超高分辨率光子计数探测器CT带有专用Denoising卷积神经网络,用于增强的骨成像
Shaojie Chang1, John C Benson1, John I Lane1
1From the Depsartment of Radiology (S.C., J.C.B., J.I.L., M.R.B., J.R.S., J.E.T., E.K.K., M.L.C., C.H.M., S.L.), Mayo Clinic, Rochester, Minnesota.
AJNR. American journal of neuroradiology
|November 11, 2024
概括
一个新的卷积神经网络 (CNN) 在超高分辨率 (UHR) 光子计数探测器 (PCD) CT 扫描中有效减少噪声. 这允许对骨结构进行更清晰的成像,改善诊断质量.
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:
- 超高分辨率 (UHR) 光子计数探测器 (PCD) CT 提供了更好的空间分辨率,但受到增加的图像噪声的影响.
- 通常使用更光滑的重建内核来减轻噪声,但这会损害可实现的分辨率.
- 这种限制阻碍了对细骨解剖学的最佳可视化.
研究的目的:
- 为UHR PCD-CT.开发和评估一个无声化的卷积神经网络 (CNN).
- 用最清晰的内核重建图像中的噪音,以保持高分辨率,以减少噪音.
- 提高骨结构的可视化,以提高诊断准确度.
主要方法:
- 一个CNN在1885张来自6个病例的图像上进行了训练,并使用双源PCD-CT (NAEOTOM Alpha) 在20个独立病例上进行了测试.
- 使用量子代重建 (QIR3) 重建图像,同时使用例行程序 (Hr84) 和最清晰的 (Hr96) 内核.
- 应用于Hr96重建图像 (QIR1) 的CNN,结果与神经放射学家的常规重建 (Hr84-QIR3,Hr96-QIR3) 相比.
主要成果:
- 与Hr96-QIR3相比,CNN降低了80%的噪音,与Hr84-QIR3相比,降低了50%,同时保持了高分辨率.
- 与Hr96-QIR3.3.相比,Hr96-CNN的噪音水平从204.63降至47.35 HU,而Hr96-CNN的噪音水平则从204.63降至47.35 HU.
- 与CNN处理的图像相比,模块骨的图像质量和可视化,脚的脚板和关节的可视化显著更好.
结论:
- 开发的CNN有效地减少了UHRPCD-CT图像中的噪声.
- 这使得可以使用最清晰的重建内核,而不会影响图像质量.
- 结合CNN和最清晰的内核显著提高诊断图像质量和骨的解剖可视化.
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