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基于深度学习的图像质量增强方法对数字BGO PET/CT系统对F-FDG全身检查的影响

Kenta Miwa¹, Shin Yamagishi², Shun Kamitaki²

¹Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-Shi, Fukushima, 960-8516, Japan. kenta5710@gmail.com.

|March 28, 2025

概括

数字PET/CT系统Omni Legend 32与精确DL (PDL) 显著提高了病变检测和图像质量. 将BSREM重建与PDL结合起来,可以提高SUVmax,SBR和SNR,从而获得更好的瘤成像.

科学领域:

医疗成像医学成像
核医学是一种核医学.
放射学放射学是一门学科.

背景情况:

奥姆尼传奇32 PET/CT系统使用修改后的BSREM重建和基于深度学习的图像增强过程 (PDL).
用幻影和临床数据评估PDL的基本特征.

研究的目的:

定义精密DL (PDL) 图像增强过程的基本特征.
评估PDL对PET/CT扫描图像质量的影响.

主要方法:

使用不同BSREMβ值和PDL强度 (LPDL,MPDL,HPDL) 的NEMA IEC身体幻影的幻影研究.
评估对比度恢复,背景变化和CNR.
用30个18F-FDG PET/CT扫描进行的临床研究分析了噪声,SUV平均值,SNR,SBR和SUVmax.
盲人读者对图像质量的视觉评估.

主要成果:

增加的BSREMβ值和PDL强度改善了CNR,SNR和SUVmax,同时降低了背景变化和SBR.
与PDL相结合的BSREM在噪声,SBR和SNR方面表现优于OSEM+PSF.
与OSEM+PSF相比,使用MPDL的BSREM (β=400-600) 显示SUVmax,SBR和SNR显著增加.
视觉评估表明BSREM与β=600和MPDL提供了最好的整体图像质量.

结论:

与OSEM+PSF相比,BSREM与PDL相结合显著提高了病变SUVmax和整体图像质量.
建议使用BSREM (β=500-600) 和MPDL的组合用于瘤全身PET/CT成像与Omni Legend上的PDL.

关键词:

BSREMEM 是一个特别的项目.深度学习是一种深度学习.聚乙烯/聚乙烯/聚乙烯

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