Zixiang Chen;Yuxi Jin;Zhenxing Huang;Na Zhang;Kaiyi Liang;Guotao Quan;Dong Liang;Hairong Zheng;Zhanli Hu
{"title":"构建用于低剂量灌注 CT 成像的动力学诱导体素聚类滤波器 (KVCF)","authors":"Zixiang Chen;Yuxi Jin;Zhenxing Huang;Na Zhang;Kaiyi Liang;Guotao Quan;Dong Liang;Hairong Zheng;Zhanli Hu","doi":"10.1109/TRPMS.2024.3402272","DOIUrl":null,"url":null,"abstract":"Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from the low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both the simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in the clinical low-dose PCT imaging tasks.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"762-773"},"PeriodicalIF":4.6000,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Building a Kinetic Induced Voxel-Clustering Filter (KVCF) for Low-Dose Perfusion CT Imaging\",\"authors\":\"Zixiang Chen;Yuxi Jin;Zhenxing Huang;Na Zhang;Kaiyi Liang;Guotao Quan;Dong Liang;Hairong Zheng;Zhanli Hu\",\"doi\":\"10.1109/TRPMS.2024.3402272\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from the low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both the simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in the clinical low-dose PCT imaging tasks.\",\"PeriodicalId\":46807,\"journal\":{\"name\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"volume\":\"8 7\",\"pages\":\"762-773\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10533269/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10533269/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Building a Kinetic Induced Voxel-Clustering Filter (KVCF) for Low-Dose Perfusion CT Imaging
Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from the low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both the simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in the clinical low-dose PCT imaging tasks.