Dehner Christoph, Lilaj Ledia, Ntziachristos Vasilis, Zahnd Guillaume, Jüstel Dominik
iThera Medical GmbH, Munich, Germany.
Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany.
Photoacoustics. 2025 May 10;44:100727. doi: 10.1016/j.pacs.2025.100727. eCollection 2025 Aug.
Model-based reconstruction provides state-of-the-art image quality for multispectral optoacoustic tomography. However, optimal regularization of in vivo data necessitates scan-specific adjustments of the regularization strength to compensate for fluctuations of the signal magnitudes between different sinograms. Magnitude fluctuations within in vivo data also pose a challenge for supervised deep learning of a model-based reconstruction operator, as training data must cover the complete range of expected signal magnitudes. In this work, we derive a scale-equivariant model-based reconstruction operator that automatically adjusts the regularization strength based on the norm of the input sinogram, and facilitates supervised deep learning of the operator using input singorams with a fixed norm. Scale-equivariant model-based reconstruction applies appropriate regularization to sinograms of arbitrary magnitude, achieves slightly better accuracy in quantifying blood oxygen saturation, and enables more accurate supervised deep learning of the operator.
基于模型的重建为多光谱光声断层扫描提供了最先进的图像质量。然而，对体内数据进行最优正则化需要针对特定扫描调整正则化强度，以补偿不同正弦图之间信号幅度的波动。体内数据中的幅度波动也给基于模型的重建算子的监督深度学习带来了挑战，因为训练数据必须覆盖预期信号幅度的完整范围。在这项工作中，我们推导了一种尺度等变的基于模型的重建算子，它基于输入正弦图的范数自动调整正则化强度，并使用具有固定范数的输入正弦图促进算子的监督深度学习。尺度等变的基于模型的重建对任意幅度的正弦图应用适当的正则化，在量化血氧饱和度方面实现了略高的准确性，并使算子的监督深度学习更加准确。
