AI 中文总结
研究针对超声计算机断层扫描重建中DNN受噪声影响问题，提出MUX-USCT架构，用“自适应MUX”自动识别滤除噪声，结合注意力机制重建声速图，在基准测试中MAE表现优且参数少，在模拟临床噪声下稳定，还给出信号质量可解释指标。
AI 中文摘要
深度神经网络（DNN）在理想无噪声环境下对超声计算机断层扫描（USCT）重建显示出强大潜力，但现有DNN在临床实践的噪声条件下易受影响，因其对不同程度噪声输入同等对待。更具挑战性的是，噪声分布随环境变化，使噪声感知训练效果不佳。我们重新思考这些挑战，发现若能知晓并滤除噪声源，DNN模型对噪声会更鲁棒，此滤波操作类似数字逻辑设计中的多路复用器（MUX），但推理时噪声随机出现，手动预定义的MUX无法工作。为此，我们提出MUX-USCT，一种新颖的编解码器DNN架构，用“自适应MUX”编码已知声学采集几何结构以自动识别和滤除噪声，重建声速图时应用注意力机制。在OpenPros基准测试中，MUX-USCT的平均绝对误差（MAE）达6.88m/s，参数比领先基线少17%，基线MAE为7.65m/s。在模拟临床噪声下，它在多种导致与几何无关基线失败的退化类型中保持稳定。结果表明，MUX-USCT中的注意力分布为换能器对之间的信号质量提供了可解释指标。
英文摘要
Deep neural networks (DNNs) have shown strong potential for ultrasound computed tomography (USCT) reconstruction in ideal noise-free environments, yet existing DNNs are vulnerable to the noisy conditions in clinical practice, as they equally treat inputs that suffer mild, moderate, or severe noise. More challenging, the distributions of noise shift along with the environment, indicating the less effectiveness of noise-aware training, which injects a specific noise distribution into the training data. We rethink these challenges and observe that the DNN models can become more robust to noise if we know the noise sources and filter them out. This filtering operation is very alike the Multiplexers (or MUX), a fundamental combinational circuit in digital logic design. However, the challenge here is that noise can happen randomly during inference; as a result, the manually predefined MUX cannot work. To address these challenges, we propose MUX-USCT, a novel encoder-decoder DNN architecture that encodes the known acoustic acquisition geometry with an "adaptive MUX" that can automatically identify and filter noise, where the attention mechanism is applied in reconstructing the speed-of-sound map. On the OpenPros benchmark, MUX-USCT reaches 6.88 m/s MAE with 17% fewer parameters than the leading baseline with 7.65 m/s of MAE. Under simulated clinical noise, it remains stable across diverse degradation types that cause geometry-agnostic baselines to fail. Results show that the attention distributions in MUX-USCT provide interpretable indicators of the signal quality between pairs of transducers.
Comments10 pages, 6 figures, 3 tables. Accepted at MICCAI 2026. This is the author's accepted manuscript; the final version will appear in Springer LNCS. Code: https://github.com/TheYuchen/mux-usct-miccai2026
