  Photoacoustic computed tomography (PACT) is a hybrid imaging modality, which
combines the high optical contrast of pure optical imaging and the high
penetration depth of ultrasound imaging. However, photoacoustic image dataset
with good quality and large quantity is lacking. In this paper, we mainly talk
about how to generate a practical photoacoustic dataset. Firstly, we extracted
389 3D vessel volumes from CT whole-lung scan database, and enhanced the blood
vessel structures. Then for each 3D vessel volume, we embedded it into a
three-layer cubic phantom to formulate a skin tissue model, which includes
epidermis, dermis, and hypodermis. The vessel volume was placed randomly in
dermis layer in 10 different ways. Thus, 3890 3D skin tissue phantoms were
generated. Then we assigned optical properties for the four kinds of tissue
types. Monte-Carlo optical simulations were deployed to obtain the optical
fluence distribution. Then acoustic propagation simulations were deployed to
obtain the photoacoustic initial pressure. Universal back-projection algorithm
was used to reconstruct the photoacoustic images. This dataset could be used
for deep learning-based photoacoustic image reconstruction, classification,
registration, quantitative image analysis.
