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MO‐FG‐204‐02: Reference Image Selection in the Presence of Multiple Scan Realizations
Author(s) -
Ruan D,
Dou T,
Thomas D,
Low D
Publication year - 2015
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 - 10.1118/1.4925423
Subject(s) - image registration , artificial intelligence , image quality , mathematics , residual , metric (unit) , boosting (machine learning) , computer vision , computer science , pattern recognition (psychology) , algorithm , image (mathematics) , operations management , economics
Purpose: Fusing information from multiple correlated realizations (e.g., 4DCT) can improve image quality. This process often involves ill‐conditioned and asymmetric nonlinear registration and the proper selection of a reference image is important. This work proposes to examine post‐registration variation indirectly for such selection, and develops further insights to reduce the number of cross‐registrations needed. Methods: We consider each individual scan as a noisy point in the vicinity of an image manifold, related by motion. Nonrigid registration “transports” a scan along the manifold to the reference neighborhood, and the residual is a surrogate for local variation. To test this conjecture, 10 thoracic scans from the same session were reconstructed from a recently developed low‐dose helical 4DCT protocol. Pairwise registration was repeated bi‐directionally (81 times) and fusion was performed with each candidate reference. The fused image quality was assessed with SNR and CNR. Registration residuals in SSD, harmonic energy, and deformation Jacobian behavior were examined. The semi‐symmetry is further utilized to reduce the number of registration needed. Results: The comparison of image quality between single image and fused ones identified reduction of local intensity variance as the major contributor of image quality, boosting SNR and CNR by 5 to 7 folds. This observation further suggests the criticality of good agreement across post‐registration images. Triangle inequality on the SSD metric provides a proficient upper‐bound and surrogate on such disagreement. Empirical observation also confirms that fused images with high residual SSD have lower SNR and CNR than the ones with low or intermediate SSDs. Registration SSD is structurally close enough to symmetry for reduced computation. Conclusion: Registration residual is shown to be a good predictor of post‐fusion image quality and can be used to identify good reference centers. Semi‐symmetry of the registration residual further reduces computation cost. Supported by in part by NIH R01 CA096679.