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深度学习重建用于加速乳房合成磁共振成像:加速和标准协议的比较分析

Fan Yang¹, Yu Jiang¹, Yitian Xiao¹

¹Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.

Quantitative imaging in medicine and surgery

|October 13, 2025

概括

深度学习重建 (DLR) 显著加快合成磁共振成像 (SyMRI) 乳腺癌诊断,减少扫描时间超过37%,同时保持诊断准确性和图像质量.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
在瘤学瘤学.

背景情况:

合成磁共振成像 (SyMRI) 显示了乳腺癌诊断的前景,但由于长时间的扫描时间而受到影响.
加快SyMRI获取对于更广泛的临床采用和改善患者体验至关重要.

研究的目的:

评估深度学习重建 (DLR) 的可行性,以加速乳腺成像中的SyMRI.
为了比较加速SyMRI与DLR的图像质量,定量准确性和诊断性能,与标准SyMRI进行比较.

主要方法:

怀疑患有乳腺癌的58名患者接受了标准的2 × SyMRI,加速的3 × SyMRI和3 × SyMRI与DLR (3 × SyMRI-DLR).
评估了定量参数 (T1,T2,PD),信号噪声比 (SNR) 和主观图像质量.
用ROC分析评估乳腺癌检测和三阴性乳腺癌 (TNBC) 分类的诊断性能.

主要成果:

3×SyMRI和3×SyMRI-DLR协议显著减少了扫描时间.
定量参数 (T1,T2,PD) 没有显示跨协议的显著差异,在3×SyMRI-DLR和标准SyMRI之间存在强烈的相关性.
与3×SYMRI相比,3×SYMRI-DLR显示出更高的解剖学清晰度和统一性,诊断性能与癌症检测和TNBC分类的标准SYMRI相比相当.

结论:

深度学习重建有效地加速乳房成像的SYMRI,与标准SYMRI相比,减少37.46%的扫描时间.
DLR加速的SyMRI保持了定量准确性和诊断性能,使其成为临床应用的可行替代方案.

关键词:

深度学习的重建 (DLR)乳腺癌乳腺癌乳腺癌乳房成像检查乳房成像检查合成磁共振成像 (SyMRI) 是一种合成磁共振成像技术.三重阴性乳腺癌 (TNBC) 是一种

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