Curve fitting criteria to determine arterial input function for mr perfusion analysis

Adam Huang, Chung Wei Lee, Hon Man Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The purpose of this study is to develop a fully automatic algorithm for determining a 'proper' arterial input function (AIF) that is critical in the deconvolution approach for cerebral perfusion quantification. We proposed using a fast gamma variate model (GVM) fitting strategy to scout the whole brain dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) dataset for AIF candidates. Goodness-of-fit criteria such as signal to noise ratios and GVM peak shapes were first used to screen out voxels of noisy signals and non-AIF-shaped concentration-time curves respectively. Last, qualified AIF candidates were ranked by bolus peak arrival time and peak width. Our method was tested by 10 DSC-MRI datasets: 5 adults (24-52 years of age) with stenosis or occlusion, and 5 youths (9-18 years of age) with moyamoya disease. The preliminary results indicated that the proposed algorithm was able to detect AIFs robustly and efficiently under 1 minute.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1809-1812
Number of pages4
ISBN (Electronic)9781538636411
DOIs
StatePublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period8/04/1911/04/19

Keywords

  • Arterial input function
  • Deconvolution
  • Gamma variate model
  • MR perfusion

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