Assessing global and regional iron content in deep gray matter as a function of age using susceptibility mapping
Purpose To investigate the correlation of non‐heme iron content in deep gray matter nuclei as a function of age using quantitative susceptibility mapping (QSM) from both whole‐structural and regional perspectives. Materials and Methods We studied a group of 174 normal subjects ranging from 20 to 69...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2016-07, Vol.44 (1), p.59-71 |
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| creator | Liu, Manju Liu, Saifeng Ghassaban, Kiarash Zheng, Weili Dicicco, Dane Miao, Yanwei Habib, Charbel Jazmati, Tarek Haacke, E. Mark |
| description | Purpose
To investigate the correlation of non‐heme iron content in deep gray matter nuclei as a function of age using quantitative susceptibility mapping (QSM) from both whole‐structural and regional perspectives.
Materials and Methods
We studied a group of 174 normal subjects ranging from 20 to 69 years old and measured the magnetic susceptibility of seven subcortical gray matter nuclei. SWI (susceptibility‐weighted imaging) phase images were used to generate the susceptibility maps, which were acquired on a 1.5T scanner. The 3D whole‐structural measurements were used to determine age‐related thresholds, which were applied to calculate the local iron deposition (RII: portion of the structure that contains iron concentration larger than the structure threshold). Age‐susceptibility correlation was reported for each measured structure for both the whole‐region and two‐region (low iron and high iron content regions) analysis.
Results
For the local high iron content region, a strong age‐susceptibility correlation was found in the caudate nucleus (CN,R = 0.9), putamen (PUT,R = 0.9), red nucleus (RN,R = 0.8), globus pallidus (GP,R = 0.7), substantia nigra (SN,R = 0.5), and pulvinar thalamus (PT,R = 0.5); for the global iron content, a strong age‐susceptibility correlation was found in CN(R = 0.6), PUT(R = 0.7), and RN(R = 0.6). Overall, for each structure analyzed in this study, regional analysis showed higher correlation coefficient and higher slope comparing to the whole‐region analysis. Further, we found the quantitative conversion factor between magnetic susceptibility and iron concentration to be 1.03 ± 0.03 ppb per μg iron/g wet tissue.
Conclusion
We conclude that the age‐susceptibility correlation can serve as a quantitative magnetic susceptibility baseline as a function of age for monitoring abnormal global and regional iron deposition. A regional analysis has shown a tighter age related behavior, providing a reliable and sensitive reference for what can be considered normal iron content for studies of neurodegenerative diseases. J. Magn. Reson. Imaging 2016;44:59–71. |
| doi_str_mv | 10.1002/jmri.25130 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1808661943</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1808661943</sourcerecordid><originalsourceid>FETCH-LOGICAL-c6000-c6adfa018b88184c5abc268d0287803c1754fb4745ee3ecd54a5843baf624c273</originalsourceid><addsrcrecordid>eNqFkU1rFTEYhYMotlY3_gAJuBFhar6TWZaL1pa2SqlU3IRMJjPkOpNMkxn0_ntz7227cKGbJG94zuHwHgBeY3SMESIf1mPyx4Rjip6AQ8wJqQhX4ml5I04rrJA8AC9yXiOE6prx5-CACFFzRdkhmE5ydjn70MN-iI0ZoAktTK73MZTBpxigjWF2YYY-wNa5CfbJbOBo5tklaDI0sFuCnYsAxg6a3sFl55eXbN00-8YPft4Kpql8vwTPOjNk9-r-PgLfPn28WX2uLr6cnq1OLiorSs5ymrYzCKtGKayY5aaxRKgWESUVohZLzrqGScado862nBmuGG1MJwizRNIj8G7vO6V4t7g869GXPMNggotL1mUtSghcM_p_VNZSSckEKejbv9B1XFLZ1I4SomQVolDv95RNMefkOj0lP5q00RjpbWV6W5neVVbgN_eWSzO69hF96KgAeA_88oPb_MNKn19enz2YVnuNz7P7_agx6acWkkqub69O9e2Kn39FP2r9nf4B3aewKQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1796681866</pqid></control><display><type>article</type><title>Assessing global and regional iron content in deep gray matter as a function of age using susceptibility mapping</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><source>Wiley Online Library (Open Access Collection)</source><creator>Liu, Manju ; Liu, Saifeng ; Ghassaban, Kiarash ; Zheng, Weili ; Dicicco, Dane ; Miao, Yanwei ; Habib, Charbel ; Jazmati, Tarek ; Haacke, E. Mark</creator><creatorcontrib>Liu, Manju ; Liu, Saifeng ; Ghassaban, Kiarash ; Zheng, Weili ; Dicicco, Dane ; Miao, Yanwei ; Habib, Charbel ; Jazmati, Tarek ; Haacke, E. Mark</creatorcontrib><description>Purpose
To investigate the correlation of non‐heme iron content in deep gray matter nuclei as a function of age using quantitative susceptibility mapping (QSM) from both whole‐structural and regional perspectives.
