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基于深度学习的重建在T2加权和扩散加权前列腺MRI图像质量的评估

Kang-Lung Lee¹, Dimitri A Kessler², Simon Dezonie³

¹Department of Radiology, University of Cambridge, United Kingdom; Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.

European journal of radiology

|August 4, 2023

概括

基于深度学习的重建 (DLR) 通过减少扫描时间和改善信号与噪声比 (SNR) 和对比与噪声比 (CNR) 来增强前列腺MRI. 这种先进的技术保持了图像质量和PI-RADS分类准确性.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能在医学中的应用

背景情况:

前列腺癌的检测严重依赖于MRI,特别是T2加权成像 (T2WI) 和扩散加权成像 (DWI).
优化图像质量和减少检查时间对于高效准确的前列腺MRI解释至关重要.

研究的目的:

评估基于深度学习的重建算法 (DLR) 对前列腺MRI的影响.
为了评估DLR对图像质量,PI-RADS分类和各种设置中的扫描持续时间的影响.

主要方法:

40名患者接受了标准护理 (SoC) 前列腺MRI (T2WI,DWI) 与或没有DLR.
分析了不同的DLR降噪水平,更快的序列 (T2WI ((快速),DWI ((快速)) 和更高分辨率的T2WI ((HR)).
进行了定量指标 (SNR,CNR,ADC) 和由两名放射科医生进行的定性评估.

主要成果:

与DLR外收购相比,DLR显著增加了SNR和CNR.
德勤航空公司将T2WI ((快速) 和DWI ((快速) 获取时间分别减少了33%和49%,而不会影响质量.
使用DLR的T2WI ((HR) 实现了空间分辨率的73%增加;DLR优化的快速序列获得了最高的定性分数.

结论:

基于深度学习的重建 (DLR) 有效地减少前列腺MRI获取时间.
DLR增强了SNR和CNR,而不会对图像质量或PI-RADS分类产生负面影响.
优化的DLR协议为高效和高质量的前列腺癌成像提供了一个有希望的方法.

关键词:

深度学习重建的重建扩散权重成像技术的使用.图像质量图像质量前列腺前列腺前列腺用T2加权成像技术进行成像.

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