Improving DTI Resolution from a Single Clinical Acquisition: A Statistical Approach Using Spatial Prior View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2013

AUTHORS

Vikash Gupta , Nicholas Ayache , Xavier Pennec

ABSTRACT

Diffusion Tensor Imaging (DTI) provides us with valuable information about the white matter fibers and their arrangement in the brain. However, clinical DTI acquisitions are often low resolution, causing partial volume effects. In this paper, we propose a new high resolution tensor estimation method. This method makes use of the spatial correlation between neighboring voxels. Unlike some super-resolution algorithms, the proposed method does not require multiple acquisitions, thus it is better suited for clinical situations. The method relies on a maximum likelihood strategy for tensor estimation to optimally account for the noise and an anisotropic regularization prior to promote smoothness in homogeneous areas while respecting the edges. To the best of our knowledge, this is the first method to produce high resolution tensor images from a single low resolution acquisition. We demonstrate the efficiency of the method on synthetic low-resolution data and real clinical data. The results show statistically significant improvements in fiber tractography. More... »

PAGES

477-84

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-40760-4_60

DOI

http://dx.doi.org/10.1007/978-3-642-40760-4_60

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029764974

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24505796


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": "Brain", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Interpretation, Statistical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diffusion Tensor Imaging", 
        "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": "Nerve Fibers, Myelinated", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Pattern Recognition, Automated", 
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "INRIA Sophia Antipolis, ASCLEPIOS Project."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gupta", 
        "givenName": "Vikash", 
        "id": "sg:person.01074616422.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074616422.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "INRIA Sophia Antipolis, ASCLEPIOS Project."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ayache", 
        "givenName": "Nicholas", 
        "id": "sg:person.01334004425.66", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01334004425.66"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "INRIA Sophia Antipolis, ASCLEPIOS Project."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pennec", 
        "givenName": "Xavier", 
        "id": "sg:person.01047036047.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047036047.02"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1006/jvci.1993.1030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008842757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2011.01.032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014839759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2011.01.032", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014839759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2012.05.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016688164"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neuroimage.2011.01.048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018398233"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0730-725x(02)00511-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032780458"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0730-725x(02)00511-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032780458"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/1522-2594(200101)45:1<29::aid-mrm1005>3.0.co;2-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044373515"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.20965", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051149571"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1695690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057766928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2007.899173", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061695047"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/isbi.2008.4541136", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095517447"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2013", 
    "datePublishedReg": "2013-01-01", 
    "description": "Diffusion Tensor Imaging (DTI) provides us with valuable information about the white matter fibers and their arrangement in the brain. However, clinical DTI acquisitions are often low resolution, causing partial volume effects. In this paper, we propose a new high resolution tensor estimation method. This method makes use of the spatial correlation between neighboring voxels. Unlike some super-resolution algorithms, the proposed method does not require multiple acquisitions, thus it is better suited for clinical situations. The method relies on a maximum likelihood strategy for tensor estimation to optimally account for the noise and an anisotropic regularization prior to promote smoothness in homogeneous areas while respecting the edges. To the best of our knowledge, this is the first method to produce high resolution tensor images from a single low resolution acquisition. We demonstrate the efficiency of the method on synthetic low-resolution data and real clinical data. The results show statistically significant improvements in fiber tractography.", 
    "editor": [
      {
        "familyName": "Salinesi", 
        "givenName": "Camille", 
        "type": "Person"
      }, 
      {
        "familyName": "Norrie", 
        "givenName": "Moira C.", 
        "type": "Person"
      }, 
      {
        "familyName": "Pastor", 
        "givenName": "\u00d3scar", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-40760-4_60", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-642-38708-1", 
        "978-3-642-38709-8"
      ], 
      "name": "Advanced Information Systems Engineering", 
      "type": "Book"
    }, 
    "name": "Improving DTI Resolution from a Single Clinical Acquisition: A Statistical Approach Using Spatial Prior", 
    "pagination": "477-84", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-40760-4_60"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "8dcbae4a4fddfb987c32f3c98a0c20af47f933082521d4718776dbffadff04ac"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1029764974"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "24505796"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-40760-4_60", 
      "https://app.dimensions.ai/details/publication/pub.1029764974"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T22:32", 
    "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/v90/0000000001_0000000264/records_8693_00000562.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-642-40760-4_60"
  }
]
 

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.10.1007/978-3-642-40760-4_60'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-40760-4_60'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-40760-4_60'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-40760-4_60'


 

