在重建过程中,使用双相学习卷积神经网络进行了SPECT-MPI代无效化
Farnaz Yousefzadeh1, Mehran Yazdi2, Seyed Mohammad Entezarmahdi3
1Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
EJNMMI physics
|October 8, 2024
概括
这项研究引入了一种代的深度无色化方法,用于单光子发射计算机断层扫描心肌输液成像. 这种新的方法可以提高图像对比度,并比传统的过技术更有效地减少噪音.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 单光子发射计算断层扫描 (SPECT) 心肌 perfusion 成像 (MPI) 面临着图像消噪的挑战.
- 现有的无色化方法可以降低图像对比度,影响诊断准确度.
研究的目的:
- 开发和评估一个基于深度神经网络的denoising方法,集成到SPECT MPI的代重建过程中.
- 为了降低背景变化系数 (COV_bkg) 并改善无色图像中的对比度和噪声比率 (CNR).
主要方法:
- 采用了生成对抗网络 (GAN),分两个阶段进行训练:对有限的图像区域进行初始训练,然后对全尺寸图像进行微调.
- 该网络使用SPECT-MPI数据训练和验证了247名高噪声和低噪声扫描患者的数据.
主要成果:
- 拟议的代深度消毒方法与重建后的低通选相比,可显著降低COV_bkg高达10.28%,与重建后的深度消毒相比,可显著降低12.52%.
- 与相同的比较方法相比,对比度和噪声比率 (CNR) 提高了多达54.54%和45.82%.
结论:
- 代深度无色化方法在2D低通高斯过和后重建深度无色化方法上表现出卓越的性能.
- 这种技术为提高SPECT MPI中的图像质量提供了一个有希望的解决方案,通过在有效减少噪声的同时保持对比度.
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