9a94361e4faabf09e45a84959ca8
166 N4f74cae9b97b49f7a65e77e2d2538fc4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Thorax
168 rdf:type schema:DefinedTerm
169 N508f1d7146b345ecaf032bf499e46815 rdf:first sg:person.01340666300.40
170 rdf:rest rdf:nil
171 N510b8c3b05964b18b05e3fd324d47bcd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name Quality Control
173 rdf:type schema:DefinedTerm
174 N5157adf2eb084d638d0719a3dac3fb5c schema:name pubmed_id
175 schema:value 12552340
176 rdf:type schema:PropertyValue
177 N58bcb2a8246e4f0eb020094f747d518e rdf:first sg:person.01101061462.02
178 rdf:rest Nf183ebee45d243d8a7bf1b9e9d5870b5
179 N5d95e2634ac946249267cacbb1acaf3d schema:volumeNumber 30
180 rdf:type schema:PublicationVolume
181 N6006014b8797471f92b6a75746ca7635 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
182 schema:name Heart
183 rdf:type schema:DefinedTerm
184 N7f28375cd066479bb1e0efd8829dfda2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
185 schema:name Male
186 rdf:type schema:DefinedTerm
187 N941a01bcf6a1479c82e5d80022c2cb94 schema:name dimensions_id
188 schema:value pub.1048628606
189 rdf:type schema:PropertyValue
190 Na9b0f45456544ed2bf0e911590afd7f5 schema:issueNumber 2
191 rdf:type schema:PublicationIssue
192 Naad67b1d19794c8a842bfa454e4d0833 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
193 schema:name Whole-Body Counting
194 rdf:type schema:DefinedTerm
195 Nb1b3fe5d8abd4675baee1de86d02a7cc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
196 schema:name Image Enhancement
197 rdf:type schema:DefinedTerm
198 Nc3e00897383749588ee413250bd2d590 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
199 schema:name Humans
200 rdf:type schema:DefinedTerm
201 Nd0ded87311994ba78b4158afa225c351 schema:name doi
202 schema:value 10.1007/s00259-002-1046-4
203 rdf:type schema:PropertyValue
204 Nd2fc0ebcc32946569d8dcd0c5d95b36d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
205 schema:name Algorithms
206 rdf:type schema:DefinedTerm
207 Nd3011e48550645809ba7230d25dc49b9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
208 schema:name Tomography, Emission-Computed
209 rdf:type schema:DefinedTerm
210 Nf183ebee45d243d8a7bf1b9e9d5870b5 rdf:first sg:person.01262435052.90
211 rdf:rest N2820c282e2c748f7949d2b15326edaa0
212 Nf27e9a94361e4faabf09e45a84959ca8 rdf:first sg:person.015426704241.73
213 rdf:rest N2ff97d77705e4cb19107c6239e0e1405
214 Nf4757a89663642f3a39d1b8d5b7f0ebe schema:affiliation grid-institutes:grid.410552.7
215 schema:familyName Numminen
216 schema:givenName P.
217 rdf:type schema:Person
218 Nf850aa013b764f2d90ba1ee8af541cf1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
219 schema:name Neoplasms
220 rdf:type schema:DefinedTerm
221 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
222 schema:name Physical Sciences
223 rdf:type schema:DefinedTerm
224 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
225 schema:name Other Physical Sciences
226 rdf:type schema:DefinedTerm
227 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
228 schema:name Medical and Health Sciences
229 rdf:type schema:DefinedTerm
230 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
231 schema:name Clinical Sciences
232 rdf:type schema:DefinedTerm
233 sg:journal.1297401 schema:issn 1619-7070
234 1619-7089
235 schema:name European Journal of Nuclear Medicine and Molecular Imaging
236 schema:publisher Springer Nature
237 rdf:type schema:Periodical
238 sg:person.01101061462.02 schema:affiliation grid-institutes:grid.410552.7
239 schema:familyName Teräs
240 schema:givenName M.
241 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101061462.02
242 rdf:type schema:Person
243 sg:person.01234705530.09 schema:affiliation grid-institutes:None
244 schema:familyName Fazio
245 schema:givenName F.
246 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01234705530.09
247 rdf:type schema:Person
248 sg:person.01262435052.90 schema:affiliation grid-institutes:None
249 schema:familyName Gilardi
250 schema:givenName M.
251 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01262435052.90
252 rdf:type schema:Person
253 sg:person.01340666300.40 schema:affiliation grid-institutes:grid.502801.e
254 schema:familyName Ruotsalainen
255 schema:givenName U.
256 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01340666300.40
257 rdf:type schema:Person
258 sg:person.015426704241.73 schema:affiliation grid-institutes:grid.502801.e
259 schema:familyName Alenius
260 schema:givenName S.
261 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015426704241.73
262 rdf:type schema:Person
263 sg:person.0621001075.97 schema:affiliation grid-institutes:None
264 schema:familyName Bettinardi
265 schema:givenName V.
266 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621001075.97
267 rdf:type schema:Person
268 sg:pub.10.1007/s002590050219 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018061321
269 https://doi.org/10.1007/s002590050219
270 rdf:type schema:CreativeWork
271 sg:pub.10.1007/s002590050410 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014377698
272 https://doi.org/10.1007/s002590050410
273 rdf:type schema:CreativeWork
274 sg:pub.10.1007/s002590100651 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023839468
275 https://doi.org/10.1007/s002590100651
276 rdf:type schema:CreativeWork
277 grid-institutes:None schema:alternateName Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy
278 schema:name Department of Nuclear Medicine, Scientific Institute H.San Raffaele, University of Milano-Bicocca, INB-CNR, Via Olgettina 60, 20132, Milan, Italy
279 rdf:type schema:Organization
280 grid-institutes:grid.410552.7 schema:alternateName Turku University Central Hospital, Finland
281 schema:name Turku University Central Hospital, Finland
282 rdf:type schema:Organization
283 grid-institutes:grid.502801.e schema:alternateName Tampere University of Technology, Institute of Signal Processing, Finland
284 schema:name Tampere University of Technology, Institute of Signal Processing, Finland
285 rdf:type schema:Organization
 




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


...