Automated geometric optimization for robotic HIFU treatment of liver tumors

Tom Williamson, Scott Everitt, Sunita Chauhan

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

Background: High intensity focused ultrasound (HIFU) represents a non-invasive method for the destruction of cancerous tissue within the body. Heating of targeted tissue by focused ultrasound transducers results in the creation of ellipsoidal lesions at the target site, the locations of which can have a significant impact on treatment outcomes. Towards this end, this work describes a method for the optimization of lesion positions within arbitrary tumors, with specific anatomical constraints. Materials & methods: A force-based optimization framework was extended to the case of arbitrary tumor position and constrained orientation. Analysis of the approximate reachable treatment volume for the specific case of treatment of liver tumors was performed based on four transducer configurations and constraint conditions derived. Evaluation was completed utilizing simplified spherical and ellipsoidal tumor models and randomly generated tumor volumes. The total volume treated, lesion overlap and healthy tissue ablated was evaluated. Two evaluation scenarios were defined and optimized treatment plans assessed. Results: The optimization framework resulted in improvements of up to 10% in tumor volume treated, and reductions of up to 20% in healthy tissue ablated as compared to the standard lesion rastering approach. Generation of optimized plans proved feasible for both sub- and intercostally located tumors. Conclusions: This work describes an optimized method for the planning of lesion positions during HIFU treatment of liver tumors. The approach allows the determination of optimal lesion locations and orientations, and can be applied to arbitrary tumor shapes and sizes.

Original languageEnglish
Pages (from-to)1-7
Number of pages8
JournalComputers in Biology and Medicine
Volume96
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • Bubble packing
  • Focused ultrasound
  • HIFU
  • Liver cancer
  • Optimization
  • Surgical planning

Cite this

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title = "Automated geometric optimization for robotic HIFU treatment of liver tumors",
abstract = "Background: High intensity focused ultrasound (HIFU) represents a non-invasive method for the destruction of cancerous tissue within the body. Heating of targeted tissue by focused ultrasound transducers results in the creation of ellipsoidal lesions at the target site, the locations of which can have a significant impact on treatment outcomes. Towards this end, this work describes a method for the optimization of lesion positions within arbitrary tumors, with specific anatomical constraints. Materials & methods: A force-based optimization framework was extended to the case of arbitrary tumor position and constrained orientation. Analysis of the approximate reachable treatment volume for the specific case of treatment of liver tumors was performed based on four transducer configurations and constraint conditions derived. Evaluation was completed utilizing simplified spherical and ellipsoidal tumor models and randomly generated tumor volumes. The total volume treated, lesion overlap and healthy tissue ablated was evaluated. Two evaluation scenarios were defined and optimized treatment plans assessed. Results: The optimization framework resulted in improvements of up to 10{\%} in tumor volume treated, and reductions of up to 20{\%} in healthy tissue ablated as compared to the standard lesion rastering approach. Generation of optimized plans proved feasible for both sub- and intercostally located tumors. Conclusions: This work describes an optimized method for the planning of lesion positions during HIFU treatment of liver tumors. The approach allows the determination of optimal lesion locations and orientations, and can be applied to arbitrary tumor shapes and sizes.",
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Automated geometric optimization for robotic HIFU treatment of liver tumors. / Williamson, Tom; Everitt, Scott; Chauhan, Sunita.

In: Computers in Biology and Medicine, Vol. 96, 01.05.2018, p. 1-7.

Research output: Contribution to journalArticleResearchpeer-review

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