一个混合注意力网络,用于光鲜血液心肌T2*MRI放松计在thalassemia中的自动化腹腔间隔膜细分

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Xiaofeng Wu¹, Hangyu Wang¹, Zeluan Chen¹

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China (X.W., H.W., Z.C., S.S., Z.L., X.Z., Y.F.); Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China (X.W., H.W., Z.C., S.S., Z.L., X.Z., Y.F.); Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou 510000, China (X.W., H.W., Z.C., S.S., Z.L., X.Z., Y.F.).

Academic radiology

|June 1, 2025

概括

一种新的深度学习方法在MRI扫描中准确地细分了室间隔膜. 这有助于精确测量心脏铁含量,改善非侵入性监测.

科学领域:

心血管成像 - 心血管成像
人工智能在医学中的应用
血液学血液学血液学

背景情况:

thalassemia患者经常经历心脏铁过载,这是发病率和死亡率的主要原因.
准确评估心肌铁沉积对于及时治疗和改善患者结果至关重要.
目前用于评估心脏铁的方法,如T2*MRI,需要精确细分心脏结构.

研究的目的:

开发和验证一种基于深度学习的,用于心脏MRI中腹腔隔膜 (IS) 的自动细分方法.
利用自动化IS细分来测量心肌T2*值,并估计心脏铁沉积在血病患者.
评估与手动细分相比,深度学习方法的准确性和一致性及其对T2*测量的影响.

主要方法:

一项回顾性研究涉及419名血病患者和心脏MRI扫描.
开发一个深度学习细分网络 (MA-BBIsegNet),在1.5T图像上进行训练,并在各种数据集上进行评估,包括3.0T图像.
使用Dice分数,布兰德-阿尔特曼分析,变化系数 (CoV) 和回归分析进行性能评估,以比较自动和手动细分和T2*测量.

主要成果:

MA-BBIsegNet获得了高的子得分:0.90 (内部),0.85 (外部) 和0.81 (CHMMOTv1数据集).
从自动细分获得的T2*测量结果与手动测量结果有很强的一致性,平均差异和Cov差异很小 (例如,内部8.9%,外部6.8%).
回归分析表明自动和手动T2*值之间有很好的相关性 (r=0.98-0.99),表明了高可靠性.

结论:

开发的深度学习网络 (MA-BBIsegNet) 能够在明亮血MRI中准确和自动地细分室间隔膜.
自动化细分方便可靠的T2*量化,显示与手动测量有很强的一致性.
这种方法提供了一个有前途的工具,用于高效,非侵入性监测心肌铁沉积在血病患者.

关键词:

自动细分系统自动细分系统亮血心肌图像的明亮血液心肌图像磁共振成像技术磁共振成像技术心肌铁过载是心肌铁过载的一个原因.T2* 测量的时间

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