PSCD：基于单次测试时训练的相干引导金字塔自对比解耦超声成像方法
Coherence-Guided Pyramid Self-Contrastive Disentanglement for Ultrasound Imaging via Single-Shot Test-Time Training
AI 导读
研究者提出金字塔自对比解耦（PSCD）超声成像模型，将 IQ 信号相干性推广到可学习潜在空间，把信号解耦为解剖结构与噪声两部分，无需预训练且可在测试时用单次成像数据训练。仿真实验中噪声水平 0 到 30 dB 下 SNR 提升 69.3%、CNR 提升 34.4%；在人体心脏六个超声切面等在体实验中，仅用两个孔径即实现 SNR 提升 84.8%、CNR 提升 25.7%，并提高分辨率与边界清晰度。
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