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Proceedings Paper

Image data-driven thermal dose prediction for microwave ablation therapy
Author(s): Alice K. Ding; Jon S. Heiselman; Michael I. Miga
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Paper Abstract

Because many patients diagnosed with hepatocellular carcinoma are not eligible for liver transplantation or resection, there has been a great deal of interest in developing locoregional therapies such as thermal ablation. One such thermal ablation therapy is microwave ablation. While benefits have been gained in the management of disease, local recurrence in locoregional therapies is still very common and represents a significant problem. One suggested factor is the presence of soft tissue deformation which is thought to compromise image-to-physical targeting of diseased tissue. This work focuses on presenting a hepatic phantom with an embedded mock tumor target and studying the effects of deformation on ablation when using image-to-physical rigid and non-rigid alignment approaches. While being deformable, the hepatic phantom was designed to enable optical visibility of the ablation zone with target lesion visibility in CT images post-treatment using albumin, agar, formaldehyde, and water constituents. Additionally, a physical mock tumor target phantom was embedded in the hepatic liver phantom and contained CT contrast agent for the designation of lesion prior to mock intervention. Using this phantom, CT scans and sparse-surface data were collected to perform rigid and nonrigid registrations. The registrations allowed for the navigation of the ablation probe to the center of the mock lesions using a custom-built guidance system; this was then followed by microwave ablation treatments. Approximately 96.8% of the mock lesion was ablated using nonrigid registration to guide delivery while none of the mock lesion was ablated using the rigid alignment for guidance, i.e. a completely missed target. This preliminary data demonstrates an improvement in the accuracy of target ablation using a guidance system that factors in soft tissue deformation.

Paper Details

Date Published: 16 March 2020
PDF: 9 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151G (16 March 2020); doi: 10.1117/12.2550550
Show Author Affiliations
Alice K. Ding, Vanderbilt Institute for Surgery and Engineering (United States)
SyBBURE Searle Undergraduate Research Program (United States)
Vanderbilt Univ. (United States)
Jon S. Heiselman, Vanderbilt Institute for Surgery and Engineering (United States)
Vanderbilt Univ. (United States)
Michael I. Miga, Vanderbilt Institute for Surgery and Engineering (United States)
Vanderbilt Univ. (United States)
Vanderbilt University Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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