分支卷积神经网络用于高率散射阵列超声成像中的接收器通道恢复
概括
这项研究引入了一种使用卷积神经网络 (CNN) 的新通道恢复框架,以提高高率超声波成像质量. 该方法有效地减少了文物,并增强了超声波图像的对比度,在紧的系统上实现更快,更高质量的成像.
科学领域:
- 医疗成像医学成像
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 高率超声波成像对于动态过程至关重要,但在使用稀疏阵列的紧系统中面临挑战.
- 稀疏的阵列导致由于未聚焦的平面波激发和格子叶的平图像质量.
研究的目的:
- 设计和实施通道恢复框架,从周期性稀疏数组推断全数组平面波通道数据.
- 为了提高高率超声波成像在稀疏配置的图像质量.
主要方法:
- 一个分支编码器-解码器卷积神经网络 (CNN) 架构被开发和训练在全阵列平面波数据从人类动脉.
- 三个CNN被训练在2,3和4次通道向下采样后恢复缺失的频道.
- 该框架的性能被评估在体外和体内数据,包括点目标和人体组织.
主要成果:
- 频道恢复框架成功地抑制了B模式图像中的空间别名工件,在所有测试的频道下采样级别.
- 图像对比度明显增强,相比下调样本道数据生成的图像.
- 该框架在RTX-2080 GPU上实现了4ms的快速推断时间.
结论:
- 拟议的框架有效地从稀疏的超声波阵列中恢复全阵列数据,显著提高图像质量.
- 这种方法有望在具有有限通道的紧系统上增强高率超声波成像.
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