Accelerated 3D Coronary Vessel Wall MR Imaging Based on Compressed Sensing with a Block-Weighted Total Variation Regularization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-03-01

AUTHORS

Zhongzhou Chen, Xiaoyong Zhang, Caiyun Shi, Shi Su, Zhaoyang Fan, Jim X. Ji, Guoxi Xie, Xin Liu

ABSTRACT

Coronary vessel wall magnetic resonance (MR) imaging is important for heart disease diagnosis but often suffers long scan time. Compressed sensing (CS) has been previously used to accelerate MR imaging by reconstructing an MR image from undersampled k-space data using a regularization framework. However, the widely used regularizations in the current CS methods often lead to smoothing effects and thus are unable to reconstruct the coronary vessel walls with sufficient resolution. To address this issue, a novel block-weighted total variation regularization is presented to accelerate the coronary vessel wall MR imaging. The proposed regularization divides the image into two parts: a region-of-interest (ROI) which contains the coronary vessel wall, and the other region with less concerned features. Different penalty weights are given to the two regions. As a result, the small details within ROI do not suffer from over-smoothing while the noise outside the ROI can be significantly suppressed. Results with both numerical simulations and in vivo experiments demonstrated that the proposed method can reconstruct the coronary vessel wall from undersampled k-space data with higher qualities than the conventional CS with the total variation or the edge-preserved total variation. More... »

PAGES

361-378

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00723-017-0866-0

DOI

http://dx.doi.org/10.1007/s00723-017-0866-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084023213


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/2008/03", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0202", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atomic, Molecular, Nuclear, Particle and Plasma Physics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0301", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Analytical Chemistry", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0306", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Chemistry (incl. Structural)", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China", 
          "id": "http://www.grid.ac/institutes/grid.410726.6", 
          "name": [
            "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China", 
            "Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Zhongzhou", 
        "id": "sg:person.013576640647.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013576640647.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China, Hefei, China", 
          "id": "http://www.grid.ac/institutes/grid.59053.3a", 
          "name": [
            "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China", 
            "Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China, Hefei, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Xiaoyong", 
        "id": "sg:person.0621735621.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621735621.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China", 
          "id": "http://www.grid.ac/institutes/grid.458489.c", 
          "name": [
            "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shi", 
        "givenName": "Caiyun", 
        "id": "sg:person.0576413304.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0576413304.98"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China", 
          "id": "http://www.grid.ac/institutes/grid.458489.c", 
          "name": [
            "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Su", 
        "givenName": "Shi", 
        "id": "sg:person.01004277421.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01004277421.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.50956.3f", 
          "name": [
            "Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fan", 
        "givenName": "Zhaoyang", 
        "id": "sg:person.01136172260.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA", 
          "id": "http://www.grid.ac/institutes/grid.264756.4", 
          "name": [
            "Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ji", 
        "givenName": "Jim X.", 
        "id": "sg:person.010646236352.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010646236352.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China", 
          "id": "http://www.grid.ac/institutes/grid.458489.c", 
          "name": [
            "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xie", 
