Non‐gaussian diffusion evaluation of the human kidney by Padé exponent model
Purpose To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects. Materials and Methods Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A r...
Gespeichert in:
| Veröffentlicht in: | Journal of magnetic resonance imaging 2018-01, Vol.47 (1), p.160-167 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 167 |
|---|---|
| container_issue | 1 |
| container_start_page | 160 |
| container_title | Journal of magnetic resonance imaging |
| container_volume | 47 |
| creator | Ljimani, Alexandra Lanzman, Rotem S. Müller‐Lutz, Anja Antoch, Gerald Wittsack, Hans‐Jörg |
| description | Purpose
To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects.
Materials and Methods
Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory‐triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b‐values [0–750 s/mm2]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R2‐test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion‐weighted imaging signal decay.
Results
The mathematical accuracy of the PEM was significantly higher than for the other three‐parameter and the monoexponential model (P |
| doi_str_mv | 10.1002/jmri.25742 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1895277452</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1975482045</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3932-33ec263b42c342622c6cd117c904063a6f5e97464e7c2b7e46d10d18d303c6173</originalsourceid><addsrcrecordid>eNp90E1O3EAQBeAWAvEXNhwgssQGIXnorv6zlxEKhGhgoihZt9rdZcYT2z1xj4HZ5Qi5Rs7BTThJPDFkkUVW9RafnkqPkGNGJ4xSOF80XTUBqQVskX0mAVKQmdoeMpU8ZRnVe-QgxgWlNM-F3CV7kAk9QLFPZrehff7x8872MVa2TXxVln2sQpvgva17u9rEUCarOSbzvhnEt8q3uE6KdfLJ-qdfCT4uQ4vtKmmCx_oN2SltHfHo5R6Sr5fvv1x8SKezq-uLd9PU8ZxDyjk6ULwQ4LgABeCU84xpl1NBFbeqlJhroQRqB4VGoTyjnmWeU-4U0_yQnI69yy587zGuTFNFh3VtWwx9NCzLJWgtJAz05B-6CH3XDt8ZlmspMqBCDupsVK4LMXZYmmVXNbZbG0bNZmazmdn8mXnAb18q-6JB_5e-7joANoKHqsb1f6rMx5vP12Ppb9MLh5c</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1975482045</pqid></control><display><type>article</type><title>Non‐gaussian diffusion evaluation of the human kidney by Padé exponent model</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><source>Wiley Free Content</source><creator>Ljimani, Alexandra ; Lanzman, Rotem S. ; Müller‐Lutz, Anja ; Antoch, Gerald ; Wittsack, Hans‐Jörg</creator><creatorcontrib>Ljimani, Alexandra ; Lanzman, Rotem S. ; Müller‐Lutz, Anja ; Antoch, Gerald ; Wittsack, Hans‐Jörg</creatorcontrib><description>Purpose
To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects.
Materials and Methods
Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory‐triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b‐values [0–750 s/mm2]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R2‐test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion‐weighted imaging signal decay.
Results
The mathematical accuracy of the PEM was significantly higher than for the other three‐parameter and the monoexponential model (P < 0.05), which enables more precise information about the deviation of the Gaussian behavior of the diffusion signal by the PEM. The biexponential model showed better fitting to the diffusion signal (medullar
Rbi2 0.989 ± 0.008, AICcbi 113.3 ± 6.6; cortical
Rbi2 0.992 ± 0.006, AICcbi 113.3 ± 5.2) than the three‐parameter models (medullar
RPadé2 0.965 ± 0.016, AICcPadé 122.6 ± 6.4,
RK2 0.954 ± 0.019, AICcK 128.5 ± 6.0; cortical
RPadé2 0.989 ± 0.005, AICcPadé 116.3 ± 4.4,
RK2 0.985 ± 0.007, AICcK 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullar
Rmono2 0.898 ± 0.039, AICcmono 141.4 ± 5.6; cortical
Rmono2 0.961 ± 0.013, AICcmono 135.4 ± 4.8).
Conclusion
The PEM is a novel promising approach to quantify diffusion properties in the human kidney and might further improve functional renal MR imaging.
