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基于深度学习的原始数据一致的视野扩展,用于双源双能量CT

Joscha Maier¹, Julien Erath^1,2, Stefan Sawall^1,2

¹German Cancer Research Center (DKFZ), Heidelberg, Germany.

Medical physics

|August 31, 2023

概括

本研究引入了一种深度学习方法,用于在双源CT扫描中恢复缺失的光谱信息,将双能量应用扩展到更大的患者. 与现有方法相比,新方法显著减少了重建错误.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

双源双能CT (DECT) 系统面临技术限制,将一个探测器对的测量场 (FOM) 限制在约35厘米.
这种有限的FOM将DECT应用限制在较小的患者身上,并将较大的个体排除在综合光谱分析之外.
患者扫描外围缺少光谱信息,阻碍了DECT对不同患者群体的充分使用.

研究的目的:

开发和评估基于深度学习的代重建技术,以在DECT的有限FOM之外恢复光谱信息.
为整个患者截面实现双能量应用,克服当前的FOM限制.
通过恢复丢失的数据,提高DECT对较大患者的诊断能力.

主要方法:

提出了一个基于深度学习的代重建算法,利用神经网络来改进CT估计.
该算法使用从较大的FOM (50厘米) 中的重建作为初始估计,并代地改进它.
培训包括模拟的胸部,腹部和骨盆扫描,这些扫描来自70个全身CT数据集,并通过模拟和测量DECT扫描进行验证.

主要成果:

提出的深度学习方法成功地为整个患者截面生成了无文物CT重建,包括在有限的FOM之外的区域.
模拟数据显示平均重建误差为1017 HU,约为参考方法的一半.
真实幻影测量实现了类似的性能,平均误差为8 HU.

结论:

基于深度学习的代重建有效地恢复了双源CT系统中缺少的双能量信息.
这种方法使得双能量应用能够涵盖整个患者的横截面,无论FOM限制如何.
该技术具有显著的潜力,可以在更广泛的患者大小范围内扩大DECT的临床实用性.

关键词:

深度学习是一种深度学习.双源双能 CT 双源双能 CT 视野扩展视野扩展

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