{"title":"Br-SDM:一种基于SDM和FEM相结合的正颌手术计划中骨相关软组织快速准确预测方法","authors":"Q. He, Jun Feng, H. Ip, J. Xia, Xianbin Cao","doi":"10.1504/IJFIPM.2009.027593","DOIUrl":null,"url":null,"abstract":"We propose a novel Statistical Deformable Model (SDM) for bone-related soft tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Method (FEM) and SDM to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based sample generation and SDM-Based soft tissue prediction, we are able to capture the prior knowledge of bone-related soft tissue deformation. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimisation. Our experiments have shown that Br-SDM is able to give comparable soft tissue prediction accuracy with respect to conventional FEM-based prediction while reducing the computation cost from O(n²) to O(n) at the same time.","PeriodicalId":216126,"journal":{"name":"Int. J. Funct. Informatics Pers. Medicine","volume":"55 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Br-SDM: a fast and accurate method for bone-related soft tissue prediction in orthognathic surgery planning based on the integration of SDM and FEM\",\"authors\":\"Q. He, Jun Feng, H. Ip, J. Xia, Xianbin Cao\",\"doi\":\"10.1504/IJFIPM.2009.027593\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a novel Statistical Deformable Model (SDM) for bone-related soft tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Method (FEM) and SDM to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based sample generation and SDM-Based soft tissue prediction, we are able to capture the prior knowledge of bone-related soft tissue deformation. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimisation. Our experiments have shown that Br-SDM is able to give comparable soft tissue prediction accuracy with respect to conventional FEM-based prediction while reducing the computation cost from O(n²) to O(n) at the same time.\",\"PeriodicalId\":216126,\"journal\":{\"name\":\"Int. J. Funct. Informatics Pers. Medicine\",\"volume\":\"55 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Funct. Informatics Pers. Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJFIPM.2009.027593\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Funct. Informatics Pers. Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJFIPM.2009.027593","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Br-SDM: a fast and accurate method for bone-related soft tissue prediction in orthognathic surgery planning based on the integration of SDM and FEM
We propose a novel Statistical Deformable Model (SDM) for bone-related soft tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Method (FEM) and SDM to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based sample generation and SDM-Based soft tissue prediction, we are able to capture the prior knowledge of bone-related soft tissue deformation. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimisation. Our experiments have shown that Br-SDM is able to give comparable soft tissue prediction accuracy with respect to conventional FEM-based prediction while reducing the computation cost from O(n²) to O(n) at the same time.