Automatic model-based 3D lesion segmentation for evaluation of MR-guided thermal ablation therapy

We are investigating magnetic resonance imaging-guided radiofrequency ablation of pathologic tissue. For many tissues, resulting lesions have a characteristic two-boundary appearance featuring an inner region and an outer hyper-intense margin in both contrast enhanced (CE) T₁ and T ₂ weighted MR images. We created a twelve-parameter, three-dimensional, globally deformable model with two quadric surfaces that describes both lesion zones. We present an energy minimization approach to automatically fit the model to a grayscale MR image volume. We applied the automatic method to in vivo lesions (n = 5) in a rabbit thigh model, using CE T₁ and T₂ weighted MR images, and compared the results to multi-operator manually segmented boundaries. For all lesions, the median error was < 1.0 mm for both the inner and outer regions, values that favorably compare to a voxel width of 0.7 mm. These results suggest that our method provides a precise, automatic approximation of lesion shape. We believe that the method has applications in lesion visualization, volume estimation, image quantification, and volume registration.