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Sensitivity study of voxel‐based PET image comparison to image registration algorithms
Author(s) -
Yip Stephen,
Chen Aileen B.,
Aerts Hugo J. W. L.,
Berbeco Ross
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4898125
Subject(s) - voxel , image registration , nuclear medicine , positron emission tomography , sørensen–dice coefficient , medical imaging , artificial intelligence , medicine , computer science , algorithm , mathematics , image (mathematics) , image segmentation
Purpose: Accurate deformable registration is essential for voxel‐based comparison of sequential positron emission tomography (PET) images for proper adaptation of treatment plan and treatment response assessment. The comparison may be sensitive to the method of deformable registration as the optimal algorithm is unknown. This study investigated the impact of registration algorithm choice on therapy response evaluation. Methods: Sixteen patients with 20 lung tumors underwent a pre‐ and post‐treatment computed tomography (CT) and 4D FDG‐PET scans before and after chemoradiotherapy. All CT images were coregistered using a rigid and ten deformable registration algorithms. The resulting transformations were then applied to the respective PET images. Moreover, the tumor region defined by a physician on the registered PET images was classified into progressor, stable‐disease, and responder subvolumes. Particularly, voxels with standardized uptake value (SUV) decreases >30% were classified as responder, while voxels with SUV increases >30% were progressor. All other voxels were considered stable‐disease. The agreement of the subvolumes resulting from difference registration algorithms was assessed by Dice similarity index (DSI). Coefficient of variation (CV) was computed to assess variability of DSI between individual tumors. Root mean square difference (RMS rigid ) of the rigidly registered CT images was used to measure the degree of tumor deformation. RMS rigid and DSI were correlated by Spearman correlation coefficient ( R ) to investigate the effect of tumor deformation on DSI. Results: Median DSI rigid was found to be 72%, 66%, and 80%, for progressor, stable‐disease, and responder, respectively. Median DSI deformable was 63%–84%, 65%–81%, and 82%–89%. Variability of DSI was substantial and similar for both rigid and deformable algorithms with CV > 10% for all subvolumes. Tumor deformation had moderate to significant impact on DSI for progressor subvolume with R rigid = − 0.60 ( p = 0.01) and R deformable = − 0.46 ( p = 0.01–0.20) averaging over all deformable algorithms. For stable‐disease subvolumes, the correlations were significant ( p < 0.001) for all registration algorithms with R rigid = − 0.71 and R deformable = − 0.72. Progressor and stable‐disease subvolumes resulting from rigid registration were in excellent agreement (DSI > 70%) for RMS rigid < 150 HU. However, tumor deformation was observed to have negligible effect on DSI for responder subvolumes with insignificant | R | < 0.26, p > 0.27. Conclusions: This study demonstrated that deformable algorithms cannot be arbitrarily chosen; different deformable algorithms can result in large differences of voxel‐based PET image comparison. For low tumor deformation (RMS rigid < 150 HU), rigid and deformable algorithms yield similar results, suggesting deformable registration is not required for these cases.