Level of Evidence: 1
Technical Efficacy: Stage 1
J. Magn. Reson. Imaging 2018;47:160–167.</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.25742</identifier><identifier>PMID: 28471524</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Adult ; Algorithms ; Diffusion ; Diffusion Magnetic Resonance Imaging ; DWI ; Feasibility studies ; Female ; Field of view ; Functional magnetic resonance imaging ; Glomerular Filtration Rate ; Healthy Volunteers ; Humans ; Image Processing, Computer-Assisted ; Kidney - diagnostic imaging ; Kidneys ; Kurtosis ; Magnetic resonance imaging ; Male ; Mathematical models ; Matrix methods ; Measurement methods ; Models, Anatomic ; Models, Theoretical ; non‐Gaussian diffusion ; Normal Distribution ; Padé exponent model ; Regression analysis ; renal fMRI ; Young Adult</subject><ispartof>Journal of magnetic resonance imaging, 2018-01, Vol.47 (1), p.160-167</ispartof><rights>2017 International Society for Magnetic Resonance in Medicine</rights><rights>2017 International Society for Magnetic Resonance in Medicine.</rights><rights>2018 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3932-33ec263b42c342622c6cd117c904063a6f5e97464e7c2b7e46d10d18d303c6173</citedby><cites>FETCH-LOGICAL-c3932-33ec263b42c342622c6cd117c904063a6f5e97464e7c2b7e46d10d18d303c6173</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.25742$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.25742$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,1427,27903,27904,45553,45554,46388,46812</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28471524$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ljimani, Alexandra</creatorcontrib><creatorcontrib>Lanzman, Rotem S.</creatorcontrib><creatorcontrib>Müller‐Lutz, Anja</creatorcontrib><creatorcontrib>Antoch, Gerald</creatorcontrib><creatorcontrib>Wittsack, Hans‐Jörg</creatorcontrib><title>Non‐gaussian diffusion evaluation of the human kidney by Padé exponent model</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Purpose
To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects.
Materials and Methods
Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory‐triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b‐values [0–750 s/mm2]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R2‐test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion‐weighted imaging signal decay.
Results
The mathematical accuracy of the PEM was significantly higher than for the other three‐parameter and the monoexponential model (P < 0.05), which enables more precise information about the deviation of the Gaussian behavior of the diffusion signal by the PEM. The biexponential model showed better fitting to the diffusion signal (medullar
Rbi2 0.989 ± 0.008, AICcbi 113.3 ± 6.6; cortical
Rbi2 0.992 ± 0.006, AICcbi 113.3 ± 5.2) than the three‐parameter models (medullar
RPadé2 0.965 ± 0.016, AICcPadé 122.6 ± 6.4,
RK2 0.954 ± 0.019, AICcK 128.5 ± 6.0; cortical
RPadé2 0.989 ± 0.005, AICcPadé 116.3 ± 4.4,
RK2 0.985 ± 0.007, AICcK 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullar
Rmono2 0.898 ± 0.039, AICcmono 141.4 ± 5.6; cortical
Rmono2 0.961 ± 0.013, AICcmono 135.4 ± 4.8).
Conclusion
The PEM is a novel promising approach to quantify diffusion properties in the human kidney and might further improve functional renal MR imaging.
Level of Evidence: 1
Technical Efficacy: Stage 1
J. Magn. Reson. Imaging 2018;47:160–167.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Diffusion</subject><subject>Diffusion Magnetic Resonance Imaging</subject><subject>DWI</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Field of view</subject><subject>Functional magnetic resonance imaging</subject><subject>Glomerular Filtration Rate</subject><subject>Healthy Volunteers</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Kidney - diagnostic imaging</subject><subject>Kidneys</subject><subject>Kurtosis</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Matrix methods</subject><subject>Measurement methods</subject><subject>Models, Anatomic</subject><subject>Models, Theoretical</subject><subject>non‐Gaussian diffusion</subject><subject>Normal Distribution</subject><subject>Padé exponent model</subject><subject>Regression analysis</subject><subject>renal fMRI</subject><subject>Young Adult</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90E1O3EAQBeAWAvEXNhwgssQGIXnorv6zlxEKhGhgoihZt9rdZcYT2z1xj4HZ5Qi5Rs7BTThJPDFkkUVW9RafnkqPkGNGJ4xSOF80XTUBqQVskX0mAVKQmdoeMpU8ZRnVe-QgxgWlNM-F3CV7kAk9QLFPZrehff7x8872MVa2TXxVln2sQpvgva17u9rEUCarOSbzvhnEt8q3uE6KdfLJ-qdfCT4uQ4vtKmmCx_oN2SltHfHo5R6Sr5fvv1x8SKezq-uLd9PU8ZxDyjk6ULwQ4LgABeCU84xpl1NBFbeqlJhroQRqB4VGoTyjnmWeU-4U0_yQnI69yy587zGuTFNFh3VtWwx9NCzLJWgtJAz05B-6CH3XDt8ZlmspMqBCDupsVK4LMXZYmmVXNbZbG0bNZmazmdn8mXnAb18q-6JB_5e-7joANoKHqsb1f6rMx5vP12Ppb9MLh5c</recordid><startdate>201801</startdate><enddate>201801</enddate><creator>Ljimani, Alexandra</creator><creator>Lanzman, Rotem S.