title: Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments.
abstract: Registration is critical for image-based treatment planning and image-guided treatment delivery. Although automatic registration is available, manual, visual-based image fusion using three orthogonal planar views (3P) is always employed clinically to verify and adjust an automatic registration result. However, the 3P fusion can be time consuming, observer dependent, as well as prone to errors, owing to the incomplete 3-dimensional (3D) volumetric image representations. It is also limited to single-pixel precision (the screen resolution). The 3D volumetric image registration (3DVIR) technique was developed to overcome these shortcomings. This technique introduces a 4th dimension in the registration criteria beyond the image volume, offering both visual and quantitative correlation of corresponding anatomic landmarks within the two registration images, facilitating a volumetric image alignment, and minimizing potential registration errors. The 3DVIR combines image classification in real-time to select and visualize a reliable anatomic landmark, rather than using all voxels for alignment. To determine the detection limit of the visual and quantitative 3DVIR criteria, slightly misaligned images were simulated and presented to eight clinical personnel for interpretation. Both of the criteria produce a detection limit of 0.1 mm and 0.1 degree. To determine the accuracy of the 3DVIR method, three imaging modalities (CT, MR and PET/CT) were used to acquire multiple phantom images with known spatial shifts. Lateral shifts were applied to these phantoms with displacement intervals of 5.0+/-0.1 mm. The accuracy of the 3DVIR technique was determined by comparing the image shifts determined through registration to the physical shifts made experimentally. The registration accuracy, together with precision, was found to be: 0.02+/-0.09 mm for CT/CT images, 0.03+/-0.07 mm for MR/MR images, and 0.03+/-0.35 mm for PET/CT images. This accuracy is consistent with the detection limit, suggesting an absence of detectable systematic error. This 3DVIR technique provides a superior alternative to the 3P fusion method for clinical applications.
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Guang
authors.initials: G
authors.lastname: Li
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Huchen
authors.initials: H
authors.lastname: Xie
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Holly
authors.initials: H
authors.lastname: Ning
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Deborah
authors.initials: D
authors.lastname: Citrin
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Jacek
authors.initials: J
authors.lastname: Capala
authors.affiliation: Department of Nuclear Medicine, Clinical Center, National Institutes of Health, Bethesda, MD, U.S.A.
authors.forename: Roberto
authors.initials: R
authors.lastname: Maass-Moreno
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Peter
authors.initials: P
authors.lastname: Guion
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Barbara
authors.initials: B
authors.lastname: Arora
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Norman
authors.initials: N
authors.lastname: Coleman
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Kevin
authors.initials: K
authors.lastname: Camphausen
authors.affiliation: Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, U.S.A.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Miller
mesh_terms: D000465:Algorithms; D001331:Automation; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*