Versatile multi-constrained planning for thermal ablation of large liver tumors.
Comput Med Imaging Graph
; 94: 101993, 2021 12.
Article
in En
| MEDLINE
| ID: mdl-34710628
The surgical planning of large hepatic tumor ablation remains a challenging task that relies on fulfilling multiple medical constraints, especially for the ablation based on configurations of multiple electrodes. The placement of the electrodes to completely ablate the tumor as well as their insertion trajectory to their final position have to be planned to cause as little damage to healthy anatomical structures as possible to allow a fast rehabilitation. In this paper, we present a novel, versatile approach for the computer-assisted planning of multi-electrode thermal ablation of large liver tumors based on pre-operative CT data with semantic annotations. This involves both the specification of the number of required electrodes and their distribution to adequately ablate the tumor region without damaging too much healthy tissue. To determine the insertion trajectory of the electrodes to their final position, we additionally incorporate a series of medical constraints into our optimization, which allows a global analysis where obstacles such as bones are taken into account and damage to healthy tissue is mitigated. Compared with the state-of-the-art method, our method achieves compact ablation regions without relying on assumptions on a potential needle path for optimal global search and, hence, is suitable for guiding clinicians through the planning of the tumor ablation. We also demonstrate the feasibility of our approach in various experiments of clinical data and demonstrate that our approach not only allows completely ablating the tumor region but also reducing the damage of healthy tissue in comparison to the previous state-of-the-art method.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Surgery, Computer-Assisted
/
Ablation Techniques
/
Liver Neoplasms
Limits:
Humans
Language:
En
Journal:
Comput Med Imaging Graph
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2021
Document type:
Article
Affiliation country:
Germany
Country of publication:
United States