Dennis A. Christie , Rene Fluit , Guillaume Durandau , Massimo Sartori , Nico Verdonschot
{"title":"Comparative evaluation of sparse and minimal data point cloud registration: A study on Tibiofemoral Bones","authors":"Dennis A. Christie , Rene Fluit , Guillaume Durandau , Massimo Sartori , Nico Verdonschot","doi":"10.1016/j.jocs.2024.102463","DOIUrl":null,"url":null,"abstract":"<div><div>An accurate bone registration is a crucial step in Computer-assisted Orthopaedic Surgery (CAOS) to estimate the relationship between a preoperative patient’s bone model and the actual position during surgery. A-mode ultrasound and motion capture system is a new promising non-invasive technique to determine the bone’s 3D pose. The main challenge with such a system is the sparsity of the measurement; it could trap the optimization, which minimizes the registration error, in the local minima. In this paper, we aim to find the registration algorithm that could provide enough surgical navigation accuracy. Several registration algorithms were compared using Monte Carlo simulations. The number of points and placement sensitivity were also investigated while keeping the practical aspect of the system. With 15 points, Unscented Kalman Filter (UKF)-based registration with 6D similarity vector showed superior to the other examined algorithms in minimizing the transformation error. In terms of balancing the accuracy and the equipment availability, the simulation showed that points needed to be dispersedly placed; 15 points were sufficient to register the femur, but 20 points were required to register the tibia. Beyond this number, the registration error hardly improved and will therefore be used to base our number of sensors on.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"84 ","pages":"Article 102463"},"PeriodicalIF":3.1000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877750324002564","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
An accurate bone registration is a crucial step in Computer-assisted Orthopaedic Surgery (CAOS) to estimate the relationship between a preoperative patient’s bone model and the actual position during surgery. A-mode ultrasound and motion capture system is a new promising non-invasive technique to determine the bone’s 3D pose. The main challenge with such a system is the sparsity of the measurement; it could trap the optimization, which minimizes the registration error, in the local minima. In this paper, we aim to find the registration algorithm that could provide enough surgical navigation accuracy. Several registration algorithms were compared using Monte Carlo simulations. The number of points and placement sensitivity were also investigated while keeping the practical aspect of the system. With 15 points, Unscented Kalman Filter (UKF)-based registration with 6D similarity vector showed superior to the other examined algorithms in minimizing the transformation error. In terms of balancing the accuracy and the equipment availability, the simulation showed that points needed to be dispersedly placed; 15 points were sufficient to register the femur, but 20 points were required to register the tibia. Beyond this number, the registration error hardly improved and will therefore be used to base our number of sensors on.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).