Journal of Medical Physics
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Year : 2020  |  Volume : 45  |  Issue : 3  |  Page : 156-167

Objective assessment of the quality and accuracy of deformable image registration

1 Department of Radiation Oncology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
2 Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina, USA

Correspondence Address:
Dr. Panayiotis Mavroidis
Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmp.JMP_47_19

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Background: The increased use of deformable registration algorithms in clinical practice has also increased the need for their validation. Aims and Objectives: The purpose of the study was to investigate the quality, accuracy, and plausibility of three commercial image registration algorithms for 4-dimensional computed tomography (4DCT) datasets using various similarity measures. Materials and Methods: 4DCT datasets were acquired for 10 lung cancer patients. 23 similarity measures were used to evaluate image registration quality. To ensure selected method's invertibility and assess resultant mechanical stress, the determinant of the Jacobian for the displacement field and 3-D Eulerian strain tensor were calculated. All the measures and calculations were applied on to extended deformable multi pass (EXDMP) and deformable multi pass (DMP) methods. Results: The results indicate the same trend for several of the studied measures. The Jacobian determinant values were always positive for the DMP method. The Eulerian strain tensor had smaller values for the DMP method than EXDMP in all of the studied cases. The negative values of the Jacobian determinant point to non-physical behavior of the EXDMP method. The Eulerian strain tensor values indicate less tissue strain for the DMP method. Large differences were also observed in the results between complete and cropped datasets (coefficient of determination: 0.55 vs. 0.93). Conclusion: A number of error and distance measures showed the best performance among the tested measures. The evaluated measures might detect CT dataset differences with higher precision if the analysis is restricted to a smaller volume.

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