Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2024-11-20 DOI:10.1016/j.media.2024.103391
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
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Abstract

Rubidium-82 (82Rb) is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of 82Rb, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of 82Rb 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, 82Rb emits high-energy positrons. Compared with other tracers such as 18F, 82Rb 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 82Rb 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 15O-water 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.
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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。
铷-82(82Rb)是一种广泛用于心脏 PET 成像的放射性同位素。尽管 82Rb 有很多优点,但有几个因素限制了它的成像质量和定量准确性。首先,82Rb 的半衰期短,导致动态帧噪声大。低信噪比会导致图像定量不准确和有偏差。动态帧噪声大也会导致参数图像噪声大。由于放射性示踪剂衰变和半衰期短,不同动态帧的噪声水平也有很大差异。由于缺乏成对的训练输入/标签,现有的去噪方法无法在不同的噪声水平下通用,因此不适用于这项任务。其次,82Rb 发射高能正电子。与 18F 等其他示踪剂相比,82Rb 在湮灭前的飞行距离更长,这会对图像的空间分辨率产生负面影响。本研究的目的是提出一种自监督方法,用于同时对 82Rb 心脏 PET 成像进行(1)噪声感知动态图像去噪和(2)正电子射程校正。通过对一系列正常志愿者的正电子发射计算机断层扫描进行测试,所提出的方法生成的图像具有卓越的视觉质量。为了证明图像量化的改进,我们将图像衍生输入函数(IDIF)与来自连续动脉血样本的动脉输入函数(AIF)进行了比较。与原始动态帧相比,由建议方法得出的 IDIF 降低了 AUC 差异,平均从 11.09% 降至 7.58%。经 15O 水扫描验证,提出的方法还改善了心肌血流(MBF)的量化,与原始动态帧相比,MBF 平均差异从 0.43 降至 0.09。我们还对来自不同国家、使用不同扫描仪扫描的 37 名患者进行了通用性实验。所提出的方法增强了缺损对比度,并降低了灌注缺损区域的区域 MBF。最后,我们还与其他相关方法进行了比较,以显示所提方法的有效性。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
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.
期刊最新文献
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]. Editorial for Special Issue on Foundation Models for Medical Image Analysis. Few-shot medical image segmentation with high-fidelity prototypes. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. AutoFOX: An automated cross-modal 3D fusion framework of coronary X-ray angiography and OCT.
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