Figure 1. Schematic of the image registration and data fusion processes.(a) Anatomical information from a spin-echo MR is first registered and then fused with functional information from a 11C thymidine PET to create a synthetic MR-PET image volume. (b) General components of the registration process.
Figure 3. B-spline deformation model. (a) 1D example of the cubic B-spline deformation model. The displacement x as a function of x is determined by the weighted sum of basis functions. The double arrow shows the region of the overall deformation affected by the weight factor w7. 3D deformations are constructed using 1D deformations for each dimension. (b) Multiresolution registration of lung data using B-splines. Both knot density and image resolution are varied during registration. This can help avoid local minima and decrease overall registration time.
Figure 4. Visualization of(a) deformation computed between datasets registered using B-splines and (b) fluid flow model. The deformation or displacement is known for every voxel but only displayed for a subset of voxels for clarity ((b) image courtesy of Gustavo Olivera, University of Wisconson).
Figure 5. Structure mapping. A tumour volume is outlined by the clinician on an MR study and then mapped to the treatment planning CT using the computed transformation.
Figure 6. Different approaches to display data from multiple studies which have been registered and reformatted.(a) Side-by-side display with linked cursor. (b) Split screen display. (c) Colourwash overlay.
Figure 10. Volumetric registration at the treatment unit. A cone-beam CT acquired at the time of treatment is registered to the treatment planning CT (larger dataset) to properly position the patient on the treatment table (courtesy of Peter Monroe, PhD, Varian Medical Systems).
x as a function of x is determined by the weighted sum of basis functions. The double arrow shows the region of the overall deformation affected by the weight factor w7. 3D deformations are constructed using 1D deformations for each dimension. (b) Multiresolution registration of lung data using B-splines. Both knot density and image resolution are varied during registration. This can help avoid local minima and decrease overall registration time.
