Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct the PA image with a supervised scheme, which requires high-quality images as ground truth labels. In practice, there are inevitable trade-offs between cost and performance since the use of more channels is an expensive strategy to access more measurements. Here, we propose a cross-domain unsupervised reconstruction (CDUR) strategy with a pure transformer model, which overcomes the lack of ground truth labels from limited PA measurements. The proposed approach exploits the equivariance of PACT to achieve high performance with a smaller number of channels. We implement a self-supervised reconstruction in a model-based form. Meanwhile, we also leverage the self-supervision to enforce the measurement and image consistency on three partitions of measured PA data, by randomly masking different channels. We find that dynamically masking a high proportion of the channels, e.g., 80%, yields nontrivial self-supervisors in both image and signal domains, which decrease the multiplicity of the pseudo solution to efficiently reconstruct the image from fewer PA measurements with minimum error of the image. Experimental results on in-vivo PACT dataset of mice demonstrate the potential of our unsupervised framework. In addition, our method shows a high performance (0.83 structural similarity index (SSIM) in the extreme sparse case with 13 channels), which is close to that of supervised scheme (0.77 SSIM with 16 channels). On top of all the advantages, our method may be deployed on different trainable models in an end-to-end manner.

翻译：精确的图像重建对光声（PA）计算机断层扫描（PACT）至关重要。近年来，深度学习通过监督方案重建光声图像，该方案需要高质量图像作为真实标签。实践中，成本与性能之间存在不可避免的权衡，因为使用更多通道是获取更多测量值的昂贵策略。本文提出了一种基于纯Transformer模型的跨域无监督重建（CDUR）策略，该策略克服了有限PA测量中真实标签缺失的问题。所提方法利用PACT的等变性，以更少的通道实现高性能。我们以模型驱动形式实现自监督重建。同时，我们利用自监督对测量的PA数据的三部分进行测量与图像一致性约束，通过随机掩蔽不同通道实现。研究发现，动态掩蔽高比例通道（如80%）可在图像域和信号域生成非平凡的自监督信号，从而降低伪解的多重性，以最小图像误差从更少的PA测量中高效重建图像。对小鼠体内PACT数据集的实验结果证明了我们无监督框架的潜力。此外，我们的方法在极端稀疏情况（13个通道）下表现出高性能（结构相似性指数SSIM为0.83），接近监督方案（16个通道下SSIM为0.77）。除了这些优势外，我们的方法可以端到端方式部署于不同可训练模型上。