用深度展开的AMP模型，提升声学显微成像的压缩重建质量。
Deep Unfolded Approximate Message Passing for Quantitative Acoustic Microscopy Image Reconstruction
- 将深度展开的近似消息传递算法用于声学显微图像重建。
- 在自然图像上预训练的模型即达63%的PSNR提升。
- 避免采样模式引起的伪影，适合高精度生物组织成像。
定量声学显微术（QAM）利用高频超声在显微尺度上生成生物组织声学与力学特性的二维定量图。频率提升可实现更高分辨率，但伴随采集时间增加和数据存储成本上升。压缩采样（CS）方法被用于从较少采样点重构QAM图像，当前最优方法采用近似消息传递（AMP）算法。本文研究了AMP-Net——一种深度展开的AMP模型——在QAM参数图压缩重建中的应用。结果表明，即使在未针对医学图像微调的默认配置下，AMP-Net仍能实现高达63%的峰值信噪比（PSNR）提升，且有效抑制由采样模式引发的伪影。
原文摘要 · Abstract (English)
Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts.
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