标记物吸收率对PET心肌血流量的量化准确度的影响:一个模拟研究
Xiaotong Hong1,2,3, Amirhossein Sanaat3, Yazdan Salimi3
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Medical physics
|May 9, 2025
概括
深度学习 (DL) 模型显著减少了使用心脏 perfusion PET成像来量化心肌动脉血流 (MBF) 的噪音和偏差. 这些先进的方法提高了准确性,特别是在较低的追踪剂吸收率下,提高了心血管风险评估.
科学领域:
- 核医学是一种核医学.
- 心血管成像 - 心血管成像
- 医学物理 医学物理
背景情况:
- 心脏输液PET对于评估心肌血流 (MBF) 和心血管风险至关重要.
- 估计MBF的动力参数是具有挑战性的,因为杂的PET数据和有限的数据点.
研究的目的:
- 通过模拟来调查PET中MBF估计中的错误.
- 评估MBF的深度学习 (DL) 和其他参数估计方法.
主要方法:
- 模拟动态13N-氨PET扫描使用基于CT图像的数字幻影.
- 与基于变压器的DL模型进行了比较,采用非线性最小平方 (NLS) 配套 (GF-NLS和DNLM-NLS).
- 在不同的休息和应力K1范围中验证了DL模型性能.
主要成果:
- 与NLS方法相比,DL方法显示噪声降低和图像结构清晰.
- 在K1范围内,DL显示了显著较低的平均绝对相对误差 (MARE),特别是在压力下.
- 与GF-NLS相比,DL和DNLM-NLS方法的MARE变化较小,压力K1变化较小.
结论:
- 更高的追踪剂吸收率 (K1) 与更好的MBF量化准确性和精度相关.
- 在PET参数成像中,DL和denoising技术有效地减轻噪声引起的偏差.
- DL模型在临床心脏PET中显示出强大的MBF估计的前景.
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