TY - GEN
T1 - 3D photoacoustic imaging
AU - Carson, Jeffrey J.L.
AU - Roumeliotis, Michael
AU - Chaudhary, Govind
AU - Stodilka, Robert Z.
AU - Anastasio, Mark A.
PY - 2010
Y1 - 2010
N2 - Our group has concentrated on development of a 3D photoacoustic imaging system for biomedical imaging research. The technology employs a sparse parallel detection scheme and specialized reconstruction software to obtain 3D optical images using a single laser pulse. With the technology we have been able to capture 3D movies of translating point targets and rotating line targets. The current limitation of our 3D photoacoustic imaging approach is its inability ability to reconstruct complex objects in the field of view. This is primarily due to the relatively small number of projections used to reconstruct objects. However, in many photoacoustic imaging situations, only a few objects may be present in the field of view and these objects may have very high contrast compared to background. That is, the objects have sparse properties. Therefore, our work had two objectives: (i) to utilize mathematical tools to evaluate 3D photoacoustic imaging performance, and (ii) to test image reconstruction algorithms that prefer sparseness in the reconstructed images. Our approach was to utilize singular value decomposition techniques to study the imaging operator of the system and evaluate the complexity of objects that could potentially be reconstructed. We also compared the performance of two image reconstruction algorithms (algebraic reconstruction and l1-norm techniques) at reconstructing objects of increasing sparseness. We observed that for a 15-element detection scheme, the number of measureable singular vectors representative of the imaging operator was consistent with the demonstrated ability to reconstruct point and line targets in the field of view. We also observed that the l1-norm reconstruction technique, which is known to prefer sparseness in reconstructed images, was superior to the algebraic reconstruction technique. Based on these findings, we concluded (i) that singular value decomposition of the imaging operator provides valuable insight into the capabilities of a 3D photoacoustic imaging system, and (ii) that reconstruction algorithms which favor sparseness can significantly improve imaging performance. These methodologies should provide a means to optimize detector count and geometry for a multitude of 3D photoacoustic imaging applications.
AB - Our group has concentrated on development of a 3D photoacoustic imaging system for biomedical imaging research. The technology employs a sparse parallel detection scheme and specialized reconstruction software to obtain 3D optical images using a single laser pulse. With the technology we have been able to capture 3D movies of translating point targets and rotating line targets. The current limitation of our 3D photoacoustic imaging approach is its inability ability to reconstruct complex objects in the field of view. This is primarily due to the relatively small number of projections used to reconstruct objects. However, in many photoacoustic imaging situations, only a few objects may be present in the field of view and these objects may have very high contrast compared to background. That is, the objects have sparse properties. Therefore, our work had two objectives: (i) to utilize mathematical tools to evaluate 3D photoacoustic imaging performance, and (ii) to test image reconstruction algorithms that prefer sparseness in the reconstructed images. Our approach was to utilize singular value decomposition techniques to study the imaging operator of the system and evaluate the complexity of objects that could potentially be reconstructed. We also compared the performance of two image reconstruction algorithms (algebraic reconstruction and l1-norm techniques) at reconstructing objects of increasing sparseness. We observed that for a 15-element detection scheme, the number of measureable singular vectors representative of the imaging operator was consistent with the demonstrated ability to reconstruct point and line targets in the field of view. We also observed that the l1-norm reconstruction technique, which is known to prefer sparseness in reconstructed images, was superior to the algebraic reconstruction technique. Based on these findings, we concluded (i) that singular value decomposition of the imaging operator provides valuable insight into the capabilities of a 3D photoacoustic imaging system, and (ii) that reconstruction algorithms which favor sparseness can significantly improve imaging performance. These methodologies should provide a means to optimize detector count and geometry for a multitude of 3D photoacoustic imaging applications.
KW - 3D
KW - Photoacoustic imaging
KW - aliasing
KW - compressed sensing
KW - crosstalk
KW - iterative image reconstruction
KW - photoacoustic tomography
KW - singular value decomposition
UR - http://www.scopus.com/inward/record.url?scp=78049471549&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049471549&partnerID=8YFLogxK
U2 - 10.1117/12.872720
DO - 10.1117/12.872720
M3 - Conference contribution
AN - SCOPUS:78049471549
SN - 9780819482419
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Photonics North 2010
T2 - Photonics North 2010
Y2 - 1 June 2010 through 3 June 2010
ER -