Matrix compression and compressed sensing reconstruction for photoacoustic tomography

Online medical diagnosis is an emerging type of medical services, also it is a new kind of service for electronic commerce. Photoacoustic tomography is a promising imaging modality that can provide high contrast and spatial-resolution images of light-absorption distribution in tissue. This paper presents novel matrix compression methods for reducing required memory and accelerating reconstruction speed for model-based reconstruction. In addition, we integrated the compressed sensing into our proposed reconstruction method for solving the optimization problem. The results of phantom experiment indicate that the proposed method provides a faster and high quality reconstruction with less memory consumption. Therefore, it makes the photoacoustic tomography suitable for online or remote medical diagnosis.