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DCE-Qnet:深度网络量化动态对比增强 (DCE) MRI

Ouri Cohen¹, Soudabeh Kargar², Sungmin Woo³

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 320 East 61st St 10025, USA. coheno1@mskcc.org.

Magma (New York, N.Y.)

|August 7, 2024

概括

一个新的深度学习模型,DCE-Qnet,通过从单个扫描中估计 perfusion 参数和组织特性来增强动态对比增强型MRI (DCE-MRI) 量化,提高精度和减少扫描时间.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
量化MRI是指数量化的MRI.

背景情况:

动态对比增强型MRI (DCE-MRI) 提供了临床见解,但在药物动力学建模方面面临广泛采用的挑战.
准确量化DCE-MRI参数对于可靠的临床解释至关重要.

研究的目的:

开发和验证一个深度学习方法 (DCE-Qnet) 进行全面的DCE-MRI量化.
评估与传统方法相比DCE-Qnet的性能,并评估其可重现性.

主要方法:

一个7层的神经网络,DCE-Qnet,在模拟的DCE-MRI数据上使用扩展Tofts模型和帕克动脉输入函数进行训练.
网络培训包括B1异质性,以估计输液 (Ktrans,vp,ve),T1放松,质子密度和玻尿酸到达时间 (BAT).
验证涉及数字幻影测试与非线性最小正方形拟合 (NLSQ) 和健康受试者和宫癌患者的体内研究.

主要成果:

在幻影研究中,DCE-Qnet表现出比NLSQ更好的性能.
在体内,不同参数的健康子宫的个体间变异性在5-51%之间.
瘤区域的可复制性显示变化系数在1-47%之间.

结论:

DCE-Qnet能够从单个采集中进行全面的DCE-MRI量化,整合输液,T1放松和BAT估计.
这种方法将扫描时间缩短约10分钟,并提高了量化准确性.
该方法有望提高瘤学和其他应用中的临床效用.

关键词:

DCE DCE 是一个非常重要的组织.无人机无人机是一个无人机.深度学习是一种深度学习.神经网络的神经网络

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Magnetic Resonance Imaging

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