Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction

• Published in: Ultrasonics Vol. 52; no. 8; pp. 1046 - 1055
• Main Authors: Zhang, Yan, Wang, Yuanyuan, Zhang, Chen
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2012; Elsevier
• Subjects: Acoustic signal processing; Acoustics; Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Biological and medical sciences; Compressed sensing; Computer science; control theory; systems; Computer Simulation; energy; Equipment Design; Exact sciences and technology; Fundamental areas of phenomenology (including applications); image analysis; Image Processing, Computer-Assisted - methods; Image reconstruction; Imaging; in vitro studies; Investigative techniques, diagnostic techniques (general aspects); Lasers, Solid-State; Mathematical analysis; Mathematical models; Medical sciences; Miscellaneous. Technology; Pattern recognition. Digital image processing. Computational geometry; Phantoms, Imaging; Photoacoustic imaging; Physics; Polyamide-imides; Reconstruction; Signal-To-Noise Ratio; Television; Theoretical computing; Total variation; Transducers; Ultrasonic investigative techniques; Ultrasonography - methods; Total variation; Photoacoustic imaging; Image reconstruction; Compressed sensing; compressed sensing; Image processing; Updating; Modeling; Gradient descent; Model matching; Imaging; Sparse representation; Robustness; Acoustic image; Signal detection; Numerical algorithm; Gradient; Energy distribution; Digitizing; Experimental study; Real time; In vitro; Search algorithm; Inverse problem; Television; Objective function; Added value; Signal to noise ratio; Photoacoustic effect
• ISSN: 0041-624X; 1874-9968; 1874-9968
• DOI: 10.1016/j.ultras.2012.08.012

► We proposed a new image reconstruction algorithm for photoacoustic imaging. ► The total variation method is involved into the image reconstruction. ► The reconstruction speed is enhanced with a equivalent reconstruction quality as existed methods. ► The proposed algorithm is stable and suitable for PAI under sparse-view condition. In photoacoustic imaging (PAI), reconstruction from sparse-view sampling data is a remaining challenge in the cases of fast or real-time imaging. In this paper, we present our study on a total variation based gradient descent (TV-GD) algorithm for sparse-view PAI reconstruction. This algorithm involves the total variation (TV) method in compressed sensing (CS) theory. The objective function of the algorithm is modified by adding the TV value of the reconstructed image. With this modification, the reconstructed image could be closer to the real optical energy distribution map. Additionally in the proposed algorithm, the photoacoustic data is processed and the image is updated individually at each detection point. In this way, the calculation with large matrix can be avoided and a more frequent image update can be obtained. Through the numerical simulations, the proposed algorithm is verified and compared with other reconstruction algorithms which have been widely used in PAI. The peak signal-to-noise ratio (PSNR) of the image reconstructed by this algorithm is higher than those by the other algorithms. Additionally, the convergence of the algorithm, the robustness to noise and the tunable parameter are further discussed. The TV-based algorithm is also implemented in the in vitro experiment. The better performance of the proposed method is revealed in the experiments results. From the results, it is seen that the TV-GD algorithm may be a practical and efficient algorithm for sparse-view PAI reconstruction.