Predictive Mechanics-based Model for Depth-of-Cut (DOC) of Waterjet in Soft-Tissue for Waterjet-assisted Medical Applications
Mahdieh Babaiasl, Boccelli Stefano, Yao Chen, Fan Yang, Jow-Lian Ding and John P. Swensen
Medical & Biological Engineering & Computing, Vol.58, pp.1845-1872
04/22/2020
Handle:
https://hdl.handle.net/2376/17722
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Predictive Mechanics-based Model for Depth-of-Cut (DOC) of Waterjet in Soft-Tissue for Waterjet-assisted Medical ApplicationsRights Statements InC/1.0 , Open Access
Abstract
The use of waterjet technology is now prevalent in medical applications including surgery, soft tissue resection, bone cutting, waterjet steerable needles, and wound debridement. The depth of the cut (DOC) of a waterjet in soft tissue is an important parameter that should be predicted in these applications. For instance, for waterjet- assisted surgery, selective cutting of tissue layers is a must to avoid damage to deeper tissue layers. For our proposed fracture-directed waterjet steerable needles, predicting the cut-depth of the waterjet in soft tissue is important to develop an accurate motion model, as well as control algorithms for this class of steerable needles. To date, most of the proposed models are only valid in the conditions of the experiments and if the soft tissue or the system properties change, the models will become invalid. The model proposed in this paper is formulated to allow for variation in parameters related to both the waterjet geometry and the tissue. In this paper, first the cut-depths of waterjet in soft tissue simulants are measured experimentally, and the effect of tissue stiffness, waterjet velocity, and nozzle diameter are studied on DOC. Then, a model based on the properties of the tissue and the waterjet is proposed to predict the DOC of waterjet in soft tissue. In order to verify the model, soft tissue properties (constitutive response, and fracture toughness) are measured using low strain rate compression tests, Split- Hopkinson-Pressure-Bar (SHPB) tests, and fracture toughness tests. The results show that the proposed model can predict the DOC of waterjet in soft tissue with acceptable accuracy if the tissue and waterjet properties are known.
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Title
Predictive Mechanics-based Model for Depth-of-Cut (DOC) of Waterjet in Soft-Tissue for Waterjet-assisted Medical Applications
Creators
Mahdieh Babaiasl (Author)
Boccelli Stefano (Author)
Yao Chen (Author)
Fan Yang (Author)
Jow-Lian Ding (Author)
John P. Swensen (Author)
Publication Details
Medical & Biological Engineering & Computing, Vol.58, pp.1845-1872
Academic Unit
Mechanical and Materials Engineering, School of
Identifiers
99900502121401842
Copyright
In copyright ; openAccess ; http://rightsstatements.org/vocab/InC/1.0/ ; http://purl.org/eprint/accessRights/OpenAccess
Language
English
Resource Type
Accepted manuscript
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Predictive Mechanics-based Model for Depth- of-Cut