Your browser doesn't support javascript.
loading
Comparing regularized Kelvinlet functions and the finite element method for registration of medical images to sparse organ data.
Ringel, Morgan J; Heiselman, Jon S; Richey, Winona L; Meszoely, Ingrid M; Jarnagin, William R; Miga, Michael I.
Afiliación
  • Ringel MJ; Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA. Electronic address: morgan.j.ringel@Vanderbilt.edu.
  • Heiselman JS; Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA; Memorial Sloan-Kettering Cancer Center, Department of Surgery, New York, NY, USA.
  • Richey WL; Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA.
  • Meszoely IM; Vanderbilt University Medical Center, Division of Surgical Oncology, Nashville, TN, USA.
  • Jarnagin WR; Memorial Sloan-Kettering Cancer Center, Department of Surgery, New York, NY, USA.
  • Miga MI; Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA.
Med Image Anal ; 96: 103221, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38824864
ABSTRACT
Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for deformations, but clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room. While finite element models can be effective in sparse data scenarios, computation time remains a limitation to widespread deployment. This paper proposes a registration algorithm that uses regularized Kelvinlets, which are analytical solutions to linear elasticity in an infinite domain, to overcome these barriers. This algorithm is demonstrated and compared to finite element-based registration on two datasets a phantom liver deformation dataset and an in vivo breast deformation dataset. The regularized Kelvinlets algorithm resulted in a significant reduction in computation time compared to the finite element method. Accuracy as evaluated by target registration error was comparable between methods. Average target registration errors were 4.6 ± 1.0 and 3.2 ± 0.8 mm on the liver dataset and 5.4 ± 1.4 and 6.4 ± 1.5 mm on the breast dataset for the regularized Kelvinlets and finite element method, respectively. Limitations of regularized Kelvinlets include the lack of organ-specific geometry and the assumptions of linear elasticity and infinitesimal strain. Despite limitations, this work demonstrates the generalizability of regularized Kelvinlets registration on two soft-tissue elastic organs. This method may improve and accelerate registration for image-guided surgery, and it shows the potential of using regularized Kelvinlets on medical imaging data.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Fantasmas de Imagen / Análisis de Elementos Finitos / Hígado Límite: Female / Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Fantasmas de Imagen / Análisis de Elementos Finitos / Hígado Límite: Female / Humans Idioma: En Revista: Med Image Anal Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos