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NEMA NU 2-2018评估和图像质量优化新一代数字32厘米轴向视野Omni LegendPET-CT使用遗传进化算法

Rhodri Lyn Smith¹, Lee Bartley², Christopher O'Callaghan²

¹The Wales Research and Diagnostic Positron Emission Tomography Imaging Centre, Cardiff University, School of Medicine, Cardiff, CF14 4XN, Wales, United Kingdom.

Biomedical physics & engineering express

|February 12, 2024

概括

新的GE数字Omni Legend PET-CT系统,没有飞行时间 (ToF),经过优化后表现出更好的性能. 一个基因算法增强了图像重建,在NEMA测试中超过了其他GE扫描仪.

科学领域:

医疗成像医学成像
核医学是一种核医学.
辐射物理学辐射物理学

背景情况:

通用电气 (GE) 数字Omni Legend PET-CT系统是第一个商用数字斯木德基质系统,视野扩展32厘米.
该系统不使用飞行时间 (ToF) 技术,而是依靠通过预训练的神经网络 (PDL) 的软件校正.

研究的目的:

根据NEMA NU2-2018标准对GE数字Omni Legend PET-CT系统进行性能评估.
将其性能与其他GE扩展视野扫描仪 (发现MI和发现IQ) 进行比较.
开发和应用一种遗传进化算法,以优化图像重建参数,以提高图像质量.

主要方法:

性能测试是按照NEMA NU2-2018标准进行的.
使用遗传进化算法优化贝叶斯惩罚概率重建 (Q.Clear) 参数和PDL设置.
对GE发现MI (支持ToF) 和发现IQ (非ToF) 扫描仪进行了比较分析.

主要成果:

奥姆尼在1厘米的辐射偏移下显示了3.9毫米FWHM的平均空间分辨率.
峰值噪声等效计数率 (NECR) 为501kcps,178kBq/ml,散射率为35.48%.
优化的PDL重建参数使得在所有NEMA测试中性能超过了Discovery MI,对比度恢复得到改善,背景变化减少.

结论:

通用电气数字Omni Legend PET-CT系统,当通过基因算法优化其PDL重建时,实现了与其他先进系统相比或超过的高性能.
该研究强调了算法驱动优化在提高PET-CT图像质量的有效性.
建议在临床环境中验证遗传算法的优化.

关键词:

验收测试验收测试验收测试验收测试验收测试验收测试验收测试数字PET数字PET的使用进化算法是一种进化算法.优化的优化优化优化.

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