arXiv cs.AI· Jiajing Zhang, Bingze Dai, Xi Zhang, Yue Xu, Wei-Ning Lee·· 15 小时前AI 评分19
基于相干性引导金字塔自对比解耦的超声成像单样本测试时训练方法
Coherence-Guided Pyramid Self-Contrastive Disentanglement for Ultrasound Imaging via Single-Shot Test-Time Training
AI 导读
研究提出金字塔自对比解耦（PSCD）模型，将IQ信号相干性推广到可学习隐空间并作为对比判别器，把接收信号隐式解耦为相干解剖空间与非相干噪声空间，仅从解剖空间估计干净IQ信号用于B模式成像。该方法可在单样本测试时训练，仿真中SNR提升69.3%、CNR提升34.4%；仅用两个孔径的活体实验（六个超声心动图视图及腹部器官）SNR提升84.8%、CNR提升25.7%，并保持更高分辨率与边界锐度。
正文 · 原文
Abstract:The inherent electronic noise, side lobes, and speckle noise complicate clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit noise assumptions that may underperform in composite noise conditions. Learning-based methods are usually pretrained in a limited image domain and face inevitable domain shift in complex \textit{in vivo} environments. This study proposes a computational ultrasound imaging pipeline with a Pyramid Self-Contrastive Disentanglement (PSCD) model. We generalize the explicit IQ signal coherence to a learnable latent space and formulate it as a contrastive critic to implicitly disentangle the received IQ signals into a coherent anatomy space and an incoherent noise space. Clean IQ signals for B-mode image formation are estimated from the anatomy space exclusively. We first apply PSCD to synthetic aperture ultrasound, where an Aperture-to-Aperture proxy task constrains the selective reconstruction in dual domains. This framework can be trained at test time using single-shot imaging data. Simulation experiments, including noise levels from 0 to 30 dB with simple to complex geometries, demonstrated improvements of 69.3$\%$ in SNR and 34.4$\%$ in CNR. The \textit{in vivo} experiments, including human hearts in six echocardiographic views and other abdominal organs, showed 84.8$\%$ SNR and 25.7$\%$ CNR gains using only two apertures. Our PSCD also achieved higher image resolution and boundary sharpness, while preserving the high-frequency details. It can naturally generalize to other ultrasound imaging pipelines involving multiple transmit-receive events, enabling precise anatomical visualization without domain shift and pretraining costs.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.12567 [cs.CV] |
| (or arXiv:2605.12567v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12567 arXiv-issued DOI via DataCite |
Submission history
From: Jiajing Zhang [view email]
[v1]
Tue, 12 May 2026 09:27:21 UTC (28,679 KB)
[v2]
Wed, 17 Jun 2026 14:38:16 UTC (21,701 KB)
[v3]
Sun, 25 Oct 2026 16:10:49 UTC (28,885 KB)
来源：arXiv cs.AI · arxiv.org