用真实虚拟模型验证深度学习重建方法在乳腺3D光声成像中的表现
Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography
- 构建基于真实解剖生理的随机数值体模评估3D深度学习重建
- 在噪声与声学畸变下测试方法，发现对个体差异敏感
- 为算法开发提供早期验证框架，适合医学影像研究者
定量光声计算机断层成像（qPACT）是一种可估算血氧饱和度等生理参数的有前景成像技术。然而，由于计算需求高、建模困难及实验不确定性，开发稳健的qPACT重建方法仍具挑战性。基于学习的方法被提出以应对这些问题，但大多缺乏有效验证。虚拟成像（VI）研究对于在进入非受控的体模或活体研究前，早期验证此类方法至关重要。有效的VI研究需采用能准确反映相关解剖与生理特征的随机生成数值体模。然而，多数先前的qPACT VI研究依赖于过于简化的体模。本文首次采用真实的虚拟成像测试平台，评估一种代表性3D学习型qPACT重建方法在乳腺成像中的性能。该方法在个体差异以及测量噪声和声学畸变等物理因素下进行了评估，揭示了其优势与局限性。
原文摘要 · Abstract (English)
Quantitative photoacoustic computed tomography (qPACT) is a promising imaging modality for estimating physiological parameters such as blood oxygen saturation. However, developing robust qPACT reconstruction methods remains challenging due to computational demands, modeling difficulties, and experimental uncertainties. Learning-based methods have been proposed to address these issues but remain largely unvalidated. Virtual imaging (VI) studies are essential for validating such methods early in development, before proceeding to less-controlled phantom or in vivo studies. Effective VI studies must employ ensembles of stochastically generated numerical phantoms that accurately reflect relevant anatomy and physiology. Yet, most prior VI studies for qPACT relied on overly simplified phantoms. In this work, a realistic VI testbed is employed for the first time to assess a representative 3D learning-based qPACT reconstruction method for breast imaging. The method is evaluated across subject variability and physical factors such as measurement noise and acoustic aberrations, offering insights into its strengths and limitations.
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