用神经网络压缩超声成像数据，省下90%存储空间
Compact Implicit Neural Representations for Plane Wave Images
- 用MLP神经网络建模超声波图像，保留角度相关特征
- 75张图像压缩至530KB，压缩比达15:1
- 适合需要高效存储的实时超声系统
超快速平面波（PW）成像常因入射角度不同产生伪影和阴影。本文提出一种基于隐式神经表示（INRs）的新方法，可紧凑编码多平面序列并保留关键的方向依赖信息。据我们所知，这是首次将INRs应用于PW角度插值。方法采用基于多层感知机（MLP）的模型，结合简化的物理增强渲染技术。通过结构相似性（SSIM）、峰值信噪比（PSNR）及标准超声指标进行定量评估，并辅以定性视觉分析，验证了该方法的有效性。此外，该方法展现出显著的存储效率：模型权重仅需530 KB，远低于直接存储75张PW图像所需的8 MB，压缩比约15:1。
原文摘要 · Abstract (English)
Ultrafast Plane-Wave (PW) imaging often produces artifacts and shadows that vary with insonification angles. We propose a novel approach using Implicit Neural Representations (INRs) to compactly encode multi-planar sequences while preserving crucial orientation-dependent information. To our knowledge, this is the first application of INRs for PW angular interpolation. Our method employs a Multi-Layer Perceptron (MLP)-based model with a concise physics-enhanced rendering technique. Quantitative evaluations using SSIM, PSNR, and standard ultrasound metrics, along with qualitative visual assessments, confirm the effectiveness of our approach. Additionally, our method demonstrates significant storage efficiency, with model weights requiring 530 KB compared to 8 MB for directly storing the 75 PW images, achieving a notable compression ratio of approximately 15:1.
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