Huidong Xie , Liang Guo , Alexandre Velo , Zhao Liu , Qiong Liu , Xueqi Guo , Bo Zhou , Xiongchao Chen , Yu-Jung Tsai , Tianshun Miao , Menghua Xia , Yi-Hwa Liu , Ian S. Armstrong , Ge Wang , Richard E. Carson , Albert J. Sinusas , Chi Liu
{"title":"通过自我监督实现铷-82 心脏正电子发射计算机断层成像的噪声感知动态图像去噪和正电子射程校正。","authors":"Huidong Xie , Liang Guo , Alexandre Velo , Zhao Liu , Qiong Liu , Xueqi Guo , Bo Zhou , Xiongchao Chen , Yu-Jung Tsai , Tianshun Miao , Menghua Xia , Yi-Hwa Liu , Ian S. Armstrong , Ge Wang , Richard E. Carson , Albert J. Sinusas , Chi Liu","doi":"10.1016/j.media.2024.103391","DOIUrl":null,"url":null,"abstract":"<div><div>Rubidium-82 (<span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span>) is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span>, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> emits high-energy positrons. Compared with other tracers such as <span><math><mrow><msup><mrow></mrow><mrow><mn>18</mn></mrow></msup><mtext>F</mtext></mrow></math></span>, <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09<span><math><mtext>%</mtext></math></span> to 7.58<span><math><mtext>%</mtext></math></span> on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against <span><math><mrow><msup><mrow></mrow><mrow><mn>15</mn></mrow></msup><mtext>O-water</mtext></mrow></math></span> scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner. The presented method enhanced defect contrast and resulted in lower regional MBF in areas with perfusion defects. Lastly, comparison with other related methods is included to show the effectiveness of the proposed method.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"100 ","pages":"Article 103391"},"PeriodicalIF":10.7000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision\",\"authors\":\"Huidong Xie , Liang Guo , Alexandre Velo , Zhao Liu , Qiong Liu , Xueqi Guo , Bo Zhou , Xiongchao Chen , Yu-Jung Tsai , Tianshun Miao , Menghua Xia , Yi-Hwa Liu , Ian S. Armstrong , Ge Wang , Richard E. Carson , Albert J. Sinusas , Chi Liu\",\"doi\":\"10.1016/j.media.2024.103391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Rubidium-82 (<span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span>) is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span>, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> emits high-energy positrons. Compared with other tracers such as <span><math><mrow><msup><mrow></mrow><mrow><mn>18</mn></mrow></msup><mtext>F</mtext></mrow></math></span>, <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for <span><math><mrow><msup><mrow></mrow><mrow><mn>82</mn></mrow></msup><mtext>Rb</mtext></mrow></math></span> cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09<span><math><mtext>%</mtext></math></span> to 7.58<span><math><mtext>%</mtext></math></span> on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against <span><math><mrow><msup><mrow></mrow><mrow><mn>15</mn></mrow></msup><mtext>O-water</mtext></mrow></math></span> scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner. The presented method enhanced defect contrast and resulted in lower regional MBF in areas with perfusion defects. Lastly, comparison with other related methods is included to show the effectiveness of the proposed method.</div></div>\",\"PeriodicalId\":18328,\"journal\":{\"name\":\"Medical image analysis\",\"volume\":\"100 \",\"pages\":\"Article 103391\"},\"PeriodicalIF\":10.7000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical image analysis\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1361841524003165\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical image analysis","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361841524003165","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision
Rubidium-82 () is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of , there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, emits high-energy positrons. Compared with other tracers such as , travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09 to 7.58 on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner. The presented method enhanced defect contrast and resulted in lower regional MBF in areas with perfusion defects. Lastly, comparison with other related methods is included to show the effectiveness of the proposed method.
期刊介绍:
Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.