{"title":"Adjoint field methods for non-linear tomographic medical imaging problems","authors":"E. L. Miller, Kate Boverman","doi":"10.1109/ICIP.2002.1040030","DOIUrl":null,"url":null,"abstract":"We show results for full three-dimensional non-linear inversion of the parameters of a diffusive partial differential equation, specifically for an optical tomography application. We compute functional derivatives of the parameters with respect to the mean-squared error using the adjoint field method, and implement two forms of regularization. In the first, a penalty term is introduced into the error functional, and in the second, the solution to the inverse problem is assumed to belong to a parametrized class of functions. In the case where this assumption is correct, our results demonstrate that the parameters can recovered with high accuracy, yielding a better inversion result than the traditional Tikhonov-type approach.","PeriodicalId":74572,"journal":{"name":"Proceedings. International Conference on Image Processing","volume":"30 1","pages":"II-II"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2002.1040030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We show results for full three-dimensional non-linear inversion of the parameters of a diffusive partial differential equation, specifically for an optical tomography application. We compute functional derivatives of the parameters with respect to the mean-squared error using the adjoint field method, and implement two forms of regularization. In the first, a penalty term is introduced into the error functional, and in the second, the solution to the inverse problem is assumed to belong to a parametrized class of functions. In the case where this assumption is correct, our results demonstrate that the parameters can recovered with high accuracy, yielding a better inversion result than the traditional Tikhonov-type approach.