        "givenName": "Guoxi", 
        "id": "sg:person.01240364572.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240364572.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China", 
          "id": "http://www.grid.ac/institutes/grid.458489.c", 
          "name": [
            "Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Xin", 
        "id": "sg:person.015547527234.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015547527234.48"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11432-011-4328-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041734795", 
          "https://doi.org/10.1007/s11432-011-4328-2"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-03-01", 
    "datePublishedReg": "2017-03-01", 
    "description": "Coronary vessel wall magnetic resonance (MR) imaging is important for heart disease diagnosis but often suffers long scan time. Compressed sensing (CS) has been previously used to accelerate MR imaging by reconstructing an MR image from undersampled k-space data using a regularization framework. However, the widely used regularizations in the current CS methods often lead to smoothing effects and thus are unable to reconstruct the coronary vessel walls with sufficient resolution. To address this issue, a novel block-weighted total variation regularization is presented to accelerate the coronary vessel wall MR imaging. The proposed regularization divides the image into two parts: a region-of-interest (ROI) which contains the coronary vessel wall, and the other region with less concerned features. Different penalty weights are given to the two regions. As a result, the small details within ROI do not suffer from over-smoothing while the noise outside the ROI can be significantly suppressed. Results with both numerical simulations and in vivo experiments demonstrated that the proposed method can reconstruct the coronary vessel wall from undersampled k-space data with higher qualities than the conventional CS with the total variation or the edge-preserved total variation.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00723-017-0866-0", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5300626", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.6976714", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8350505", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1102112", 
        "issn": [
          "0937-9347", 
          "1613-7507"
        ], 
        "name": "Applied Magnetic Resonance", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "48"
      }
    ], 
    "keywords": [
      "Compressed Sensing", 
      "undersampled k-space data", 
      "current CS methods", 
      "k-space data", 
      "conventional compressed sensing", 
      "numerical simulations", 
      "total variation regularization", 
      "edge-preserving total variation", 
      "CS method", 
      "variation regularization", 
      "sensing", 
      "concerned features", 
      "wall", 
      "long scan times", 
      "sufficient resolution", 
      "high quality", 
      "scan time", 
      "simulations", 
      "vessel wall", 
      "different penalty weights", 
      "penalty weights", 
      "total variation", 
      "noise", 
      "coronary vessel wall", 
      "method", 
      "small details", 
      "Based", 
      "heart disease diagnosis", 
      "regularization", 
      "images", 
      "results", 
      "variation", 
      "smoothing", 
      "resolution", 
      "experiments", 
      "regularization framework", 
      "region", 
      "detail", 
      "vessel wall MR", 
      "vivo experiments", 
      "time", 
      "effect", 
      "vessel wall magnetic resonance imaging", 
      "MR images", 
      "quality", 
      "data", 
      "features", 
      "ROI", 
      "part", 
      "disease diagnosis", 
      "issues", 
      "interest", 
      "framework", 
      "imaging", 
      "weight", 
      "magnetic resonance imaging", 
      "MR", 
      "resonance imaging", 
      "diagnosis"
    ], 
    "name": "Accelerated 3D Coronary Vessel Wall MR Imaging Based on Compressed Sensing with a Block-Weighted Total Variation Regularization", 
    "pagination": "361-378", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1084023213"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00723-017-0866-0"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00723-017-0866-0", 
      "https://app.dimensions.ai/details/publication/pub.1084023213"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:43", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_754.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00723-017-0866-0"
  }
]
 

