通过频率正则化平衡高低频信息，提升稀疏视角断层重建精度
Frequency-regularized Neural Representation Method for Sparse-view Tomographic Reconstruction
- 在神经网络输入中引入频率正则化，控制可见频段
- 在CBCT和SPECT数据上达到当前最优重建精度
- 适合需要低剂量成像的医学影像重建场景
稀疏视角断层重建是降低辐射剂量、提升临床应用的关键方向。尽管已有大量研究致力于从稀疏二维投影重建断层图像，但现有模型往往过度关注高频信息而忽略低频成分，导致在重建切片边缘和边界处出现严重过拟合。本文提出频率正则化的神经衰减/活性场（Freq-NAF），通过频率正则化直接控制神经网络输入中的可见频率带宽，有效平衡高低频信息，缓解过拟合问题。我们在CBCT和SPECT数据集上进行了数值实验，结果表明该方法在重建精度上达到当前最优水平。
原文摘要 · Abstract (English)
Sparse-view tomographic reconstruction is a pivotal direction for reducing radiation dose and augmenting clinical applicability. While many research works have proposed the reconstruction of tomographic images from sparse 2D projections, existing models tend to excessively focus on high-frequency information while overlooking low-frequency components within the sparse input images. This bias towards high-frequency information often leads to overfitting, particularly intense at edges and boundaries in the reconstructed slices. In this paper, we introduce the Frequency Regularized Neural Attenuation/Activity Field (Freq-NAF) for self-supervised sparse-view tomographic reconstruction. Freq-NAF mitigates overfitting by incorporating frequency regularization, directly controlling the visible frequency bands in the neural network input. This approach effectively balances high-frequency and low-frequency information. We conducted numerical experiments on CBCT and SPECT datasets, and our method demonstrates state-of-the-art accuracy.
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