Photoacoustic tomography from weak and noisy signals by using a pulse decomposition algorithm in the time-domain

Abstract

Photoacoustic tomography is a promising and rapidly developed methodology of biomedical imaging. It confronts an increasing urgent problem to reconstruct the image from weak and noisy photoacoustic signals, owing to its high benefit in extending the imaging depth and decreasing the dose of laser exposure. Based on the time-domain characteristics of photoacoustic signals, a pulse decomposition algorithm is proposed to reconstruct a photoacoustic image from signals with low signal-to-noise ratio. In this method, a photoacoustic signal is decomposed as the weighted summation of a set of pulses in the time-domain. Images are reconstructed from the weight factors, which are directly related to the optical absorption coefficient. Both simulation and experiment are conducted to test the performance of the method. Numerical simulations show that when the signal-to-noise ratio is -4 dB, the proposed method decreases the reconstruction error to about 17%, in comparison with the conventional back-projection method. Moreover, it can produce acceptable images even when the signal-to-noise ratio is decreased to -10 dB. Experiments show that, when the laser influence level is low, the proposed method achieves a relatively clean image of a hair phantom with some well preserved pattern details. The proposed method demonstrates imaging potential of photoacoustic tomography in expanding applications.

Recent advances toward preclinical and clinical translation of photoacoustic tomography: a review

“…From the boundary PA data, various reconstruction algorithms can be used for obtaining the initial pressure rise distribution inside the tissue. [138][139][140][141][142][143][144][145] Several advancements have happened in the last couple of years in the PAT reconstruction algorithms: (a) a least-square-based algorithm for accurate reconstruction with a fewer number of transducers, 146 (b) basis pursuit deconvolution algorithm to retain the structural information accurately, 147 (c) filtered-backprojection algorithm, 148 (d) single-stage algorithm for high quality (compared to traditional two-stage algorithms) PA imaging, 149 (e) pulse decomposition algorithm in the time-domain for weak, noisy PA signal reconstruction, 150 (f) an algorithm integrating focal-line-based 3-D image reconstruction with coherent weight to improve the elevation resolution of commercial linear UST array-based PAT, 151 (g) a multiview Hilbert transformation method for full-view PAT imaging using linear UST array, 63 (h) asymmetric DAQ optimized with compressed sensing method for full-view PAT, 152 and (i) improving tangential resolution in PAT with a modified delay-and-sum algorithm. 84,85 7 Conclusions and Future Directions…”

Fast photoacoustic imaging systems using pulsed laser diodes: a review

“…Several reconstruction algorithms have been development in parallel for PA image formation and display: (a) a model-based reconstruction [95][96][97][98], (b) filtered-back-projection algorithm [99], (c) single-stage algorithm [100], (e) pulse decomposition algorithm [101], (e) focal-linebased 3-D image reconstruction algorithm [102], (e) a multi-view Hilbert transformation [103], (f) algorithm based on compressed sensing [104], and (g) simple delayand-sum algorithm [105,106]. Some of these reconstruction algorithms are computationally expensive and slow, therefore, efforts are ongoing for developing fast, efficient, real-time reconstruction methods.…”

Model-Based Optoacoustic Tomography Image Reconstruction With Non-local and Sparsity Regularizations

“…Image reconstruction is an essential step in optoacoustic imaging technology, and has a great influence on the quality of the generated images. Generally, the image reconstruction algorithms for OAT can be classified into two main categories: the analytical methods [9]- [12] and the model-based (MB) methods [13]- [16]. For the analytical methods, Lihong Wang's group first proposed a universal back-projection (UBP) algorithm for OAT image reconstruction [9].…”