Deep-Learning-Aided Intraframe Motion Correction for Low-Count Dynamic Brain PET

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Erik Reimers;Ju-Chieh Cheng;Vesna Sossi
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引用次数: 0

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.
低计数动态脑 PET 的深度学习辅助帧内运动校正
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。
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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
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