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

174 TRIPLES      23 PREDICATES      50 URIs      33 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-40760-4_60 schema:about N21fec9ec2c0c438ebe1f2c30bd218a11
2 N2507ef775a7c43f0b72c8f543bb42f3b
3 N3b72a61ba11c433a938d903c4ae2cf0a
4 N681bf841879a490a9f52153fb50eb567
5 N6b943083c94e45778eab412f1ccb09dd
6 N6deb1b120126475ba64b640bc52eacba
7 N79e890f80067499a9538f61ef6024fe6
8 Nbafac4e8634d41e194b06c9678579e76
9 Nbcbea9a5c70642ce8eaf8a1018d36691
10 Nca7eeceef4b0401ea76346e74d485c2e
11 Nd0971780bc3241bf8eb83ef833ac670c
12 Nf3f3b64c070b4bffb38dcf0fe90daa16
13 anzsrc-for:08
14 anzsrc-for:0801
15 schema:author Nd7b42fedeff14df78e9884e8e8f02975
16 schema:citation https://doi.org/10.1002/1522-2594(200101)45:1<29::aid-mrm1005>3.0.co;2-z
17 https://doi.org/10.1002/mrm.20965
18 https://doi.org/10.1006/jvci.1993.1030
19 https://doi.org/10.1016/j.media.2012.05.003
20 https://doi.org/10.1016/j.neuroimage.2011.01.032
21 https://doi.org/10.1016/j.neuroimage.2011.01.048
22 https://doi.org/10.1016/s0730-725x(02)00511-8
23 https://doi.org/10.1063/1.1695690
24 https://doi.org/10.1109/isbi.2008.4541136
25 https://doi.org/10.1109/tmi.2007.899173
26 schema:datePublished 2013
27 schema:datePublishedReg 2013-01-01
28 schema:description Diffusion Tensor Imaging (DTI) provides us with valuable information about the white matter fibers and their arrangement in the brain. However, clinical DTI acquisitions are often low resolution, causing partial volume effects. In this paper, we propose a new high resolution tensor estimation method. This method makes use of the spatial correlation between neighboring voxels. Unlike some super-resolution algorithms, the proposed method does not require multiple acquisitions, thus it is better suited for clinical situations. The method relies on a maximum likelihood strategy for tensor estimation to optimally account for the noise and an anisotropic regularization prior to promote smoothness in homogeneous areas while respecting the edges. To the best of our knowledge, this is the first method to produce high resolution tensor images from a single low resolution acquisition. We demonstrate the efficiency of the method on synthetic low-resolution data and real clinical data. The results show statistically significant improvements in fiber tractography.
29 schema:editor N5d22c074e14d49e3b3afbb7672dc00f7
30 schema:genre chapter
31 schema:inLanguage en
32 schema:isAccessibleForFree true
33 schema:isPartOf N6d231219e9a24e198f7ada9ac9b15b6f
34 schema:name Improving DTI Resolution from a Single Clinical Acquisition: A Statistical Approach Using Spatial Prior
35 schema:pagination 477-84
36 schema:productId N15676aa983f846fab46039b763e801cc
37 N31029e38a34b49a791c661edd443f53d
38 N4c9f6ed9f2374f65b2ba784e472d2e79
39 Nf11d4fe84ee949a08153294579dc1ac7
40 schema:publisher Nda58d0743302485d82a52f0a77102e2a
41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029764974
42 https://doi.org/10.1007/978-3-642-40760-4_60
43 schema:sdDatePublished 2019-04-15T22:32
44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
45 schema:sdPublisher N131e19c26f8c4014aee03636cc0e2c0e
46 schema:url http://link.springer.com/10.1007/978-3-642-40760-4_60
47 sgo:license sg:explorer/license/
48 sgo:sdDataset chapters
49 rdf:type schema:Chapter
50 N083b299704ae42c8b0f6180d5246f2f2 rdf:first sg:person.01047036047.02
51 rdf:rest rdf:nil
52 N08eeb7ce63084e3aab3f1ddf7d91a7c7 schema:familyName Pastor
53 schema:givenName Óscar
54 rdf:type schema:Person
55 N131e19c26f8c4014aee03636cc0e2c0e schema:name Springer Nature - SN SciGraph project
56 rdf:type schema:Organization
57 N15676aa983f846fab46039b763e801cc schema:name dimensions_id
58 schema:value pub.1029764974
59 rdf:type schema:PropertyValue
60 N21fec9ec2c0c438ebe1f2c30bd218a11 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
61 schema:name Image Interpretation, Computer-Assisted
62 rdf:type schema:DefinedTerm
63 N2507ef775a7c43f0b72c8f543bb42f3b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
64 schema:name Sensitivity and Specificity
65 rdf:type schema:DefinedTerm
66 N31029e38a34b49a791c661edd443f53d schema:name pubmed_id
67 schema:value 24505796
68 rdf:type schema:PropertyValue
69 N3b72a61ba11c433a938d903c4ae2cf0a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
70 schema:name Nerve Fibers, Myelinated
71 rdf:type schema:DefinedTerm
72 N401e696e91334e3a8cfefe196668e687 rdf:first sg:person.01334004425.66
73 rdf:rest N083b299704ae42c8b0f6180d5246f2f2
74 N4c9f6ed9f2374f65b2ba784e472d2e79 schema:name readcube_id
75 schema:value 8dcbae4a4fddfb987c32f3c98a0c20af47f933082521d4718776dbffadff04ac
76 rdf:type schema:PropertyValue
77 N5c8a73c45fb245128176343b8cdd6a0e schema:familyName Salinesi
78 schema:givenName Camille
79 rdf:type schema:Person
80 N5d22c074e14d49e3b3afbb7672dc00f7 rdf:first N5c8a73c45fb245128176343b8cdd6a0e
81 rdf:rest Nd70220ab011f4f0cb36be30d841a1f55
