Image fusion using CT, MRI and PET for treatment planning, navigation and follow up in percutaneous RFA

Abstract

Aim: To evaluate the feasibility of fusion of morphologic and functional imaging modalities to facilitate treatment planning, probe placement, probe re-positioning, and early detection of residual disease following radiofrequency ablation (RFA) of cancer. Methods: Multi-modality datasets were separately acquired that included functional (FDG-PET and DCE-MRI) and standard morphologic studies (CT and MRI). Different combinations of imaging modalities were registered and fused prior to, during, and following percutaneous image-guided tumor ablation with radiofrequency. Different algorithms and visualization tools were evaluated for both intra-modality and inter-modality image registration using the software MIPAV (Medical Image Processing, Analysis and Visualization). Semi-automated and automated registration algorithms were used on astandard PC workstation: 1) landmark-based least-squares rigid registration, 2) landmark-based thin-plate spline elastic registration, and 3) automatic voxel-similarity, affine registration. Results: Intra- and inter-modality image fusion were successfully performed prior to, during and after RFA procedures. Fusion of morphologic and functional images provided a useful view of the spatial relationship of lesion structure and functional significance. Fused axial images and segmented three-dimensional surface models were used for treatment planning and post-RFA evaluation, to assess potential for optimizing needle placement during procedures. Conclusion: Fusion of morphologic and functional images is feasible before, during and after radiofrequency ablation of tumors in abdominal organs. For routine use, the semi-automated registration algorithms may be most practical. Image fusion may facilitate interventional procedures like RFA and should be further evaluated.