An automatic registration method for pre‐ and post‐interventional CT images for assessing treatment success in liver RFA treatment

• Published in: Medical physics (Lancaster) 2015-09, Vol.42 (9), p.5559-5567
• Main Authors: Luu, Ha Manh, Niessen, Wiro, van Walsum, Theo, Klink, Camiel, Moelker, Adriaan
• Format: Article
• Language: English
• Subjects: Analysis of texture; Automation; Biological material, e.g. blood, urine; Haemocytometers; biomechanics; cancer; Catheter Ablation; Computed tomography; Computerised tomographs; computerised tomography; CT image registration; deformation; Digital computing or data processing equipment or methods, specially adapted for specific applications; Humans; Image data processing or generation, in general; Image Processing, Computer-Assisted - methods; image registration; image segmentation; image texture; Jacobians; liver; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - surgery; local rigid deformation; Mechanical and electrical properties of tissues and organs; Medical image contrast; medical image processing; Medical image segmentation; Monte Carlo methods; non‐rigid registration; radiation therapy; Registration; RFA liver treatment; Segmentation; Therapeutic applications; Tissue ablation; Tomography, X-Ray Computed; Treatment Outcome; tumours
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4927790

Purpose: In image‐guided radio frequency ablation for liver cancer treatment, pre‐ and post‐interventional CT images are typically used to verify the treatment success of the therapy. In current clinical practice, the tumor zone in the diagnostic, preinterventional images is mentally or manually mapped to the ablation zone in the post‐interventional images to decide success of the treatment. However, liver deformation and differences in image quality as well as in texture of the ablation zone and the tumor area make the mental or manual registration a challenging task. Purpose of this paper is to develop an automatic framework to register the pre‐interventional image to the post‐interventional image. Methods: The authors propose a registration approach enabling a nonrigid deformation of the tumor to the ablation zone, while keeping locally rigid deformation of the tumor area. The method was evaluated on CT images of 38 patient datasets from Erasmus MC. The evaluation is based on Dice coefficients of the liver segmentation on both the pre‐interventional and post‐interventional images, and mean distances between the liver segmentations. Additionally, residual distances after registration between corresponding landmarks and local mean surface distance in the images were computed. Results: The results show that rigid registration gives a Dice coefficient of 87.9%, a mean distance of the liver surfaces of 5.53 mm, and a landmark error of 5.38 mm, while non‐rigid registration with local rigid deformation has a Dice coefficient of 92.2%, a mean distance between the liver segmentation boundaries near the tumor area of 3.83 mm, and a landmark error of 2.91 mm, where a part of this error can be attributed to the slice spacing in the authors’ CT images. Conclusions: This method is thus a promising tool to assess the success of RFA liver cancer treatment.