{"title":"锥束CT功能成像体素时间密度模型研究","authors":"Ying Qian, Can Xia","doi":"10.1145/3415048.3416110","DOIUrl":null,"url":null,"abstract":"In the current study we study the variation law of the voxel time-density (TDC) curve in the arteries, tissues and tumors regions, and apply this rule to functional CBCT imaging, which solve the problem that functional CBCT imaging could not directly obtain the TDC curve. Methods: In the arteries, tumors and tissue regions on the DCE-CT sequence image, a 3 ×3 pixels is selected as the region of interest (ROI) respectively, and acquired CBCT projection data. The TDC model was established according to the shape of arterial, tissue and tumor curve respectly. The TDC model is substituted into the CBCT projection data, the approximate TDC (SimuTDC) and attribute (the voxel is located in the artery, tumor or tissue area) of each voxel is obtained by inverse solution. European distance and recall rate were used to evaluate the accuracy of SimuTDC measurements and attribute with the TDC model. Results: European distance (arteries, 0.0644; tumors, 0.0557; tissues, 0.1673) analyses revealed highly significant correlations between SimuTDC values calculated with our method and TrueTDC. Recall rate (arteries, 1; tumors, 1; tissues, 1)analyses revealed that using our method can well predict whether the voxel is located in the artery, tumor or tissue area. Conclusion: The SimuTDC and attribute of each voxel can be obtained using our method. Due to computational speed and hardware equipment, the data used in the experiment is limited, which reduces the reliability and reproducibility of this approach.","PeriodicalId":122511,"journal":{"name":"Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Voxel Time-Density Model in Cone-Beam CT Functional Imaging\",\"authors\":\"Ying Qian, Can Xia\",\"doi\":\"10.1145/3415048.3416110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the current study we study the variation law of the voxel time-density (TDC) curve in the arteries, tissues and tumors regions, and apply this rule to functional CBCT imaging, which solve the problem that functional CBCT imaging could not directly obtain the TDC curve. Methods: In the arteries, tumors and tissue regions on the DCE-CT sequence image, a 3 ×3 pixels is selected as the region of interest (ROI) respectively, and acquired CBCT projection data. The TDC model was established according to the shape of arterial, tissue and tumor curve respectly. The TDC model is substituted into the CBCT projection data, the approximate TDC (SimuTDC) and attribute (the voxel is located in the artery, tumor or tissue area) of each voxel is obtained by inverse solution. European distance and recall rate were used to evaluate the accuracy of SimuTDC measurements and attribute with the TDC model. Results: European distance (arteries, 0.0644; tumors, 0.0557; tissues, 0.1673) analyses revealed highly significant correlations between SimuTDC values calculated with our method and TrueTDC. Recall rate (arteries, 1; tumors, 1; tissues, 1)analyses revealed that using our method can well predict whether the voxel is located in the artery, tumor or tissue area. Conclusion: The SimuTDC and attribute of each voxel can be obtained using our method. Due to computational speed and hardware equipment, the data used in the experiment is limited, which reduces the reliability and reproducibility of this approach.\",\"PeriodicalId\":122511,\"journal\":{\"name\":\"Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3415048.3416110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 International Conference on Pattern Recognition and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3415048.3416110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Voxel Time-Density Model in Cone-Beam CT Functional Imaging
In the current study we study the variation law of the voxel time-density (TDC) curve in the arteries, tissues and tumors regions, and apply this rule to functional CBCT imaging, which solve the problem that functional CBCT imaging could not directly obtain the TDC curve. Methods: In the arteries, tumors and tissue regions on the DCE-CT sequence image, a 3 ×3 pixels is selected as the region of interest (ROI) respectively, and acquired CBCT projection data. The TDC model was established according to the shape of arterial, tissue and tumor curve respectly. The TDC model is substituted into the CBCT projection data, the approximate TDC (SimuTDC) and attribute (the voxel is located in the artery, tumor or tissue area) of each voxel is obtained by inverse solution. European distance and recall rate were used to evaluate the accuracy of SimuTDC measurements and attribute with the TDC model. Results: European distance (arteries, 0.0644; tumors, 0.0557; tissues, 0.1673) analyses revealed highly significant correlations between SimuTDC values calculated with our method and TrueTDC. Recall rate (arteries, 1; tumors, 1; tissues, 1)analyses revealed that using our method can well predict whether the voxel is located in the artery, tumor or tissue area. Conclusion: The SimuTDC and attribute of each voxel can be obtained using our method. Due to computational speed and hardware equipment, the data used in the experiment is limited, which reduces the reliability and reproducibility of this approach.