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Quantification of perfusion fMRI using a numerical model of arterial spin labeling that accounts for dynamic transit time effects
A new approach to modeling the signal observed in arterial spin labeling (ASL) experiments during changing perfusion conditions is presented in this article. The new model uses numerical methods to extend first‐order kinetic principles to include the changes in arrival time of the arterial tag that...
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Published in: | Magnetic resonance in medicine 2005-10, Vol.54 (4), p.955-964 |
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creator | Hernandez-Garcia, Luis Lee, Gregory R. Vazquez, Alberto L. Yip, Chun-Yu Noll, Douglas C. |
description | A new approach to modeling the signal observed in arterial spin labeling (ASL) experiments during changing perfusion conditions is presented in this article. The new model uses numerical methods to extend first‐order kinetic principles to include the changes in arrival time of the arterial tag that occur during neuronal activation. Estimation of the perfusion function from the ASL signal using this model is also demonstrated. The estimation algorithm uses a roughness penalty as well as prior information. The approach is demonstrated in numerical simulations and human experiments. The approach presented here is particularly suitable for fast ASL acquisition schemes, such as turbo continuous ASL (Turbo‐CASL), which allows subtraction pairs to be acquired in less than 3 s but is sensitive to arrival time changes. This modeling approach can also be extended to other acquisition schemes. Magn Reson Med, 2005. © 2005 Wiley‐Liss, Inc. |
doi_str_mv | 10.1002/mrm.20613 |
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Reson. Med</addtitle><description>A new approach to modeling the signal observed in arterial spin labeling (ASL) experiments during changing perfusion conditions is presented in this article. The new model uses numerical methods to extend first‐order kinetic principles to include the changes in arrival time of the arterial tag that occur during neuronal activation. Estimation of the perfusion function from the ASL signal using this model is also demonstrated. The estimation algorithm uses a roughness penalty as well as prior information. The approach is demonstrated in numerical simulations and human experiments. The approach presented here is particularly suitable for fast ASL acquisition schemes, such as turbo continuous ASL (Turbo‐CASL), which allows subtraction pairs to be acquired in less than 3 s but is sensitive to arrival time changes. This modeling approach can also be extended to other acquisition schemes. 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Lee, Gregory R. ; Vazquez, Alberto L. ; Yip, Chun-Yu ; Noll, Douglas C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4273-74d7a14453be94c497cc5cd3e6610fc0da2b6117345e777861824a8f45eabb9e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithms</topic><topic>arterial spin labeling</topic><topic>Arteries - anatomy & histology</topic><topic>Arteries - physiology</topic><topic>blood flow</topic><topic>Blood Flow Velocity - physiology</topic><topic>brain</topic><topic>Brain - anatomy & histology</topic><topic>Brain - blood supply</topic><topic>Brain - physiology</topic><topic>Brain Mapping - methods</topic><topic>Computer Simulation</topic><topic>fMRI</topic><topic>Humans</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Kinetics</topic><topic>Magnetic Resonance Imaging - instrumentation</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Models, Cardiovascular</topic><topic>perfusion</topic><topic>Phantoms, Imaging</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Spin Labels</topic><topic>Time Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hernandez-Garcia, Luis</creatorcontrib><creatorcontrib>Lee, Gregory R.</creatorcontrib><creatorcontrib>Vazquez, Alberto L.</creatorcontrib><creatorcontrib>Yip, Chun-Yu</creatorcontrib><creatorcontrib>Noll, Douglas C.</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>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Magnetic resonance in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hernandez-Garcia, Luis</au><au>Lee, Gregory R.</au><au>Vazquez, Alberto L.</au><au>Yip, Chun-Yu</au><au>Noll, Douglas C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantification of perfusion fMRI using a numerical model of arterial spin labeling that accounts for dynamic transit time effects</atitle><jtitle>Magnetic resonance in medicine</jtitle><addtitle>Magn. Reson. Med</addtitle><date>2005-10</date><risdate>2005</risdate><volume>54</volume><issue>4</issue><spage>955</spage><epage>964</epage><pages>955-964</pages><issn>0740-3194</issn><eissn>1522-2594</eissn><abstract>A new approach to modeling the signal observed in arterial spin labeling (ASL) experiments during changing perfusion conditions is presented in this article. The new model uses numerical methods to extend first‐order kinetic principles to include the changes in arrival time of the arterial tag that occur during neuronal activation. Estimation of the perfusion function from the ASL signal using this model is also demonstrated. The estimation algorithm uses a roughness penalty as well as prior information. The approach is demonstrated in numerical simulations and human experiments. 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subjects | Algorithms arterial spin labeling Arteries - anatomy & histology Arteries - physiology blood flow Blood Flow Velocity - physiology brain Brain - anatomy & histology Brain - blood supply Brain - physiology Brain Mapping - methods Computer Simulation fMRI Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Kinetics Magnetic Resonance Imaging - instrumentation Magnetic Resonance Imaging - methods Models, Cardiovascular perfusion Phantoms, Imaging Reproducibility of Results Sensitivity and Specificity Spin Labels Time Factors |
title | Quantification of perfusion fMRI using a numerical model of arterial spin labeling that accounts for dynamic transit time effects |
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