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/s00723-017-0866-0'

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/s00723-017-0866-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00723-017-0866-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00723-017-0866-0'


 

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

201 TRIPLES      21 PREDICATES      87 URIs      75 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00723-017-0866-0 schema:about anzsrc-for:02
2 anzsrc-for:0202
3 anzsrc-for:03
4 anzsrc-for:0301
5 anzsrc-for:0306
6 schema:author N4c18f9db27cc442da040818f9e449b53
7 schema:citation sg:pub.10.1007/s11432-011-4328-2
8 schema:datePublished 2017-03-01
9 schema:datePublishedReg 2017-03-01
10 schema:description Coronary vessel wall magnetic resonance (MR) imaging is important for heart disease diagnosis but often suffers long scan time. Compressed sensing (CS) has been previously used to accelerate MR imaging by reconstructing an MR image from undersampled k-space data using a regularization framework. However, the widely used regularizations in the current CS methods often lead to smoothing effects and thus are unable to reconstruct the coronary vessel walls with sufficient resolution. To address this issue, a novel block-weighted total variation regularization is presented to accelerate the coronary vessel wall MR imaging. The proposed regularization divides the image into two parts: a region-of-interest (ROI) which contains the coronary vessel wall, and the other region with less concerned features. Different penalty weights are given to the two regions. As a result, the small details within ROI do not suffer from over-smoothing while the noise outside the ROI can be significantly suppressed. Results with both numerical simulations and in vivo experiments demonstrated that the proposed method can reconstruct the coronary vessel wall from undersampled k-space data with higher qualities than the conventional CS with the total variation or the edge-preserved total variation.
11 schema:genre article
12 schema:isAccessibleForFree false
13 schema:isPartOf N2553cbe1f76d47acbb5a1df841d11b43
14 Nd7b58f4a0b104f49a1413fe815020c7b
15 sg:journal.1102112
16 schema:keywords Based
17 CS method
18 Compressed Sensing
19 MR
20 MR images
21 ROI
22 concerned features
23 conventional compressed sensing
24 coronary vessel wall
25 current CS methods
26 data
27 detail
28 diagnosis
29 different penalty weights
30 disease diagnosis
31 edge-preserving total variation
32 effect
33 experiments
34 features
35 framework
36 heart disease diagnosis
37 high quality
38 images
39 imaging
40 interest
41 issues
42 k-space data
43 long scan times
44 magnetic resonance imaging
45 method
46 noise
47 numerical simulations
48 part
49 penalty weights
50 quality
51 region
52 regularization
53 regularization framework
54 resolution
55 resonance imaging
56 results
57 scan time
58 sensing
59 simulations
60 small details
61 smoothing
62 sufficient resolution
63 time
64 total variation
65 total variation regularization
66 undersampled k-space data
67 variation
68 variation regularization
69 vessel wall
70 vessel wall MR
71 vessel wall magnetic resonance imaging
72 vivo experiments
73 wall
74 weight
75 schema:name Accelerated 3D Coronary Vessel Wall MR Imaging Based on Compressed Sensing with a Block-Weighted Total Variation Regularization
76 schema:pagination 361-378
77 schema:productId N50f5021d55ec47dca889dc33e6f76684
78 N8ab5109c65454847ba0639cdde621348
79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084023213
80 https://doi.org/10.1007/s00723-017-0866-0
81 schema:sdDatePublished 2022-10-01T06:43
82 schema:sdLicense https://scigraph.springernature.com/explorer/license/
83 schema:sdPublisher N282013594c8c45d0a657a25a9e653eee
84 schema:url https://doi.org/10.1007/s00723-017-0866-0
85 sgo:license sg:explorer/license/
86 sgo:sdDataset articles
87 rdf:type schema:ScholarlyArticle
88 N253e3a3f2e6d4a1595b6c10aafa7d6a9 rdf:first sg:person.01240364572.83
89 rdf:rest N2914e11467ef4cd2b710554181ecad5b
90 N2553cbe1f76d47acbb5a1df841d11b43 schema:volumeNumber 48
91 rdf:type schema:PublicationVolume
92 N282013594c8c45d0a657a25a9e653eee schema:name Springer Nature - SN SciGraph project
93 rdf:type schema:Organization
94 N2914e11467ef4cd2b710554181ecad5b rdf:first sg:person.015547527234.48
95 rdf:rest rdf:nil
96 N4c18f9db27cc442da040818f9e449b53 rdf:first sg:person.013576640647.33
97 rdf:rest Nb26bdda28b214a2aacee5d7e07b4818c
98 N5029ec628bb4454b87ef3aa403c7b808 rdf:first sg:person.010646236352.59
99 rdf:rest N253e3a3f2e6d4a1595b6c10aafa7d6a9
100 N50f5021d55ec47dca889dc33e6f76684 schema:name dimensions_id
101 schema:value pub.1084023213
102 rdf:type schema:PropertyValue
103 N5f3459da31c54c0a81d407f2b4682380 rdf:first sg:person.0576413304.98
104 rdf:rest Nea6dc0bf501d4bf68834002eabfd46cc
105 N8ab5109c65454847ba0639cdde621348 schema:name doi
106 schema:value 10.1007/s00723-017-0866-0
107 rdf:type schema:PropertyValue
