通过使用深度渐进式学习重建算法,提高F-FDG PET图像质量和不同体重指数的病变诊断性能
Zhihao Chen1,2,3,4,5, Hongxing Yang1,2,3,4,5, Ming Qi1,2,3,4,5
1Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
概括
深度渐进式学习 (DPL) 与传统的OSEM重建相比,显著提高了2-deoxy-2-[-18]fluoro-D-glucose (F-FDG) 定子发射断层扫描 (PET) 图像质量和病变检测在所有身体质量指数 (BMI) 类别.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 核医学是一种核医学.
背景情况:
- 增加的体重指数 (BMI) 降低了2-deoxy-2-[-18]fluoro-D-glucose (18F-FDG) 定子发射断层扫描 (PET) 图像质量,通过有序子集期望最大化 (OSEM) 重建.
- 图像质量的下降会对病变检测和诊断准确性产生负面影响.
- 需要新的重建技术来保持在各种BMI范围内一致的诊断性能.
研究的目的:
- 为了评估深度渐进式学习 (DPL) 算法对F-FDG PET/CT图像重建的有效性.
- 将DPL的图像质量和病变诊断性能与不同BMI类别的OSEM进行比较.
- 为了确定DPL是否能够克服与更高的BMI相关的图像质量退化.
主要方法:
- 150名患有不同的BMI的患者接受了F-FDG PET/CT扫描.
- 使用OSEM和DPL算法重建了PET图像.
- 图像质量被视觉 (利克尔特尺度) 和定量 (肝脏均性,信号与噪声比) 评估.
- 对466个病变的诊断性能通过比较SUVmax,SBR和DPL和OSEM之间的CNR等指标来评估.
主要成果:
- 与OSEM相比,DPL在所有BMI组中显示出优越的PET图像质量和损伤对比度.
- 虽然DPL图像质量随着BMI的增加而略有下降,但OSEM的图像质量呈现出更显著的退化.
- DPL保持了稳定的定量指标和更高的对比度,提高了病变检测能力,特别是在超重个体中.
结论:
- 与OSEM相比,DPL算法始终提供优越的F-FDG PET/CT图像质量和病变诊断性能,无论患者的BMI如何.
- DPL有效地减轻了增加BMI对PET图像质量和诊断准确性的负面影响.
- 该研究建议DPL用于例行F-FDG PET/CT重建,以提高所有患者群体的诊断信心和一致性.
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