Ontology type: schema:Chapter
2009
AUTHORSH. J. Wittsack , R. S. Lanzman , U. Mödder , D. Blondin
ABSTRACTThe use of diffusion weighted magnetic resonance imaging (DWI) has been more and more extended to abdominal organs. In comparison to the human brain the tissue of the kidney shows considerably different microscopic structure, which must be considered in the calculating of the apparent diffusion constant (ADC). In most studies ADC is determined using a mono-exponential model. Due to the high vascularization in the kidney a bi-exponential approach is reasonable to allow for a differentiation between pure diffusion fraction and a fraction influenced by perfusion effects.In our work we analyzed whether the mono- or bi-exponential approach is more accurate from statistical point of view for in-vivo DWI of the kidney. For this purpose we acquired DWI in five healthy subjects. Further we simulated a DWI signal varying the value of the perfusion fraction to investigate the relation between the results of mono- and bi-exponential analysis. Besides we simulated a DWI signal at different signal to noise ratios to analyze the influence of noise on the ADC resulting from the mono- and bi-exponential approach.The statistical analysis of F-test, Akaike’s information criterion (AIC) and Schwarz criterion (FC) of the in-vivo data shows that the bi-exponential approach represents the “best regression” to determine ADC. In five in-vivo investigations 87% (F-test), 95 % (AIC) and 92% (SC) of the pixels possessed bi-exponential characteristics. The simulation of the DWI signal asserts increasing mono-exponential calculated ADC values with rising perfusion fraction within the tissue. Further our simulation shows that the variations of the mono-exponential results with increasing noise are less than that of bi-exponential approach. More... »
PAGES119-122
World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany
ISBN
978-3-642-03878-5
978-3-642-03879-2
http://scigraph.springernature.com/pub.10.1007/978-3-642-03879-2_34
DOIhttp://dx.doi.org/10.1007/978-3-642-03879-2_34
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1004430735
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/2008/11",
"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/2008/1103",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Clinical Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany",
"id": "http://www.grid.ac/institutes/grid.14778.3d",
"name": [
"Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany"
],
"type": "Organization"
},
"familyName": "Wittsack",
"givenName": "H. J.",
"id": "sg:person.0675141725.09",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675141725.09"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany",
"id": "http://www.grid.ac/institutes/grid.14778.3d",
"name": [
"Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany"
],
"type": "Organization"
},
"familyName": "Lanzman",
"givenName": "R. S.",
"id": "sg:person.01332457401.96",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01332457401.96"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany",
"id": "http://www.grid.ac/institutes/grid.14778.3d",
"name": [
"Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany"
],
"type": "Organization"
},
"familyName": "M\u00f6dder",
"givenName": "U.",
"id": "sg:person.0637037231.08",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0637037231.08"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany",
"id": "http://www.grid.ac/institutes/grid.14778.3d",
"name": [
"Institute of Radiology, D\u00fcsseldorf University Hospital, D\u00fcsseldorf, Germany"
],
"type": "Organization"
},
"familyName": "Blondin",
"givenName": "D.",
"id": "sg:person.0777602654.02",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777602654.02"
],
"type": "Person"
}
],
"datePublished": "2009",
"datePublishedReg": "2009-01-01",
