  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.
