较短的SPECT扫描使用自主监督坐标学习来合成跳过的投影视图
Zongyu Li1,2, Yixuan Jia3,4, Xiaojian Xu1
1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109-2122, USA.
EJNMMI physics
|May 20, 2025
概括
这项研究介绍了SpeRF,一种自我监督的学习方法,它合成了SPECT投影视图,以减少Lu-177 SPECT扫描的成像时间. SpeRF显著缩短扫描持续时间,同时保持定量准确性,有利于低计数和全身成像协议.
科学领域:
- 核医学就是核医学.
- 医疗成像医学成像
- 医疗保健中的人工智能
背景情况:
- 延长SPECT成像持续时间,特别是在像Lu-177 SPECT这样的低计数条件下,会带来临床挑战.
- 减少扫描时间对于患者的舒适性和提高核医学的吞吐量至关重要.
- 目前用于缩短SPECT采集时间的方法可能会损害图像质量和定量准确性.
研究的目的:
- 开发和评估一种自主监督学习方法 (SpeRF),用于合成SPECT投影视图.
- 通过减少获得的投影数量,缩短临床环境中的SPECT扫描时间,特别是在Lu-177成像中.
- 为了保持数量准确性和图像质量,尽管采集时间缩短.
主要方法:
- 开发了SpeRF,一种使用自主监督,基于坐标的学习框架的SPECT重建管道,灵感来自神经辐射场 (NeRF).
- SpeRF独立地训练一个多层感知子 (MLP) 来估计每个扫描的跳过的SPECT投影视图.
- 在Lu-177幻影和临床SPECT/CT数据集 ([177Lu]Lu-DOTATATE和[177Lu]Lu-PSMA-617) 上测试了带有下方采样因子 (DFs=2,4,8) 的SpeRF,将重建与"完全"",部分"和"线性Int"方法进行比较.
主要成果:
- 在幻象和患者研究中,SpeRF预测显示,与线性插值 (LinInt) 预测相比,正常化根平均平方差异 (NRMSD) 较低.
- 在DF=4时,SpeRF重建在DOTATATE和PSMA-617研究中都在损伤和器官的对比度 (CNR) 中超过了LinInt和Partial方法.
- SpeRF表现出良好的定量准确性,数量恢复接近"完全"获取,表现优于LinInt,特别是在器官方面.
结论:
- SpeRF可以显著减少SPECT获取时间 (高达4倍),同时保持临床协议的定量准确性.
- 由于SpeRF的自我监督性,因此无需广泛的培训数据集,每位患者独立处理数据.
- 这种方法特别有利于低数量的SPECT成像和需要多个床位的协议,例如全身成像.
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