"description": "The use of diffusion weighted magnetic resonance imaging (DWI) has been more and more extended to abdominal organs. In comparison to the human brain the tissue of the kidney shows considerably different microscopic structure, which must be considered in the calculating of the apparent diffusion constant (ADC). In most studies ADC is determined using a mono-exponential model. Due to the high vascularization in the kidney a bi-exponential approach is reasonable to allow for a differentiation between pure diffusion fraction and a fraction influenced by perfusion effects.In our work we analyzed whether the mono- or bi-exponential approach is more accurate from statistical point of view for in-vivo DWI of the kidney. For this purpose we acquired DWI in five healthy subjects. Further we simulated a DWI signal varying the value of the perfusion fraction to investigate the relation between the results of mono- and bi-exponential analysis. Besides we simulated a DWI signal at different signal to noise ratios to analyze the influence of noise on the ADC resulting from the mono- and bi-exponential approach.The statistical analysis of F-test, Akaike\u2019s information criterion (AIC) and Schwarz criterion (FC) of the in-vivo data shows that the bi-exponential approach represents the \u201cbest regression\u201d to determine ADC. In five in-vivo investigations 87% (F-test), 95 % (AIC) and 92% (SC) of the pixels possessed bi-exponential characteristics. The simulation of the DWI signal asserts increasing mono-exponential calculated ADC values with rising perfusion fraction within the tissue. Further our simulation shows that the variations of the mono-exponential results with increasing noise are less than that of bi-exponential approach.",
"editor": [
{
"familyName": "D\u00f6ssel",
"givenName": "Olaf",
"type": "Person"
},
{
"familyName": "Schlegel",
"givenName": "Wolfgang C.",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-642-03879-2_34",
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-642-03878-5",
"978-3-642-03879-2"
],
"name": "World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany",
"type": "Book"
},
"keywords": [
"Akaike information criterion",
"information criterion",
"statistical point",
"influence of noise",
"Schwarz criterion",
"different microscopic structures",
"magnetic resonance imaging",
"diffusion constants",
"noise",
"statistical analysis",
"simulations",
"resonance imaging",
"result of mono",
"DWI signal",
"perfusion fraction",
"use of diffusion",
"F-test",
"microscopic structure",
"different signals",
"Diffusion-Weighted Magnetic Resonance Imaging",
"approach",
"Weighted Magnetic Resonance Imaging",
"diffusion",
"healthy subjects",
"signals",
"abdominal organs",
"calculating",
"high vascularization",
"kidney",
"ADC values",
"human kidney",
"mono-exponential model",
"perfusion effects",
"model",
"bi-exponential analysis",
"calculated ADC values",
"human brain",
"vivo data",
"DWI",
"good regression",
"point",
"criteria",
"constants",
"results",
"regression",
"tissue",
"values",
"imaging",
"structure",
"analysis",
"brain",
"ADC",
"vascularization",
"work",
"organs",
"comparison",
"apparent diffusion",
"diffusion fraction",
"subjects",
"relation",
"fraction",
"pixels",
"variation",
"data",
"differentiation",
"determination",
"view",
"ratio",
"characteristics",
"influence",
"effect",
"use",
"purpose",
"mono"
],
"name": "Diffusion Weighted Magnetic Resonance Imaging of the Human Kidney: \u201cBest Regression\u201d for the Determination of Diffusion Constants",
"pagination": "119-122",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1004430735"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-642-03879-2_34"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-642-03879-2_34",
"https://app.dimensions.ai/details/publication/pub.1004430735"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-08-04T17:17",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/chapter/chapter_252.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-642-03879-2_34"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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.10.1007/978-3-642-03879-2_34'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-03879-2_34'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-03879-2_34'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-03879-2_34'
This table displays all metadata directly associated to this object as RDF triples.
159 TRIPLES
22 PREDICATES
99 URIs
92 LITERALS
7 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/978-3-642-03879-2_34 | schema:about | anzsrc-for:11 |
2 | ″ | ″ | anzsrc-for:1103 |
3 | ″ | schema:author | N54c220aea3514105b5a12483abfc71d2 |
