In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
DAS
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
UBP
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
pDAS
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
DMAS
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
MV
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
EIGMV
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
SLSC
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
GSC
Evaluation of 10 Current Image Reconstruction Algorithms for Linear Array Photoacoustic Imaging.Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged.  Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them.  In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data.  Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity.  We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications. 
TR
In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. 
FD