Performance evaluation of a newly developed three-dimensional model-based global-to-local registration in prostate cancer.

We evaluated the performance of a newly developed three-dimensional (3D) model-based global-to-local registration of multiple organs, by comparing it with a 3D model-based global registration in the prostate region. This study included 220 prostate cancer patients who underwent intensity-modulated radiotherapy or volumetric-modulated arc therapy. Our registration proceeded sequentially, i.e. global registration including affine and piece-wise affine transformation followed by local registration. As a local registration, Laplacian-based and finite element method-based registration was implemented in Algorithm A and B, respectively. Algorithm C was for global registration alone. The template models for the prostate, seminal vesicles, rectum and bladder were constructed from the first 20 patients, and then three different registrations were performed on these organs for the remaining 200 patients, to assess registration accuracy. The 75th percentile Hausdorff distance was <1 mm in Algorithm A; it was >1 mm in Algorithm B, except for the prostate; and 3.9 mm for the prostate and >7.8 mm for other organs in Algorithm C. The median computation time to complete registration was <101, 30 and 16 s in Algorithms A, B and C, respectively. Analysis of variance revealed significant differences among Algorithms A-C in the Hausdorff distance and computation time. In addition, no significant difference was observed in the difference of Hausdorff distance between Algorithm A and B with Tukey's multiple comparison test. The 3D model-based global-to-local registration, especially that implementing Laplacian-based registration, completed surface registration rapidly and provided sufficient registration accuracy in the prostate region.

Intraoperative localized constrained registration in navigated bronchoscopy.

PURPOSE: One of the major challenges in electromagnetic navigated bronchoscopy is the navigation accuracy. An initial rigid image-to-patient registration may not be optimal for the entire lung volume, as the lung tissue anatomy is likely to have shifted since the time of computer tomography (CT) acquisition. The accuracy of the initial rigid registration will also be affected throughout the procedure by breathing, coughing, patient movement and tissue displacements due to pressure from bronchoscopy tools. A method to minimize the negative impact from these factors by updating the registration locally during the procedure is needed and suggested in this paper. METHODS: The intraoperative local registration method updates the initial registration by optimization in an area of special interest, for example, close to a biopsy position. The local registration was developed through an adaptation of a previously published registration method used for the initial registration of CT to the patient anatomy. The method was tested in an experimental breathing phantom setup, where respiratory movements were induced by a robotic arm. Deformations were also applied to the phantom to see if the local registration could compensate for these. RESULTS: The local registration was successfully applied in all 15 repetitions, five in each of the three parts of the airway phantom. The mean registration accuracy was improved from 11.8-19.4 mm to 4.0-6.7 mm, varying to some degree in the different segments of the airway model. CONCLUSIONS: A local registration method, to update and improve the initial image-to patient registration during navigated bronchoscopy, was developed. The method was successfully tested in a breathing phantom setup. Further development is needed to make the method more automatic. It must also be verified in human studies.

Evaluation of Medical Image Registration Techniques Based on Nature and Domain of the Transformation.

A lot of research has been done during the past 20 years in the area of medical image registration for obtaining detailed, important, and complementary information from two or more images and aligning them into a single, more informative image. Nature of the transformation and domain of the transformation are two important medical image registration techniques that deal with characters of objects (motions) in images. This article presents a detailed survey of the registration techniques that belong to both categories with detailed elaboration on their features, issues, and challenges. An investigation estimating similarity and dissimilarity measures and performance evaluation is the main objective of this work. This article also provides reference knowledge in a compact form for researchers and clinicians looking for the proper registration technique for a particular application.