82 N681bf841879a490a9f52153fb50eb567 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Algorithms
84 rdf:type schema:DefinedTerm
85 N6b943083c94e45778eab412f1ccb09dd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
86 schema:name Pattern Recognition, Automated
87 rdf:type schema:DefinedTerm
88 N6d231219e9a24e198f7ada9ac9b15b6f schema:isbn 978-3-642-38708-1
89 978-3-642-38709-8
90 schema:name Advanced Information Systems Engineering
91 rdf:type schema:Book
92 N6deb1b120126475ba64b640bc52eacba schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Brain
94 rdf:type schema:DefinedTerm
95 N76a90ffcbda74efca5389d9c89b60eda schema:name INRIA Sophia Antipolis, ASCLEPIOS Project.
96 rdf:type schema:Organization
97 N79e890f80067499a9538f61ef6024fe6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name Humans
99 rdf:type schema:DefinedTerm
100 Nbafac4e8634d41e194b06c9678579e76 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Diffusion Tensor Imaging
102 rdf:type schema:DefinedTerm
103 Nbcbea9a5c70642ce8eaf8a1018d36691 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Subtraction Technique
105 rdf:type schema:DefinedTerm
106 Nbe4bee27a87444518d26d5672018d25d schema:name INRIA Sophia Antipolis, ASCLEPIOS Project.
107 rdf:type schema:Organization
108 Nca7eeceef4b0401ea76346e74d485c2e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Data Interpretation, Statistical
110 rdf:type schema:DefinedTerm
111 Nd0971780bc3241bf8eb83ef833ac670c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Reproducibility of Results
113 rdf:type schema:DefinedTerm
114 Nd70220ab011f4f0cb36be30d841a1f55 rdf:first Nfb33abf1ea8e4c2487b7b3a0ae35a564
115 rdf:rest Nfe015c29086e46b8901df13f7516666b
116 Nd7b42fedeff14df78e9884e8e8f02975 rdf:first sg:person.01074616422.44
117 rdf:rest N401e696e91334e3a8cfefe196668e687
118 Nda58d0743302485d82a52f0a77102e2a schema:location Berlin, Heidelberg
119 schema:name Springer Berlin Heidelberg
120 rdf:type schema:Organisation
121 Nf11d4fe84ee949a08153294579dc1ac7 schema:name doi
122 schema:value 10.1007/978-3-642-40760-4_60
123 rdf:type schema:PropertyValue
124 Nf3f3b64c070b4bffb38dcf0fe90daa16 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Image Enhancement
126 rdf:type schema:DefinedTerm
127 Nfb33abf1ea8e4c2487b7b3a0ae35a564 schema:familyName Norrie
128 schema:givenName Moira C.
129 rdf:type schema:Person
130 Nfe015c29086e46b8901df13f7516666b rdf:first N08eeb7ce63084e3aab3f1ddf7d91a7c7
131 rdf:rest rdf:nil
132 Nfe45c96a845148f48e0ba896ccc148cc schema:name INRIA Sophia Antipolis, ASCLEPIOS Project.
133 rdf:type schema:Organization
134 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
135 schema:name Information and Computing Sciences
136 rdf:type schema:DefinedTerm
137 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
138 schema:name Artificial Intelligence and Image Processing
139 rdf:type schema:DefinedTerm
140 sg:person.01047036047.02 schema:affiliation N76a90ffcbda74efca5389d9c89b60eda
141 schema:familyName Pennec
142 schema:givenName Xavier
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047036047.02
144 rdf:type schema:Person
145 sg:person.01074616422.44 schema:affiliation Nbe4bee27a87444518d26d5672018d25d
146 schema:familyName Gupta
147 schema:givenName Vikash
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074616422.44
149 rdf:type schema:Person
150 sg:person.01334004425.66 schema:affiliation Nfe45c96a845148f48e0ba896ccc148cc
151 schema:familyName Ayache
152 schema:givenName Nicholas
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01334004425.66
154 rdf:type schema:Person
155 https://doi.org/10.1002/1522-2594(200101)45:1<29::aid-mrm1005>3.0.co;2-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1044373515
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1002/mrm.20965 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051149571
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1006/jvci.1993.1030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008842757
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.media.2012.05.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016688164
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.neuroimage.2011.01.032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014839759
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.neuroimage.2011.01.048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018398233
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/s0730-725x(02)00511-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032780458
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1063/1.1695690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057766928
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1109/isbi.2008.4541136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095517447
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1109/tmi.2007.899173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061695047
174 rdf:type schema:CreativeWork
 




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


...