4 | ″ | schema:datePublished | 2009 |
5 | ″ | schema:datePublishedReg | 2009-01-01 |
6 | ″ | schema:description | The use of diffusion weighted magnetic resonance imaging (DWI) has been more and more extended to abdominal organs. In comparison to the human brain the tissue of the kidney shows considerably different microscopic structure, which must be considered in the calculating of the apparent diffusion constant (ADC). In most studies ADC is determined using a mono-exponential model. Due to the high vascularization in the kidney a bi-exponential approach is reasonable to allow for a differentiation between pure diffusion fraction and a fraction influenced by perfusion effects.In our work we analyzed whether the mono- or bi-exponential approach is more accurate from statistical point of view for in-vivo DWI of the kidney. For this purpose we acquired DWI in five healthy subjects. Further we simulated a DWI signal varying the value of the perfusion fraction to investigate the relation between the results of mono- and bi-exponential analysis. Besides we simulated a DWI signal at different signal to noise ratios to analyze the influence of noise on the ADC resulting from the mono- and bi-exponential approach.The statistical analysis of F-test, Akaike’s information criterion (AIC) and Schwarz criterion (FC) of the in-vivo data shows that the bi-exponential approach represents the “best regression” to determine ADC. In five in-vivo investigations 87% (F-test), 95 % (AIC) and 92% (SC) of the pixels possessed bi-exponential characteristics. The simulation of the DWI signal asserts increasing mono-exponential calculated ADC values with rising perfusion fraction within the tissue. Further our simulation shows that the variations of the mono-exponential results with increasing noise are less than that of bi-exponential approach. |
7 | ″ | schema:editor | N2ea49d991fb4411382ca2276bbf6effd |
8 | ″ | schema:genre | chapter |
9 | ″ | schema:isAccessibleForFree | false |
10 | ″ | schema:isPartOf | N4c91c14744b0418dac186ce16355e584 |
11 | ″ | schema:keywords | ADC |
12 | ″ | ″ | ADC values |
13 | ″ | ″ | Akaike information criterion |
14 | ″ | ″ | DWI |
15 | ″ | ″ | DWI signal |
16 | ″ | ″ | Diffusion-Weighted Magnetic Resonance Imaging |
17 | ″ | ″ | F-test |
18 | ″ | ″ | Schwarz criterion |
19 | ″ | ″ | Weighted Magnetic Resonance Imaging |
20 | ″ | ″ | abdominal organs |
21 | ″ | ″ | analysis |
22 | ″ | ″ | apparent diffusion |
23 | ″ | ″ | approach |
24 | ″ | ″ | bi-exponential analysis |
25 | ″ | ″ | brain |
26 | ″ | ″ | calculated ADC values |
27 | ″ | ″ | calculating |
28 | ″ | ″ | characteristics |
29 | ″ | ″ | comparison |
30 | ″ | ″ | constants |
31 | ″ | ″ | criteria |
32 | ″ | ″ | data |
33 | ″ | ″ | determination |
34 | ″ | ″ | different microscopic structures |
35 | ″ | ″ | different signals |
36 | ″ | ″ | differentiation |
37 | ″ | ″ | diffusion |
38 | ″ | ″ | diffusion constants |
39 | ″ | ″ | diffusion fraction |
40 | ″ | ″ | effect |
41 | ″ | ″ | fraction |
42 | ″ | ″ | good regression |
43 | ″ | ″ | healthy subjects |
44 | ″ | ″ | high vascularization |
45 | ″ | ″ | human brain |
46 | ″ | ″ | human kidney |
47 | ″ | ″ | imaging |
48 | ″ | ″ | influence |
49 | ″ | ″ | influence of noise |
50 | ″ | ″ | information criterion |
51 | ″ | ″ | kidney |
52 | ″ | ″ | magnetic resonance imaging |
53 | ″ | ″ | microscopic structure |
54 | ″ | ″ | model |
55 | ″ | ″ | mono |
56 | ″ | ″ | mono-exponential model |
57 | ″ | ″ | noise |
58 | ″ | ″ | organs |
59 | ″ | ″ | perfusion effects |
60 | ″ | ″ | perfusion fraction |
61 | ″ | ″ | pixels |
62 | ″ | ″ | point |
63 | ″ | ″ | purpose |
64 | ″ | ″ | ratio |
65 | ″ | ″ | regression |
66 | ″ | ″ | relation |
67 | ″ | ″ | resonance imaging |
68 | ″ | ″ | result of mono |
69 | ″ | ″ | results |
70 | ″ | ″ | signals |
71 | ″ | ″ | simulations |
72 | ″ | ″ | statistical analysis |
73 | ″ | ″ | statistical point |
74 | ″ | ″ | structure |
75 | ″ | ″ | subjects |
76 | ″ | ″ | tissue |
77 | ″ | ″ | use |
78 | ″ | ″ | use of diffusion |
79 | ″ | ″ | values |
80 | ″ | ″ | variation |
81 | ″ | ″ | vascularization |
82 | ″ | ″ | view |
83 | ″ | ″ | vivo data |
84 | ″ | ″ | work |
85 | ″ | schema:name | Diffusion Weighted Magnetic Resonance Imaging of the Human Kidney: “Best Regression” for the Determination of Diffusion Constants |
