Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements.
A major assumption in arterial spin labeling (ASL) MRI perfusion quantification is the time course of the signal on arrival in the capillary network. The normally assumed square label profile is not preserved during transit of the label through the vasculature. This change in profile can be attribut...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Sprog: | English |
| Udgivet: |
2013
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| _version_ | 1826263155326582784 |
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| author | Chappell, M Woolrich, M Kazan, S Jezzard, P Payne, S MacIntosh, B |
| author_facet | Chappell, M Woolrich, M Kazan, S Jezzard, P Payne, S MacIntosh, B |
| author_sort | Chappell, M |
| collection | OXFORD |
| description | A major assumption in arterial spin labeling (ASL) MRI perfusion quantification is the time course of the signal on arrival in the capillary network. The normally assumed square label profile is not preserved during transit of the label through the vasculature. This change in profile can be attributed to a number of effects collectively denoted as dispersion. A number of models for this effect have been proposed, but they have been difficult to validate. In this study ASL data acquired whilst the label was still within larger arteries was used to compare models of label dispersion. Models were fit using a probabilistic algorithm and evaluated according to their ability to fit the data. Data from an elderly population were considered including both healthy controls and patients with a variety of vascular disease. The authors conclude that modeling ASL dispersion using a convolution of the ideal ASL label profile with a dispersion kernel is most appropriate, where the kernel itself takes the form of a gamma distribution. This model provided a best fit to the data considered, was consistent with the measured flow profile in arteries and was sufficiently mathematically simple to make it practical for ASL tissue perfusion quantification. |
| first_indexed | 2024-03-06T19:47:12Z |
| format | Journal article |
| id | oxford-uuid:22b7490f-269c-4a98-ad3b-1c6e11bd1bf0 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:47:12Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:22b7490f-269c-4a98-ad3b-1c6e11bd1bf02022-03-26T11:40:15ZModeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:22b7490f-269c-4a98-ad3b-1c6e11bd1bf0EnglishSymplectic Elements at Oxford2013Chappell, MWoolrich, MKazan, SJezzard, PPayne, SMacIntosh, BA major assumption in arterial spin labeling (ASL) MRI perfusion quantification is the time course of the signal on arrival in the capillary network. The normally assumed square label profile is not preserved during transit of the label through the vasculature. This change in profile can be attributed to a number of effects collectively denoted as dispersion. A number of models for this effect have been proposed, but they have been difficult to validate. In this study ASL data acquired whilst the label was still within larger arteries was used to compare models of label dispersion. Models were fit using a probabilistic algorithm and evaluated according to their ability to fit the data. Data from an elderly population were considered including both healthy controls and patients with a variety of vascular disease. The authors conclude that modeling ASL dispersion using a convolution of the ideal ASL label profile with a dispersion kernel is most appropriate, where the kernel itself takes the form of a gamma distribution. This model provided a best fit to the data considered, was consistent with the measured flow profile in arteries and was sufficiently mathematically simple to make it practical for ASL tissue perfusion quantification. |
| spellingShingle | Chappell, M Woolrich, M Kazan, S Jezzard, P Payne, S MacIntosh, B Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. |
| title | Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. |
| title_full | Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. |
| title_fullStr | Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. |
| title_full_unstemmed | Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. |
| title_short | Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. |
| title_sort | modeling dispersion in arterial spin labeling validation using dynamic angiographic measurements |
| work_keys_str_mv | AT chappellm modelingdispersioninarterialspinlabelingvalidationusingdynamicangiographicmeasurements AT woolrichm modelingdispersioninarterialspinlabelingvalidationusingdynamicangiographicmeasurements AT kazans modelingdispersioninarterialspinlabelingvalidationusingdynamicangiographicmeasurements AT jezzardp modelingdispersioninarterialspinlabelingvalidationusingdynamicangiographicmeasurements AT paynes modelingdispersioninarterialspinlabelingvalidationusingdynamicangiographicmeasurements AT macintoshb modelingdispersioninarterialspinlabelingvalidationusingdynamicangiographicmeasurements |