DCL-A:一个无监督的超声波束成形框架,具有适应性深度连贯性损失,用于单平面波浪成像
Taejin Kim1, Seongbin Hwang2, Minho Song3
1Department of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea.
Diagnostics (Basel, Switzerland)
|December 30, 2025
概括
这项研究引入了自适应性深度连贯性损失 (DCL-A) 束形成方法,以减少单平面波形象成像 (SPWI) 中的工件. 在实时超声波应用中,DCL-A方法显著提高了图像质量.
科学领域:
- 超声波成像 超声波成像
- 医学成像物理学 医学成像物理学
- 信号处理 信号处理
背景情况:
- 单平面波成像 (SPWI) 能够实现超快的超声波采集,但受到光束成形工件的损害.
- 侧叶和叶干扰降低了SPWI图像质量,限制了实时应用.
研究的目的:
- 引入一个无监督的深度学习光束形成框架,自适应深度连贯性损失 (DCL-A),以加强SPWI中的文物抑制.
- 为了提高SPWI的整体图像质量,而不会影响其高率.
主要方法:
- 开发了一个无监督的束形框架,使用具有线性或非线性权重的自适应深度连贯损失 (DCL-A).
- 适应性重量 (α) 由训练期间输入和目标平面波 (PW) 之间的角距离决定.
- 使用模拟,幻影和体内超声数据集验证了DCL-A框架.
主要成果:
- 在模拟和幻影研究中,与传统DCL和线性DCL-A相比,使用非线性加权的DCL-A实现了更高的峰值范围侧叶水平 (PRSLL) 降低 (7-14dB).
- 在整个方法中保持了可比的半最大全宽 (FWHM).
- 在体内研究表明,DCL-A与非线性加权产生了最高的对比分辨率,提高了2-3%的总体对比与噪声比率 (gCNR).
结论:
- 拟议的基于深度学习的光束成型框架显著提高了SPWI图像质量.
- 在不牺牲率的情况下,DCL-A方法有效地抑制了文物.
- 显示了高速,高分辨率临床超声波应用的潜力,如心脏成像和干预指导.
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