TY - GEN
T1 - Comparison of reconstruction algorithms for sparse-array detection photoacoustic tomography
AU - Chaudhary, G.
AU - Roumeliotis, M.
AU - Carson, J. J.L.
AU - Anastasio, M. A.
N1 - Funding Information:
This work was supported by the Ministry of Health and Medical Education of the Islamic Republic of Iran and the National Institute of Health Research. The funding body had direct role in neither step of study design, data collection, analysis, and interpretation, or writing the manuscript.
PY - 2010/5/3
Y1 - 2010/5/3
N2 - A photoacoustic tomography (PAT) imaging system based on a sparse 2D array of detector elements and an iterative image reconstruction algorithm has been proposed, which opens the possibility for high frame-rate 3D PAT. The efficacy of this PAT implementation is highly influenced by the choice of the reconstruction algorithm. In recent years, a variety of new reconstruction algorithms have been proposed for medical image reconstruction that have been motivated by the emerging theory of compressed sensing. These algorithms have the potential to accurately reconstruct sparse objects from highly incomplete measurement data, and therefore may be highly suited for sparse array PAT. In this context, a sparse object is one that is described by a relatively small number of voxel elements, such as typically arises in blood vessel imaging. In this work, we investigate the use of a gradient projection-based iterative reconstruction algorithm for image reconstruction in sparse-array PAT. The algorithm seeks to minimize an 1-norm penalized least-squares cost function. By use of computer-simulation studies, we demonstrate that the gradient projection algorithm may further improve the efficacy of sparse-array PAT.
AB - A photoacoustic tomography (PAT) imaging system based on a sparse 2D array of detector elements and an iterative image reconstruction algorithm has been proposed, which opens the possibility for high frame-rate 3D PAT. The efficacy of this PAT implementation is highly influenced by the choice of the reconstruction algorithm. In recent years, a variety of new reconstruction algorithms have been proposed for medical image reconstruction that have been motivated by the emerging theory of compressed sensing. These algorithms have the potential to accurately reconstruct sparse objects from highly incomplete measurement data, and therefore may be highly suited for sparse array PAT. In this context, a sparse object is one that is described by a relatively small number of voxel elements, such as typically arises in blood vessel imaging. In this work, we investigate the use of a gradient projection-based iterative reconstruction algorithm for image reconstruction in sparse-array PAT. The algorithm seeks to minimize an 1-norm penalized least-squares cost function. By use of computer-simulation studies, we demonstrate that the gradient projection algorithm may further improve the efficacy of sparse-array PAT.
KW - Image reconstruction
KW - Photoacoustic tomography
KW - Sparse-array photoacoustic tomography
UR - http://www.scopus.com/inward/record.url?scp=77951600279&partnerID=8YFLogxK
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U2 - 10.1117/12.842607
DO - 10.1117/12.842607
M3 - Conference contribution
AN - SCOPUS:77951600279
SN - 9780819479600
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Photons Plus Ultrasound
T2 - Photons Plus Ultrasound: Imaging and Sensing 2010
Y2 - 24 January 2010 through 26 January 2010
ER -