First validation of a model-based hepatic percutaneous microwave ablation planning on a clinical dataset

Scientific Reports, vol. 13, pp. 16862

Abstract

A model-based planning tool, integrated in an imaging system, is envisioned for CT-guided percutaneous microwave ablation. This study aims to evaluate the biophysical model performance, by comparing its prediction retrospectively with the actual ablation ground truth from a clinical dataset in liver. The biophysical model uses a simplified formulation of heat deposition on the applicator and a heat sink related to vasculature to solve the bioheat equation. A performance metric is defined to assess how the planned ablation overlaps the actual ground truth. Results demonstrate superiority of this model prediction compared to manufacturer tabulated data and a significant influence of the vasculature cooling effect. Nevertheless, vasculature shortage due to branches occlusion and applicator misalignment due to registration error between scans affects the thermal prediction. With a more accurate vasculature segmentation, occlusion risk can be estimated, whereas branches can be used as liver landmarks to improve the registration accuracy. Overall, this study emphasizes the benefit of a model-based thermal ablation solution in better planning the ablation procedures. Contrast and registration protocols must be adapted to facilitate its integration into the clinical workflow.

Authors 7

1. Bruno Frackowiak corresponding

   Philips (Netherlands)

   Affiliation as printed

   Philips Research, Data Science & Digital Twin, 5656 AE, Eindhoven, The Netherlands. Bruno.Frackowiak@philips.com

   Philips Research, Data Science & Digital Twin, Eindhoven, The Netherlands

2. Vincent Van den Bosch Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

3. Zoi Tokoutsi
   Affiliation as printed

   Philips Research, Data Science & Digital Twin, 5656 AE, Eindhoven, The Netherlands

   Philips Research, Data Science & Digital Twin, Eindhoven, The Netherlands

4. Marco Baragona
   Affiliation as printed

   Philips Research, Data Science & Digital Twin, 5656 AE, Eindhoven, The Netherlands

   Philips Research, Data Science & Digital Twin, Eindhoven, The Netherlands

5. Martijn de Greef
   Affiliation as printed

   Philips Research, Data Science & Digital Twin, 5656 AE, Eindhoven, The Netherlands

   Philips Research, Data Science & Digital Twin, Eindhoven, The Netherlands

6. Aaldert Elevelt
   Affiliation as printed

   Philips Research, Data Science & Digital Twin, 5656 AE, Eindhoven, The Netherlands

   Philips Research, Data Science & Digital Twin, Eindhoven, The Netherlands

7. Peter Isfort Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, 52074, Aachen, Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany