{"title":"用保边加权正则化校正99mTc-TRODAT-1脑SPECT图像的部分体积效应","authors":"T. Yin, N. Chiu","doi":"10.1109/ISBI.2017.7950702","DOIUrl":null,"url":null,"abstract":"The partial volume effect (PVE) due to the low resolution of SPECT in brain SPECT volumes can be modeled as a convolution of a three-dimensional point-spread function (PSF) with the underlying true radioactivity. In this paper, a deconvolution guided by the edge locations in the geometric transfer matrix (GTM) method as a weighted regularization, denoted as RGTM, was proposed to take into account both the discrepancy from the convolution and the regional-homogeneity prior information in the correction of the PVE (PVC). Two steps were conducted: GTM and then a weighted regularization. Twenty digital phantom simulations were made to compare the performance of RGTM with those of Van-Cittert deconvolution (VC), GTM, and the region-based voxel-wise correction (RBV). Clinical data from eighty-four healthy adults with 99mTc-TRODAT-1 SPECT and MRI scans were also tested. Because the proposed RGTM was good in both constant and non-constant ROIs, its robustness is better than other methods if the distribution of the underlying radioactivity is not known exactly.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"11 1","pages":"1074-1077"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Correction of partial volume effect in 99mTc-TRODAT-1 brain SPECT images using an edge-preserving weighted regularization\",\"authors\":\"T. Yin, N. Chiu\",\"doi\":\"10.1109/ISBI.2017.7950702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The partial volume effect (PVE) due to the low resolution of SPECT in brain SPECT volumes can be modeled as a convolution of a three-dimensional point-spread function (PSF) with the underlying true radioactivity. In this paper, a deconvolution guided by the edge locations in the geometric transfer matrix (GTM) method as a weighted regularization, denoted as RGTM, was proposed to take into account both the discrepancy from the convolution and the regional-homogeneity prior information in the correction of the PVE (PVC). Two steps were conducted: GTM and then a weighted regularization. Twenty digital phantom simulations were made to compare the performance of RGTM with those of Van-Cittert deconvolution (VC), GTM, and the region-based voxel-wise correction (RBV). Clinical data from eighty-four healthy adults with 99mTc-TRODAT-1 SPECT and MRI scans were also tested. Because the proposed RGTM was good in both constant and non-constant ROIs, its robustness is better than other methods if the distribution of the underlying radioactivity is not known exactly.\",\"PeriodicalId\":6547,\"journal\":{\"name\":\"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)\",\"volume\":\"11 1\",\"pages\":\"1074-1077\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBI.2017.7950702\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2017.7950702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Correction of partial volume effect in 99mTc-TRODAT-1 brain SPECT images using an edge-preserving weighted regularization
The partial volume effect (PVE) due to the low resolution of SPECT in brain SPECT volumes can be modeled as a convolution of a three-dimensional point-spread function (PSF) with the underlying true radioactivity. In this paper, a deconvolution guided by the edge locations in the geometric transfer matrix (GTM) method as a weighted regularization, denoted as RGTM, was proposed to take into account both the discrepancy from the convolution and the regional-homogeneity prior information in the correction of the PVE (PVC). Two steps were conducted: GTM and then a weighted regularization. Twenty digital phantom simulations were made to compare the performance of RGTM with those of Van-Cittert deconvolution (VC), GTM, and the region-based voxel-wise correction (RBV). Clinical data from eighty-four healthy adults with 99mTc-TRODAT-1 SPECT and MRI scans were also tested. Because the proposed RGTM was good in both constant and non-constant ROIs, its robustness is better than other methods if the distribution of the underlying radioactivity is not known exactly.