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一个模块化的深度学习管道,用于增强平面波束成形和B模式图像质量

Hamza Hadri¹, Abderahhim Fail¹, Mohamed Sadik¹

¹1NEST Research Group, LRI Lab, ENSEM of Hassan II University of Casablanca., Casablanca, Morocco.

Medical physics

|July 23, 2025

概括

这项研究引入了一种用于超声成像的新型模块化光束成形管道,显著提高图像质量和对比度,特别是在无声结构中. 新方法通过克服传统平面波复合技术的局限性,提高了诊断准确度.

科学领域:

医疗成像医学成像
超声波技术超声波技术超声波技术
人工智能在医学中的应用

背景情况:

平面波 (PW) 超声波成像通常在图像质量和对比度方面扎,特别是在无声结构方面.
传统的光束成形方法,如延迟和总和和的PW复合,在分辨率和率之间进行了权衡,影响了诊断准确性.

研究的目的:

引入一个模块化束形管道,旨在提高PW超声波图像质量和对比度.
通过两阶段的光束成形方法,提高超声波成像过程的模块性.

主要方法:

实现一个模块化束形管道,包括一个多注意力U-Net模型和一个超分辨率模型.
使用模拟,实验和体内数据 (PICMUS数据集) 的评估.
与传统方法 (PW1,PW9,U-Net) 进行比较,使用对比比率 (CNR),对比比率 (CR) 和通用对比比率 (gCNR) 等指标.

主要成果:

拟议的模型在模拟,实验和体内数据集中表现出卓越的性能.
与传统方法相比,在CNR,CR和ChnR中观察到显著的改善,特别是在无声区域.
量化收益包括在模拟数据上CNR提高0.99分,CR增加3.5dB.

结论:

模块化光束成形方法为超声波成像提供了适应性和卓越的图像质量.
管道的灵活性允许在各种临床应用中定制率和图像质量.
这种方法为超声波中的传统光束成形技术提供了一个有希望的替代方案.

关键词:

深度学习是一种深度学习.生成式对抗网络 (GAN) 是一种产生式对抗网络.模块化光束成形管道模块化光束成形管道平面-波形象成像技术超声波波束成形仪超声波波束成形仪

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