Materials and Methods
We studied a group of 174 normal subjects ranging from 20 to 69 years old and measured the magnetic susceptibility of seven subcortical gray matter nuclei. SWI (susceptibility‐weighted imaging) phase images were used to generate the susceptibility maps, which were acquired on a 1.5T scanner. The 3D whole‐structural measurements were used to determine age‐related thresholds, which were applied to calculate the local iron deposition (RII: portion of the structure that contains iron concentration larger than the structure threshold). Age‐susceptibility correlation was reported for each measured structure for both the whole‐region and two‐region (low iron and high iron content regions) analysis.
Results
For the local high iron content region, a strong age‐susceptibility correlation was found in the caudate nucleus (CN,R = 0.9), putamen (PUT,R = 0.9), red nucleus (RN,R = 0.8), globus pallidus (GP,R = 0.7), substantia nigra (SN,R = 0.5), and pulvinar thalamus (PT,R = 0.5); for the global iron content, a strong age‐susceptibility correlation was found in CN(R = 0.6), PUT(R = 0.7), and RN(R = 0.6). Overall, for each structure analyzed in this study, regional analysis showed higher correlation coefficient and higher slope comparing to the whole‐region analysis. Further, we found the quantitative conversion factor between magnetic susceptibility and iron concentration to be 1.03 ± 0.03 ppb per μg iron/g wet tissue.
Conclusion
We conclude that the age‐susceptibility correlation can serve as a quantitative magnetic susceptibility baseline as a function of age for monitoring abnormal global and regional iron deposition. A regional analysis has shown a tighter age related behavior, providing a reliable and sensitive reference for what can be considered normal iron content for studies of neurodegenerative diseases. J. Magn. Reson. Imaging 2016;44:59–71.</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.25130</identifier><identifier>PMID: 26695834</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Adult ; Aged ; Aging - metabolism ; Brain - metabolism ; brain iron ; Female ; ferritin ; Gray Matter - metabolism ; Humans ; Image Interpretation, Computer-Assisted - methods ; Iron - metabolism ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Middle Aged ; Molecular Imaging - methods ; quantitative susceptibility mapping (QSM) ; Reproducibility of Results ; Sensitivity and Specificity ; Tissue Distribution ; two-region analysis (RII)</subject><ispartof>Journal of magnetic resonance imaging, 2016-07, Vol.44 (1), p.59-71</ispartof><rights>2015 Wiley Periodicals, Inc.</rights><rights>2016 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6000-c6adfa018b88184c5abc268d0287803c1754fb4745ee3ecd54a5843baf624c273</citedby><cites>FETCH-LOGICAL-c6000-c6adfa018b88184c5abc268d0287803c1754fb4745ee3ecd54a5843baf624c273</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.25130$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.25130$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26695834$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Manju</creatorcontrib><creatorcontrib>Liu, Saifeng</creatorcontrib><creatorcontrib>Ghassaban, Kiarash</creatorcontrib><creatorcontrib>Zheng, Weili</creatorcontrib><creatorcontrib>Dicicco, Dane</creatorcontrib><creatorcontrib>Miao, Yanwei</creatorcontrib><creatorcontrib>Habib, Charbel</creatorcontrib><creatorcontrib>Jazmati, Tarek</creatorcontrib><creatorcontrib>Haacke, E. Mark</creatorcontrib><title>Assessing global and regional iron content in deep gray matter as a function of age using susceptibility mapping</title><title>Journal of magnetic resonance imaging</title><addtitle>J. Magn. Reson. Imaging</addtitle><description>Purpose
To investigate the correlation of non‐heme iron content in deep gray matter nuclei as a function of age using quantitative susceptibility mapping (QSM) from both whole‐structural and regional perspectives.
