Purpose: To develop a model-based method for automatic evaluation of radio frequency (RF) ablation treatment using magnetic resonance (MR) images. Materials and Methods: RF current lesions were generated in a rabbit thigh model using MR imaging (MRI) guidance. We created a 12-parameter, three-dimensional, globally deformable model with quadric surfaces that delineates lesion boundaries and is automatically fitted to MR grayscale data. We applied this method to in vivo T2- and contrast-enhanced (CE) T1-weighted MR images acquired immediately post-ablation and four days later. We then compared results to manually segmented MR and three-dimensional registered corresponding histological boundaries of cellular damage. Results: Resulting lesions featured a two-boundary appearance with an inner region and an outer hyperintense margin on MR images. For automated vs. manual MR boundaries, the mean errors over all specimens were 0.19 ± 0. 51 mm and 0.27 ± 0.52 mm for the inner surface, and -0.29 ± 0.40 mm and -0.12 ± 0.17 mm for the outer surface, for T2- and CE T1-weighted images, respectively. For automated vs. histological boundaries, mean errors over all specimens were 0.07 ± 0.64 mm and 0.33 ± 0.71 mm for the inner surface, and -0.27 ± 0.69 mm and 0.02 ± 0.43 mm for the outer surface, for T2- and CE T 1-weighted images, respectively. All boundary errors compared favorably to MR voxel dimensions, which were 0.7 mm in-plane and 3.0 mm thick. Conclusion: The method is accurate both in describing MR-apparent boundaries and in predicting histological response and has applications in lesion visualization, volume estimation, and treatment evaluation.
- Automatic segmentation
- Image analysis
- Interventional magnetic resonance imaging
- Medical image processing
- Radio frequency thermal ablation
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging