Vol. 31, No. 2, 2009 (June)
	Content

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

F.L. Giesel^{1, 2, 3}, A. Mehndiratta^{2, 4}, J. Locklin¹, M.J. McAuliffe⁵, S. White⁶, P.L. Choyke⁷, M.V. Knopp⁸, B.J. Wood^{1, 9}, U. Haberkorn³, H. von Tengg-Kobligk^{1, 2, 8}

¹Department of Radiology, Clinical Center, National Institutes of Health, Bethesda 20892, MD, USA ²German Cancer Research Center (dkfz), Department of Radiology, Heidelberg 69120, Germany ³Department of Nuclear Medicine, University of Heidelberg, Heidelberg 69117, Germany ⁴School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, India ⁵National Institutes of Health, Center for Information Technology, Bethesda 20892, MD, USA ⁶New Jersey School of Medicine, Newark, 07103 NJ, USA ⁷National Institutes of Health, Molecular Imaging Program, National Cancer Institute, Bethesda 20892, MD, USA ⁸The Ohio State University, Department of Radiology, Columbus 43210, OH, USA ⁹National Institutes of Health, Surgery Branch, National Cancer Institute, Bethesda 20892, MD, USA

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 a standard 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.

Key Words: image fusion, radiofrequency ablation, registration, registration algorithms, treatment planning

Language:

[full text]