</creator><creator>Müller‐Lutz, Anja</creator><creator>Antoch, Gerald</creator><creator>Wittsack, Hans‐Jörg</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>201801</creationdate><title>Non‐gaussian diffusion evaluation of the human kidney by Padé exponent model</title><author>Ljimani, Alexandra ; Lanzman, Rotem S. ; Müller‐Lutz, Anja ; Antoch, Gerald ; Wittsack, Hans‐Jörg</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3932-33ec263b42c342622c6cd117c904063a6f5e97464e7c2b7e46d10d18d303c6173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Diffusion</topic><topic>Diffusion Magnetic Resonance Imaging</topic><topic>DWI</topic><topic>Feasibility studies</topic><topic>Female</topic><topic>Field of view</topic><topic>Functional magnetic resonance imaging</topic><topic>Glomerular Filtration Rate</topic><topic>Healthy Volunteers</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Kidney - diagnostic imaging</topic><topic>Kidneys</topic><topic>Kurtosis</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Matrix methods</topic><topic>Measurement methods</topic><topic>Models, Anatomic</topic><topic>Models, Theoretical</topic><topic>non‐Gaussian diffusion</topic><topic>Normal Distribution</topic><topic>Padé exponent model</topic><topic>Regression analysis</topic><topic>renal fMRI</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ljimani, Alexandra</creatorcontrib><creatorcontrib>Lanzman, Rotem S.</creatorcontrib><creatorcontrib>Müller‐Lutz, Anja</creatorcontrib><creatorcontrib>Antoch, Gerald</creatorcontrib><creatorcontrib>Wittsack, Hans‐Jörg</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ljimani, Alexandra</au><au>Lanzman, Rotem S.</au><au>Müller‐Lutz, Anja</au><au>Antoch, Gerald</au><au>Wittsack, Hans‐Jörg</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Non‐gaussian diffusion evaluation of the human kidney by Padé exponent model</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2018-01</date><risdate>2018</risdate><volume>47</volume><issue>1</issue><spage>160</spage><epage>167</epage><pages>160-167</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Purpose
To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects.
Materials and Methods
Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory‐triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b‐values [0–750 s/mm2]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R2‐test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion‐weighted imaging signal decay.
Results
The mathematical accuracy of the PEM was significantly higher than for the other three‐parameter and the monoexponential model (P < 0.05), which enables more precise information about the deviation of the Gaussian behavior of the diffusion signal by the PEM. The biexponential model showed better fitting to the diffusion signal (medullar
Rbi2 0.989 ± 0.008, AICcbi 113.3 ± 6.6; cortical
Rbi2 0.992 ± 0.006, AICcbi 113.3 ± 5.2) than the three‐parameter models (medullar
RPadé2 0.965 ± 0.016, AICcPadé 122.6 ± 6.4,
RK2 0.954 ± 0.019, AICcK 128.5 ± 6.0; cortical
RPadé2 0.989 ± 0.005, AICcPadé 116.3 ± 4.4,
RK2 0.985 ± 0.007, AICcK 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullar
Rmono2 0.898 ± 0.039, AICcmono 141.4 ± 5.6; cortical
Rmono2 0.961 ± 0.013, AICcmono 135.4 ± 4.8).
Conclusion
The PEM is a novel promising approach to quantify diffusion properties in the human kidney and might further improve functional renal MR imaging.
Level of Evidence: 1
Technical Efficacy: Stage 1
J. Magn. Reson. Imaging 2018;47:160–167.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>28471524</pmid><doi>10.1002/jmri.25742</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1053-1807 |
| ispartof | Journal of magnetic resonance imaging, 2018-01, Vol.47 (1), p.160-167 |
| issn | 1053-1807 1522-2586 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1895277452 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; Wiley Free Content |
| subjects | Adult Algorithms Diffusion Diffusion Magnetic Resonance Imaging DWI Feasibility studies Female Field of view Functional magnetic resonance imaging Glomerular Filtration Rate Healthy Volunteers Humans Image Processing, Computer-Assisted Kidney - diagnostic imaging Kidneys Kurtosis Magnetic resonance imaging Male Mathematical models Matrix methods Measurement methods Models, Anatomic Models, Theoretical non‐Gaussian diffusion Normal Distribution Padé exponent model Regression analysis renal fMRI Young Adult |
| title | Non‐gaussian diffusion evaluation of the human kidney by Padé exponent model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T17%3A53%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Non%E2%80%90gaussian%20diffusion%20evaluation%20of%20the%20human%20kidney%20by%20Pad%C3%A9%20exponent%20model&rft.jtitle=Journal%20of%20magnetic%20resonance%20imaging&rft.au=Ljimani,%20Alexandra&rft.date=2018-01&rft.volume=47&rft.issue=1&rft.spage=160&rft.epage=167&rft.pages=160-167&rft.issn=1053-1807&rft.eissn=1522-2586&rft_id=info:doi/10.1002/jmri.25742&rft_dat=%3Cproquest_cross%3E1975482045%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1975482045&rft_id=info:pmid/28471524&rfr_iscdi=true |