用深度图像先验解决光声成像的有限视角伪影问题
Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts
- 利用深度图像先验无监督重建，避免依赖标注数据
- 在有限视角下比传统TV方法提升多个定量指标
- 适合实验噪声大、视角受限的光声成像场景
我们研究将深度图像先验（DIP）框架应用于光声成像（PAT），作为无监督重建方法以缓解实验中常见的有限视角伪影和噪声问题。通过采用近期提出的圆形测量几何下的快速正向与伴随算法，实现高效计算。通过快速逆问题求解和总变差（TV）正则化进行初始化，进一步抑制噪声并减轻过拟合。对比实验包括有限视角下的模拟PAT测量及不同噪声水平的数据，以及实际测量结合数字孪生进行质量评估。结果表明，即使在挑战性的有限视角条件下，DIP框架仍能提供稳健的重建效果，在多个定量指标上优于传统TV重建。
原文摘要 · Abstract (English)
We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited-view artifacts and noise commonly encountered in experimental settings. Efficient implementation is achieved by employing recently published fast forward and adjoint algorithms for circular measurement geometries. Initialization via a fast inverse and total variation (TV) regularization are applied to further suppress noise and mitigate overfitting. For comparison, we compute a classical TV reconstruction. Our experiments comprise simulated PAT measurements under limited-view geometries and varying levels of added noise as well as experimental measurements together with using a digital twin for quality assessment. Our findings suggest that DIP framework provides an effective unsupervised strategy for robust PAT reconstruction even in the challenging case of a limited view geometry providing improvement in several quantitative measures over total variation reconstructions.
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