低计数动态脑 PET 的深度学习辅助帧内运动校正

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Erik Reimers;Ju-Chieh Cheng;Vesna Sossi
{"title":"低计数动态脑 PET 的深度学习辅助帧内运动校正","authors":"Erik Reimers;Ju-Chieh Cheng;Vesna Sossi","doi":"10.1109/TRPMS.2023.3333202","DOIUrl":null,"url":null,"abstract":"Data-driven intraframe motion correction of a dynamic brain PET scan (with each frame duration on the order of minutes) is often achieved through the co-registration of high-temporal-resolution (e.g., 1-s duration) subframes to estimate subject head motion. However, this conventional method of subframe co-registration may perform poorly during periods of low counts and/or drastic changes in the spatial tracer distribution over time. Here, we propose a deep learning (DL), U-Net-based convolutional neural network model which aids in the PET motion estimation to overcome these limitations. Unlike DL models for PET denoising, a nonstandard 2.5-D DL model was used which transforms the high-temporal-resolution subframes into nonquantitative DL subframes which allow for improved differentiation between noise and structural/functional landmarks and estimate a constant tracer distribution across time. When estimating motion during periods of drastic change in spatial distribution (within the first minute of the scan, ~1-s temporal resolution), the proposed DL method was found to reduce the expected magnitude of error (+/−) in the estimation for an artificially injected motion trace from 16 mm and 7° (conventional method) to 0.7 mm and 0.6° (DL method). During periods of low counts but a relatively constant spatial tracer distribution (60th min of the scan, ~1-s temporal resolution), an expected error was reduced from 0.5 mm and 0.7° (conventional method) to 0.3 mm and 0.4° (DL method). The use of the DL method was found to significantly improve the accuracy of an image-derived input function calculation when motion was present during the first minute of the scan.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 1","pages":"53-63"},"PeriodicalIF":4.6000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep-Learning-Aided Intraframe Motion Correction for Low-Count Dynamic Brain PET\",\"authors\":\"Erik Reimers;Ju-Chieh Cheng;Vesna Sossi\",\"doi\":\"10.1109/TRPMS.2023.3333202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data-driven intraframe motion correction of a dynamic brain PET scan (with each frame duration on the order of minutes) is often achieved through the co-registration of high-temporal-resolution (e.g., 1-s duration) subframes to estimate subject head motion. However, this conventional method of subframe co-registration may perform poorly during periods of low counts and/or drastic changes in the spatial tracer distribution over time. Here, we propose a deep learning (DL), U-Net-based convolutional neural network model which aids in the PET motion estimation to overcome these limitations. Unlike DL models for PET denoising, a nonstandard 2.5-D DL model was used which transforms the high-temporal-resolution subframes into nonquantitative DL subframes which allow for improved differentiation between noise and structural/functional landmarks and estimate a constant tracer distribution across time. When estimating motion during periods of drastic change in spatial distribution (within the first minute of the scan, ~1-s temporal resolution), the proposed DL method was found to reduce the expected magnitude of error (+/−) in the estimation for an artificially injected motion trace from 16 mm and 7° (conventional method) to 0.7 mm and 0.6° (DL method). During periods of low counts but a relatively constant spatial tracer distribution (60th min of the scan, ~1-s temporal resolution), an expected error was reduced from 0.5 mm and 0.7° (conventional method) to 0.3 mm and 0.4° (DL method). The use of the DL method was found to significantly improve the accuracy of an image-derived input function calculation when motion was present during the first minute of the scan.\",\"PeriodicalId\":46807,\"journal\":{\"name\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"volume\":\"8 1\",\"pages\":\"53-63\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-11-16\",\"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/10319877/\",\"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/10319877/","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}
引用次数: 0

摘要

对动态脑 PET 扫描(每帧持续时间约为几分钟)进行数据驱动的帧内运动校正,通常是通过对高时间分辨率(如 1 秒持续时间)子帧进行共配准来估计受试者的头部运动。然而,这种传统的子帧共存方法在低计数和/或空间示踪剂分布随时间发生急剧变化时可能表现不佳。在此,我们提出了一种基于深度学习(DL)、U-Net 的卷积神经网络模型,该模型有助于 PET 运动估计,以克服这些局限性。与用于 PET 去噪的 DL 模型不同,我们使用的是一种非标准的 2.5-D DL 模型,该模型将高时间分辨率子帧转换为非定量 DL 子帧,从而改进了噪音与结构/功能性地标之间的区分,并估算出跨时间的恒定示踪剂分布。在空间分布急剧变化期间(扫描的前一分钟内,约 1 秒的时间分辨率)估计运动时,发现提议的 DL 方法可将人工注入运动轨迹的估计误差预期幅度(+/-)从 16 毫米和 7°(传统方法)减少到 0.7 毫米和 0.6°(DL 方法)。在低计数但空间示踪剂分布相对恒定的时期(扫描的第 60 分钟,~1 秒时间分辨率),预期误差从 0.5 毫米和 0.7°(传统方法)减小到 0.3 毫米和 0.4°(DL 方法)。当扫描的前一分钟出现运动时,使用 DL 方法可显著提高图像衍生输入函数计算的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep-Learning-Aided Intraframe Motion Correction for Low-Count Dynamic Brain PET
Data-driven intraframe motion correction of a dynamic brain PET scan (with each frame duration on the order of minutes) is often achieved through the co-registration of high-temporal-resolution (e.g., 1-s duration) subframes to estimate subject head motion. However, this conventional method of subframe co-registration may perform poorly during periods of low counts and/or drastic changes in the spatial tracer distribution over time. Here, we propose a deep learning (DL), U-Net-based convolutional neural network model which aids in the PET motion estimation to overcome these limitations. Unlike DL models for PET denoising, a nonstandard 2.5-D DL model was used which transforms the high-temporal-resolution subframes into nonquantitative DL subframes which allow for improved differentiation between noise and structural/functional landmarks and estimate a constant tracer distribution across time. When estimating motion during periods of drastic change in spatial distribution (within the first minute of the scan, ~1-s temporal resolution), the proposed DL method was found to reduce the expected magnitude of error (+/−) in the estimation for an artificially injected motion trace from 16 mm and 7° (conventional method) to 0.7 mm and 0.6° (DL method). During periods of low counts but a relatively constant spatial tracer distribution (60th min of the scan, ~1-s temporal resolution), an expected error was reduced from 0.5 mm and 0.7° (conventional method) to 0.3 mm and 0.4° (DL method). The use of the DL method was found to significantly improve the accuracy of an image-derived input function calculation when motion was present during the first minute of the scan.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信