Compressive Sensing Based Algorithms for Limited-View PAT Image Reconstruction

Limited-view sensor arrangement is a major concern in medical imaging as it limits the data that the sensor could acquire. However, this limitation, signal sparsity, can be exploited using compressive sensing (CS) techniques to reconstruct high-resolution images. The objective of this research paper is to develop CS-based algorithms for reconstructing images in limited-view photoacoustic tomography. Various CS reconstruction algorithms and sensor arrangements were assessed to identify the optimal approach for reconstructing images from limited-view sensor data. The results show that the split Bregman total variation (SBTV)-l
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CS algorithm is the most efficient for all sensor arrangements. The study also reveals that the convex sensor array yields the best results among all sensor arrangements. Additionally, the implementation of SBTV-l
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using Cholesky factorization requires less computation time and is 10 to 15 times faster than the direct implementation.