隐含的神经表示用于无监督的超分辨率和4D流MRI无声化
Simone Saitta1, Marcello Carioni2, Subhadip Mukherjee3
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Computer methods and programs in biomedicine
|February 9, 2024
概括
鼻状表达网络 (SIREN) 通过改善胸前大动脉中血液流速的无声化和超分辨率来增强4D流动MRI. 这种方法可以显著减少错误,并保持精确的流量测量.
科学领域:
- 医疗成像医学成像
- 计算流体动力学的流体动力学.
- 人工智能的人工智能
背景情况:
- 4D流MRI提供了有价值的时间解析的血液流速数据,但面临着时空分辨率和噪声的挑战.
- 提高4D流MRI数据的质量对于准确的心血管分析至关重要.
研究的目的:
- 为了研究正弦形表示网络 (SIREN) 在4D流MRI速度场的无声化和超分辨率方面的有效性.
- 评估SIREN在表现胸前大动脉中复杂的血液流动动态方面的表现.
主要方法:
- 通过取样voxel坐标和在船壁应用防滑条件,SIREN被训练在4D中.
- 来自计算流体动力学模拟的合成4D流动MRI数据被用于评估在不同噪声水平下的性能.
- 优化了SIREN架构,并将该方法与现有的消除噪音和超分辨率技术进行了比较.
主要成果:
- 一个拥有300个神经元/层和20个层的SIREN显示出卓越的性能,将矢量RMSE降低高达50%,大小RMSE降低42%,方向误差降低15%.
- 该方法成功地在患者特异性的大动脉动脉瘤数据中检测和超分辨速度场,保留了基本的宏观流量特征.
- 对目前的4D流MRI数据增强方法来说,SIRENs提供了显著的改进.
结论:
- 状表示网络 (SIREN) 是可行的,用于表示复杂的临床4D流动MRI血液速度数据.
- 塞伦提供了一个计算效率高,易于实施的解决方案,用于提高4D流MRI数据质量.
- 这种方法有望改善临床环境中4D流MRI的诊断效用.
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