Literature DB >> 27494827

The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration.

Daniel L Saenz1, Hojin Kim, Josephine Chen, Sotirios Stathakis, Neil Kirby.   

Abstract

The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu's method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

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Year:  2016        PMID: 27494827     DOI: 10.1088/0031-9155/61/17/6269

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  3 in total

1.  Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools.

Authors:  Mohammad Hussein; Adeyemi Akintonde; Jamie McClelland; Richard Speight; Catharine H Clark
Journal:  Br J Radiol       Date:  2021-04-29       Impact factor: 3.039

2.  Implementing user-defined atlas-based auto-segmentation for a large multi-centre organisation: the Australian Experience.

Authors:  Yunfei Hu; Mikel Byrne; Ben Archibald-Heeren; Kenton Thompson; Andrew Fong; Marcel Knesl; Amy Teh; Eve Tiong; Richard Foster; Paul Melnyk; Michelle Burr; Amelia Thompson; Jiy Lim; Luke Moore; Fiona Gordon; Rylie Humble; Anna Hardy; Saul Williams
Journal:  J Med Radiat Sci       Date:  2019-10-28

3.  Quantifying the accuracy of deformable image registration for cone-beam computed tomography with a physical phantom.

Authors:  Richard Y Wu; Amy Y Liu; Tyler D Williamson; Jinzhong Yang; Paul G Wisdom; Xiaorong R Zhu; Steven J Frank; Clifton D Fuller; Gary B Gunn; Song Gao
Journal:  J Appl Clin Med Phys       Date:  2019-09-21       Impact factor: 2.102

  3 in total

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