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在心脏SPECT中比较后重建和基于重建的深度学习方法

Antti Sohlberg^1,2, Tuija Kangasmaa³, Antti Tikkakoski⁴

¹Department of Nuclear Medicine, Päijät-Häme Central Hospital, Lahti, Finland.

Biomedical physics & engineering express

|September 4, 2023

概括

深度学习方法显著提高了心肌 perfusion SPECT (MPS) 成像质量. 基于重建的深度学习 (DLP) 提供了更好的缺陷检测,而后重建无误化 (OSEM+DL) 则产生了更光滑的图像,这两种方法都优于传统方法.

科学领域:

核医学是一种核医学.
医疗成像医学成像
医疗保健中的人工智能

背景情况:

心肌 perfusion SPECT (MPS) 图像质量经常受到低数统计数据的损害,可能导致诊断错误.
深度学习 (DL) 为增强低数MPS研究提供了有希望的解决方案.

研究的目的:

为了比较MPS的后重建与基于重建的DL消除方法的有效性.
评估这些DL方法对图像噪声,对比度和缺陷检测的影响.

主要方法:

一个UNET类型的DL网络被训练在低活动的MPS数据上.
评估了两种DL方法:后重建拒绝 (OSEM+DL) 和整合到重建中的DL处罚 (DLP).
使用噪音水平,心肌室对比度和人工 perfusion 缺陷的信号噪音比率 (SNR) 来评估性能.

主要成果:

在所有活动中,OSEM+DL实现了最低的噪音水平.
与标准OSEM相比,OSEM+DL和DLP都表现出优异的缺陷检测能力.
与OSEM+DL相比,DLP提供了更高的SNR和更好的心肌-心室对比.

结论:

无论是后重建还是基于重建的DL denoising都显著提高了MPS图像质量.
方法之间的选择涉及图像光滑性和缺陷检测性能之间的权衡.

关键词:

在SPECT重建的重建.心脏 SPECT 的情况.深度学习是一种深度学习.拒绝的意思是拒绝.

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