title: Volume sweeping and bodyline matching for automated prealignment in volumetric medical image registration.
abstract: For an automated image registration to converge to a good registration, it is crucial that the initial registration is within the capture range of the true registration, as local optimization methods are frequently employed. The ways to set an initial registration in current practice are not ideal and it is highly desirable to automate this initial registration (prealignment). Two automatic prealignment methods are reported here. In the volume sweeping approach, one volume is swept through the other, the overlapping volumes are coarsely aligned in the x and y directions, and a similarity measure is calculated at each sweeping position. Once sweeping is done, the position that gives the best similarity measure is chosen as the prealignment. In the second bodyline matching approach, patient bodyline profiles (the furthest anterior or posterior body boundary points) are extracted from two volumes and objectively matched. A prealignment is then derived from the matched bodylines. Both methods are tested on 19 PET/CT alignments of five patients with known ground truths acquired on hybrid PET/CT scanners. The absolute differences in the three translational parameters between the volume sweeping prealignment and the ground truth are 6.1 ± 3.9, 2.2 ± 2.7, and 4.2 ± 6.1mm, and between the bodyline matching and the ground truth are 5.2 ± 3.0, 3.3 ± 3.0, and 3.3 ± 4.1mm, which are within the capture range of automatic registration algorithms. The volume sweeping and bodyline matching can thus be used as a preprocessing step for automatic registration, making it possible to run an automatic registration algorithm without user intervention.
authors.affiliation: Nuclear Medicine Applications, Imaging Clinical Applications and Platforms Philips Healthcare, 595 Miner Road, Cleveland, OH 44143, USA. yzhu@computer.org
authors.forename: Yang-Ming
authors.initials: YM
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging / Q000379:methods; D049268:Positron-Emission Tomography; D014057:Tomography, X-Ray Computed; D060726:Torso / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D051598:Whole Body Imaging / Q000379:methods*