SU-E-J-98: Assessment of Deformable Image Registration Algorithms by Using Different Similarity Measures for the Registration Evaluation

Authors

  • Jurkovic I,

    1. Department of Radiation Oncology, University of Texas HSC SA, San Antonio, TX
    2. Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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  • Papanikolaou N,

    1. Department of Radiation Oncology, University of Texas HSC SA, San Antonio, TX
    2. Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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  • Stathakis S,

    1. Department of Radiation Oncology, University of Texas HSC SA, San Antonio, TX
    2. Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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  • Mavroidis P

    1. Department of Radiation Oncology, University of Texas HSC SA, San Antonio, TX
    2. Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
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Abstract

Purpose:

To evaluate deformable image registration algorithms used for 4DCT data sets registration. Furthermore, to validate several similarity measures applied on clinical 4DCT images as part of the evaluation.

Methods:

The capabilities of the registration algorithms — Deformable Multi Pass (DMP), Extended Deformable Multi Pass (EXDMP), structure guided deformable (SGD), and rigid — applied in the commercial software Velocity (Medical Solutions, Atlanta, GA) were evaluated by several different similarity measures applied on 4DCT images. Measures included the cross correlation (CC), root mean squared error (RMSE), relative dimensionless global error in synthesis (Erreur Relative Globale Adimensionelle de Synthese — ERGAS), image quality index (Q), structural similarity index (SSIM), feature similarity index (FSIM), gradient magnitude similarity deviation (GMSD), mutual information (MI), dice similarity coefficient (DSC), Tanimoto coefficient (TC), and specificity (S).

Results:

The analysis showed that for two of the registration methods (DMP and SGD), in 3 out of the 4 cases, the same registration results (the two deformed data sets were exactly the same) were obrained regardless of the phase that was used for registration. For the four cases done so far, only two similarity measures (RMSE, ERGAS) ranked rigid registration in the last place compared to the other methods. Another measure that indicated an inferior performance for the rigid registration was the MI, which ranked it in the 3rd place in front of EXDMP in only one of the cases studied. The EXDMP was ranked as the best performing algorithm by the different measures combined in 43%, DMP in 22%, SGD in 20% and RIGID in 15% of the cases considered.

Conclusion:

Root mean squared error, relative dimensionless global error in synthesis and mutual information appear to have an advantage over other similarity measures that are considered in the evaluation of the accuracy of deformable image registration.

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