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Research ArticleBRAIN

Automated Method for Generating the Arterial Input Function on Perfusion-Weighted MR Imaging: Validation in Patients with Stroke

Michael Mlynash, Irina Eyngorn, Roland Bammer, Michael Moseley and David C. Tong
American Journal of Neuroradiology June 2005, 26 (6) 1479-1486;
Michael Mlynash
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Irina Eyngorn
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Roland Bammer
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Michael Moseley
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David C. Tong
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  • Article
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Article Figures & Data

Figures

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  • Fig 1.
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    Fig 1.

    Manual and automated choice of the best AIF.

    A, Manual choice, based on visual estimation of the overall shape of the curve, high signal intensity, early peak time, and small width.

    B, Automatically computed AIF is identified as the one with the optimal combination of Gaussian-fit parameters (maximum A0 combined with A1 and A2 in the predefined limits) and the one satisfying the goodness-of-fit test.

  • Fig 2.
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    Fig 2.

    AIF identified at voxels 24 mm apart.

    A, Manual.

    B, Automated.

  • Fig 3.
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    Fig 3.

    Tmax and CBF maps, created on the basis of the manual (A, C) and automated (B, D) AIFs from Figure 1, have spatial pattern correlations of r = 0.87 and r = 0.86, respectively. Distance between corresponding AIF voxels, or d, is 30.6 mm.

  • Fig 4.
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    Fig 4.

    Tmax and CBF maps (A, C) and (B, D) corresponding to Figure 2 have spatial pattern correlations of r = 0.74 and r = 0.60, respectively.

Tables

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  • Comparison of automated and manual AIF parameters

    SubjectΔA0 (sec−1)ΔA1 (sec)ΔA2 (sec)d (mm)Correlation r*
    TmaxCBF
    121.520.690.3411.30.910.94
    217.091.32−0.1715.90.870.92
    30000.91.001.00
    419.75−0.59−0.377.10.820.66
    547.48−2.98−1.2311.40.700.60
    611.390.32−0.8832.20.820.72
    729.06−0.39−1.2818.90.680.74
    837.220.57−0.6818.20.760.83
    912.830.120.766.00.750.72
    1032.611.30−0.3918.20.810.87
    1125.22−0.79−0.1730.60.870.86
    12−4.32−2.65−2.1535.90.810.82
    131.24−0.24−0.3037.80.730.82
    1427.30−2.46−0.2424.00.740.60
    1515.26−0.19−0.073.00.850.88
    1617.830.361.362.70.800.77
    172.71−5.25−2.1835.00.540.68
    180001.31.001.00
    1919.27−0.62−0.0916.90.870.87
    2011.780.71−0.8744.50.820.89
    2110.17−0.19−0.3011.60.850.87
    220001.31.001.00
    Mean16.16−0.50−0.4117.50.820.82
    SD13.49NANA13.450.110.12
    P value<0.0010.3740.018NANANA
    • Note.—Differences were computed as the automated AIF parameter − manual AIF parameter. A0 = curve height (maximum concentration of contrast agent), A1 = curve center (time to peak), A2 = curve width, d = distance between automated and manual AIF pixels, NA = not applicable.

    • * Between automated and manual maps.

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American Journal of Neuroradiology: 26 (6)
American Journal of Neuroradiology
Vol. 26, Issue 6
1 Jun 2005
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Automated Method for Generating the Arterial Input Function on Perfusion-Weighted MR Imaging: Validation in Patients with Stroke
Michael Mlynash, Irina Eyngorn, Roland Bammer, Michael Moseley, David C. Tong
American Journal of Neuroradiology Jun 2005, 26 (6) 1479-1486;

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Automated Method for Generating the Arterial Input Function on Perfusion-Weighted MR Imaging: Validation in Patients with Stroke
Michael Mlynash, Irina Eyngorn, Roland Bammer, Michael Moseley, David C. Tong
American Journal of Neuroradiology Jun 2005, 26 (6) 1479-1486;
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