Model-Based Photoacoustic Image Reconstruction using Compressed Sensing and Smoothed L0 Norm

Abstract

Photoacoustic imaging (PAI) is a novel medical imaging modality that uses the advantages of the spatial resolution of ultrasound imaging and the high contrast of pure optical imaging. Analytical algorithms are usually employed to reconstruct the photoacoustic (PA) images as a result of their simple implementation. However, they provide a low accurate image. Model-based (MB) algorithms are used to improve the image quality and accuracy while a large number of transducers and data acquisition are needed. In this paper, we have combined the theory of compressed sensing (CS) with MB algorithms to reduce the number of transducer. Smoothed version of L0-norm (SL0) was proposed as the reconstruction method, and it was compared with simple iterative reconstruction (IR) and basis pursuit. The results show that S${\ell }_0$ provides a higher image quality in comparison with other methods while a low number of transducers were. Quantitative comparison demonstrates that, at the same condition, the SL0 leads to a peak-signal-to-noise ratio for about two times of the basis pursuit.

arXiv commentThis paper is presented in Photons Plus Ultrasound: Imaging and Sensing 2018 conference and published by International Society for Optics and Photonics

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Cited by in corpus (1)

• Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging