Automatic Model-Based Evaluation of Magnetic Resonance-Guided Radio Frequency Ablation Lesions with Histological Correlation

Roee S. Lazebnik, Michael S. Breen, Jonathan S. Lewin, David L. Wilson

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)245-254
Number of pages10
JournalJournal of Magnetic Resonance Imaging
Volume19
Issue number2
DOIs
StatePublished - Feb 1 2004

Keywords

  • Automatic segmentation
  • Histology
  • Image analysis
  • Interventional magnetic resonance imaging
  • Medical image processing
  • Radio frequency thermal ablation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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