Materials and Methods
We studied a group of 174 normal subjects ranging from 20 to 69 years old and measured the magnetic susceptibility of seven subcortical gray matter nuclei. SWI (susceptibility‐weighted imaging) phase images were used to generate the susceptibility maps, which were acquired on a 1.5T scanner. The 3D whole‐structural measurements were used to determine age‐related thresholds, which were applied to calculate the local iron deposition (RII: portion of the structure that contains iron concentration larger than the structure threshold). Age‐susceptibility correlation was reported for each measured structure for both the whole‐region and two‐region (low iron and high iron content regions) analysis.
Results
For the local high iron content region, a strong age‐susceptibility correlation was found in the caudate nucleus (CN,R = 0.9), putamen (PUT,R = 0.9), red nucleus (RN,R = 0.8), globus pallidus (GP,R = 0.7), substantia nigra (SN,R = 0.5), and pulvinar thalamus (PT,R = 0.5); for the global iron content, a strong age‐susceptibility correlation was found in CN(R = 0.6), PUT(R = 0.7), and RN(R = 0.6). Overall, for each structure analyzed in this study, regional analysis showed higher correlation coefficient and higher slope comparing to the whole‐region analysis. Further, we found the quantitative conversion factor between magnetic susceptibility and iron concentration to be 1.03 ± 0.03 ppb per μg iron/g wet tissue.
Conclusion
We conclude that the age‐susceptibility correlation can serve as a quantitative magnetic susceptibility baseline as a function of age for monitoring abnormal global and regional iron deposition. A regional analysis has shown a tighter age related behavior, providing a reliable and sensitive reference for what can be considered normal iron content for studies of neurodegenerative diseases. J. Magn. Reson. Imaging 2016;44:59–71.</description><subject>Adult</subject><subject>Aged</subject><subject>Aging - metabolism</subject><subject>Brain - metabolism</subject><subject>brain iron</subject><subject>Female</subject><subject>ferritin</subject><subject>Gray Matter - metabolism</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Iron - metabolism</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Molecular Imaging - methods</subject><subject>quantitative susceptibility mapping (QSM)</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Tissue Distribution</subject><subject>two-region analysis (RII)</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1rFTEYhYMotlY3_gAJuBFhar6TWZaL1pa2SqlU3IRMJjPkOpNMkxn0_ntz7227cKGbJG94zuHwHgBeY3SMESIf1mPyx4Rjip6AQ8wJqQhX4ml5I04rrJA8AC9yXiOE6prx5-CACFFzRdkhmE5ydjn70MN-iI0ZoAktTK73MZTBpxigjWF2YYY-wNa5CfbJbOBo5tklaDI0sFuCnYsAxg6a3sFl55eXbN00-8YPft4Kpql8vwTPOjNk9-r-PgLfPn28WX2uLr6cnq1OLiorSs5ymrYzCKtGKayY5aaxRKgWESUVohZLzrqGScado862nBmuGG1MJwizRNIj8G7vO6V4t7g869GXPMNggotL1mUtSghcM_p_VNZSSckEKejbv9B1XFLZ1I4SomQVolDv95RNMefkOj0lP5q00RjpbWV6W5neVVbgN_eWSzO69hF96KgAeA_88oPb_MNKn19enz2YVnuNz7P7_agx6acWkkqub69O9e2Kn39FP2r9nf4B3aewKQ</recordid><startdate>201607</startdate><enddate>201607</enddate><creator>Liu, Manju</creator><creator>Liu, Saifeng</creator><creator>Ghassaban, Kiarash</creator><creator>Zheng, Weili</creator><creator>Dicicco, Dane</creator><creator>Miao, Yanwei</creator><creator>Habib, Charbel</creator><creator>Jazmati, Tarek</creator><creator>Haacke, E. Mark</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><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>201607</creationdate><title>Assessing global and regional iron content in deep gray matter as a function of age using susceptibility mapping</title><author>Liu, Manju ; Liu, Saifeng ; Ghassaban, Kiarash ; Zheng, Weili ; Dicicco, Dane ; Miao, Yanwei ; Habib, Charbel ; Jazmati, Tarek ; Haacke, E. Mark</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6000-c6adfa018b88184c5abc268d0287803c1754fb4745ee3ecd54a5843baf624c273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aging - metabolism</topic><topic>Brain - metabolism</topic><topic>brain iron</topic><topic>Female</topic><topic>ferritin</topic><topic>Gray Matter - metabolism</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Iron - metabolism</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Molecular Imaging - methods</topic><topic>quantitative susceptibility mapping (QSM)</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Tissue Distribution</topic><topic>two-region analysis (RII)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Manju</creatorcontrib><creatorcontrib>Liu, Saifeng</creatorcontrib><creatorcontrib>Ghassaban, Kiarash</creatorcontrib><creatorcontrib>Zheng, Weili</creatorcontrib><creatorcontrib>Dicicco, Dane</creatorcontrib><creatorcontrib>Miao, Yanwei</creatorcontrib><creatorcontrib>Habib, Charbel</creatorcontrib><creatorcontrib>Jazmati, Tarek</creatorcontrib><creatorcontrib>Haacke, E. Mark</creatorcontrib><collection>Istex</collection><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>Liu, Manju</au><au>Liu, Saifeng</au><au>Ghassaban, Kiarash</au><au>Zheng, Weili</au><au>Dicicco, Dane</au><au>Miao, Yanwei</au><au>Habib, Charbel</au><au>Jazmati, Tarek</au><au>Haacke, E. Mark</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing global and regional iron content in deep gray matter as a function of age using susceptibility mapping</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J. Magn. Reson. Imaging</addtitle><date>2016-07</date><risdate>2016</risdate><volume>44</volume><issue>1</issue><spage>59</spage><epage>71</epage><pages>59-71</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Purpose
To investigate the correlation of non‐heme iron content in deep gray matter nuclei as a function of age using quantitative susceptibility mapping (QSM) from both whole‐structural and regional perspectives.
Materials and Methods
We studied a group of 174 normal subjects ranging from 20 to 69 years old and measured the magnetic susceptibility of seven subcortical gray matter nuclei. SWI (susceptibility‐weighted imaging) phase images were used to generate the susceptibility maps, which were acquired on a 1.5T scanner. The 3D whole‐structural measurements were used to determine age‐related thresholds, which were applied to calculate the local iron deposition (RII: portion of the structure that contains iron concentration larger than the structure threshold). Age‐susceptibility correlation was reported for each measured structure for both the whole‐region and two‐region (low iron and high iron content regions) analysis.
Results
For the local high iron content region, a strong age‐susceptibility correlation was found in the caudate nucleus (CN,R = 0.9), putamen (PUT,R = 0.9), red nucleus (RN,R = 0.8), globus pallidus (GP,R = 0.7), substantia nigra (SN,R = 0.5), and pulvinar thalamus (PT,R = 0.5); for the global iron content, a strong age‐susceptibility correlation was found in CN(R = 0.6), PUT(R = 0.7), and RN(R = 0.6). Overall, for each structure analyzed in this study, regional analysis showed higher correlation coefficient and higher slope comparing to the whole‐region analysis. Further, we found the quantitative conversion factor between magnetic susceptibility and iron concentration to be 1.03 ± 0.03 ppb per μg iron/g wet tissue.
Conclusion
We conclude that the age‐susceptibility correlation can serve as a quantitative magnetic susceptibility baseline as a function of age for monitoring abnormal global and regional iron deposition. A regional analysis has shown a tighter age related behavior, providing a reliable and sensitive reference for what can be considered normal iron content for studies of neurodegenerative diseases. J. Magn. Reson. Imaging 2016;44:59–71.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>26695834</pmid><doi>10.1002/jmri.25130</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Aged Aging - metabolism Brain - metabolism brain iron Female ferritin Gray Matter - metabolism Humans Image Interpretation, Computer-Assisted - methods Iron - metabolism Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Middle Aged Molecular Imaging - methods quantitative susceptibility mapping (QSM) Reproducibility of Results Sensitivity and Specificity Tissue Distribution two-region analysis (RII) |
| title | Assessing global and regional iron content in deep gray matter as a function of age using susceptibility mapping |
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