108 Nb26bdda28b214a2aacee5d7e07b4818c rdf:first sg:person.0621735621.42
109 rdf:rest N5f3459da31c54c0a81d407f2b4682380
110 Nbe28c6b3c508447688d03caf15137909 rdf:first sg:person.01136172260.58
111 rdf:rest N5029ec628bb4454b87ef3aa403c7b808
112 Nd7b58f4a0b104f49a1413fe815020c7b schema:issueNumber 4
113 rdf:type schema:PublicationIssue
114 Nea6dc0bf501d4bf68834002eabfd46cc rdf:first sg:person.01004277421.97
115 rdf:rest Nbe28c6b3c508447688d03caf15137909
116 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
117 schema:name Physical Sciences
118 rdf:type schema:DefinedTerm
119 anzsrc-for:0202 schema:inDefinedTermSet anzsrc-for:
120 schema:name Atomic, Molecular, Nuclear, Particle and Plasma Physics
121 rdf:type schema:DefinedTerm
122 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
123 schema:name Chemical Sciences
124 rdf:type schema:DefinedTerm
125 anzsrc-for:0301 schema:inDefinedTermSet anzsrc-for:
126 schema:name Analytical Chemistry
127 rdf:type schema:DefinedTerm
128 anzsrc-for:0306 schema:inDefinedTermSet anzsrc-for:
129 schema:name Physical Chemistry (incl. Structural)
130 rdf:type schema:DefinedTerm
131 sg:grant.5300626 http://pending.schema.org/fundedItem sg:pub.10.1007/s00723-017-0866-0
132 rdf:type schema:MonetaryGrant
133 sg:grant.6976714 http://pending.schema.org/fundedItem sg:pub.10.1007/s00723-017-0866-0
134 rdf:type schema:MonetaryGrant
135 sg:grant.8350505 http://pending.schema.org/fundedItem sg:pub.10.1007/s00723-017-0866-0
136 rdf:type schema:MonetaryGrant
137 sg:journal.1102112 schema:issn 0937-9347
138 1613-7507
139 schema:name Applied Magnetic Resonance
140 schema:publisher Springer Nature
141 rdf:type schema:Periodical
142 sg:person.01004277421.97 schema:affiliation grid-institutes:grid.458489.c
143 schema:familyName Su
144 schema:givenName Shi
145 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01004277421.97
146 rdf:type schema:Person
147 sg:person.010646236352.59 schema:affiliation grid-institutes:grid.264756.4
148 schema:familyName Ji
149 schema:givenName Jim X.
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010646236352.59
151 rdf:type schema:Person
152 sg:person.01136172260.58 schema:affiliation grid-institutes:grid.50956.3f
153 schema:familyName Fan
154 schema:givenName Zhaoyang
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01136172260.58
156 rdf:type schema:Person
157 sg:person.01240364572.83 schema:affiliation grid-institutes:grid.458489.c
158 schema:familyName Xie
159 schema:givenName Guoxi
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240364572.83
161 rdf:type schema:Person
162 sg:person.013576640647.33 schema:affiliation grid-institutes:grid.410726.6
163 schema:familyName Chen
164 schema:givenName Zhongzhou
165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013576640647.33
166 rdf:type schema:Person
167 sg:person.015547527234.48 schema:affiliation grid-institutes:grid.458489.c
168 schema:familyName Liu
169 schema:givenName Xin
170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015547527234.48
171 rdf:type schema:Person
172 sg:person.0576413304.98 schema:affiliation grid-institutes:grid.458489.c
173 schema:familyName Shi
174 schema:givenName Caiyun
175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0576413304.98
176 rdf:type schema:Person
177 sg:person.0621735621.42 schema:affiliation grid-institutes:grid.59053.3a
178 schema:familyName Zhang
179 schema:givenName Xiaoyong
180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621735621.42
181 rdf:type schema:Person
182 sg:pub.10.1007/s11432-011-4328-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041734795
183 https://doi.org/10.1007/s11432-011-4328-2
184 rdf:type schema:CreativeWork
185 grid-institutes:grid.264756.4 schema:alternateName Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
186 schema:name Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
187 rdf:type schema:Organization
188 grid-institutes:grid.410726.6 schema:alternateName Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
189 schema:name Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
190 Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
191 rdf:type schema:Organization
192 grid-institutes:grid.458489.c schema:alternateName Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
193 schema:name Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
194 rdf:type schema:Organization
195 grid-institutes:grid.50956.3f schema:alternateName Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
196 schema:name Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
197 rdf:type schema:Organization
198 grid-institutes:grid.59053.3a schema:alternateName Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China, Hefei, China
199 schema:name Centers for Biomedical Engineering, College of Information Science and Technology, University of Science and Technology of China, Hefei, China
200 Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
201 rdf:type schema:Organization
 




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


...