Quantitative photoacoustic tomography (QPAT) is a hybrid imaging modality that simultaneously reconstructs absorption and scattering coefficients with multi-source or multi-wavelength setting. In contrast with PAT, QPAT eliminates the artifacts due to photon transfer in depth and characterizes the intrinsic biological attributes. However, data acquisition for QPAT is time consuming because for each optical source (or wavelength), a 360-degree acoustic measurement on the boundary of imaging region is required. In this work, we investigate a novel limited-view multi-source (LVMS) QPAT scheme and reconstruction algorithm based on coupled opto-acousto model. A unique setting that binds optical source and acoustic detector together is presented. Under each illumination, only a limited-view measurement is acquired, which is incomplete for PAT reconstruction but sufficient for direct QPAT reconstruction; then the source and detector rotate to next data acquisition position synchronously. In terms of reconstruction, a sparsity-regularized formulation based on total variation norm is adopted here and optimized through alternating direction method of multipliers with LBFGS solver, during which the adjoint method is used for rapid computation of numerical gradient of objective function. However, the aperture effect i.e. anisotropic angular sensitivity of a finite-dimension transducer would cause temporal distortion of receiving acoustic signal and further resolution reduction. Therefore, we numerically integrate ideal PAT system spatial impulse response (SIR) with the angular response from circular transducer to improve the modeling accuracy. In summary, the proposed LVMS-QPAT has the potential to shorten the data acquisition time, enhance system SNR and improve image resolution.
Jing Feng and Hao Gao, "Limited view multi-source photoacoustic tomography with finite transducer dimension," Proc. SPIE 9708, Photons Plus Ultrasound: Imaging and Sensing 2016, 97080Y (Presented at SPIE BiOS: February 15, 2016; Published: 15 March 2016); https://doi.org/10.1117/12.2208320.
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