86 | ″ | schema:pagination | 119-122 |
87 | ″ | schema:productId | N567e3a47a3c14fc7ab837a92b15122f4 |
88 | ″ | ″ | N7ca16517ac2748079cc7e1f2582e626b |
89 | ″ | schema:publisher | N3878d9f9a3fe4be5ad02b0087efad779 |
90 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1004430735 |
91 | ″ | ″ | https://doi.org/10.1007/978-3-642-03879-2_34 |
92 | ″ | schema:sdDatePublished | 2022-08-04T17:17 |
93 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
94 | ″ | schema:sdPublisher | N6880c8f40850463bb1dd2dc33427c8b2 |
95 | ″ | schema:url | https://doi.org/10.1007/978-3-642-03879-2_34 |
96 | ″ | sgo:license | sg:explorer/license/ |
97 | ″ | sgo:sdDataset | chapters |
98 | ″ | rdf:type | schema:Chapter |
99 | N0ac2cdb6e1cf46c0b0391610addffb4d | rdf:first | sg:person.0777602654.02 |
100 | ″ | rdf:rest | rdf:nil |
101 | N2ea49d991fb4411382ca2276bbf6effd | rdf:first | N50a01518ddca44ae8a0062447a505776 |
102 | ″ | rdf:rest | N7d95377192d2472a9ab7c06f593e281a |
103 | N3878d9f9a3fe4be5ad02b0087efad779 | schema:name | Springer Nature |
104 | ″ | rdf:type | schema:Organisation |
105 | N3f5c9482d70a463688f3311856652867 | schema:familyName | Schlegel |
106 | ″ | schema:givenName | Wolfgang C. |
107 | ″ | rdf:type | schema:Person |
108 | N4c91c14744b0418dac186ce16355e584 | schema:isbn | 978-3-642-03878-5 |
109 | ″ | ″ | 978-3-642-03879-2 |
110 | ″ | schema:name | World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany |
111 | ″ | rdf:type | schema:Book |
112 | N50a01518ddca44ae8a0062447a505776 | schema:familyName | Dössel |
113 | ″ | schema:givenName | Olaf |
114 | ″ | rdf:type | schema:Person |
115 | N54c220aea3514105b5a12483abfc71d2 | rdf:first | sg:person.0675141725.09 |
116 | ″ | rdf:rest | Ndc998d4c22aa4218b802944bf97ca873 |
117 | N567e3a47a3c14fc7ab837a92b15122f4 | schema:name | dimensions_id |
118 | ″ | schema:value | pub.1004430735 |
119 | ″ | rdf:type | schema:PropertyValue |
120 | N6880c8f40850463bb1dd2dc33427c8b2 | schema:name | Springer Nature - SN SciGraph project |
121 | ″ | rdf:type | schema:Organization |
122 | N7ca16517ac2748079cc7e1f2582e626b | schema:name | doi |
123 | ″ | schema:value | 10.1007/978-3-642-03879-2_34 |
124 | ″ | rdf:type | schema:PropertyValue |
125 | N7d95377192d2472a9ab7c06f593e281a | rdf:first | N3f5c9482d70a463688f3311856652867 |
126 | ″ | rdf:rest | rdf:nil |
127 | Ndc998d4c22aa4218b802944bf97ca873 | rdf:first | sg:person.01332457401.96 |
128 | ″ | rdf:rest | Nfbcfb0bfef5445ac981ce3fe31934b7e |
129 | Nfbcfb0bfef5445ac981ce3fe31934b7e | rdf:first | sg:person.0637037231.08 |
130 | ″ | rdf:rest | N0ac2cdb6e1cf46c0b0391610addffb4d |
131 | anzsrc-for:11 | schema:inDefinedTermSet | anzsrc-for: |
132 | ″ | schema:name | Medical and Health Sciences |
133 | ″ | rdf:type | schema:DefinedTerm |
134 | anzsrc-for:1103 | schema:inDefinedTermSet | anzsrc-for: |
135 | ″ | schema:name | Clinical Sciences |
136 | ″ | rdf:type | schema:DefinedTerm |
137 | sg:person.01332457401.96 | schema:affiliation | grid-institutes:grid.14778.3d |
138 | ″ | schema:familyName | Lanzman |
139 | ″ | schema:givenName | R. S. |
140 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01332457401.96 |
141 | ″ | rdf:type | schema:Person |
142 | sg:person.0637037231.08 | schema:affiliation | grid-institutes:grid.14778.3d |
143 | ″ | schema:familyName | Mödder |
144 | ″ | schema:givenName | U. |
145 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0637037231.08 |
146 | ″ | rdf:type | schema:Person |
147 | sg:person.0675141725.09 | schema:affiliation | grid-institutes:grid.14778.3d |
148 | ″ | schema:familyName | Wittsack |
149 | ″ | schema:givenName | H. J. |
150 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675141725.09 |
151 | ″ | rdf:type | schema:Person |
152 | sg:person.0777602654.02 | schema:affiliation | grid-institutes:grid.14778.3d |
153 | ″ | schema:familyName | Blondin |
154 | ″ | schema:givenName | D. |
155 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0777602654.02 |
156 | ″ | rdf:type | schema:Person |
157 | grid-institutes:grid.14778.3d | schema:alternateName | Institute of Radiology, Düsseldorf University Hospital, Düsseldorf, Germany |
158 | ″ | schema:name | Institute of Radiology, Düsseldorf University Hospital, Düsseldorf, Germany |
159 | ″ | rdf:type | schema:Organization |