Psicolingüística

In order to determine whether or not an imaging task fails or succeeds, an object described by the cubic voxel expansion function should be projected onto the set of computed singular vectors in the matrix VT. The fidelity with which the image is produced indicates the capability of the imaging system to fundamentally capture the information contained in the object. However, whether or not the task succeeds or fails is necessarily subjective based on the required task of the imaging system.

The link between crosstalk and SVD is important. While it is the SVD matrices that are directly responsible for producing an image from a measured set of data, it is the

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crosstalk matrix that provides insight in addressing why an imaging task failed or succeeded. It is the expansion coefficients describing an object that must be resolved in order to accurately capture the information when performing an imaging task. The capability of the system to do this is intrinsically contained within the SVD matrices; however, it is the crosstalk matrix that actually quantifies the spatially-dependent overlap of expansion coefficients (aliasing). If an imaging system subjectively fails an imaging task, the aliasing contribution extracted from the crosstalk matrix can be assessed and the imaging system can be improved where necessary.

3.5 Conclusion

A technique was developed to acquire a data set that described an imaging

operator for a PAI system. Two scans were completed. The first contained a step size of 2 mm within an object space of 16x16x16 mm3. The second was completed at a step size of 3 mm within an object space of 30x30x30 mm3. Utilizing these data, computations to produce a voxel-based crosstalk matrix were made in order to characterize the spatially dependent sensitivity and aliasing. The lack of uniformity in the sensitivity confirmed the findings of our previous work. Singular value decomposition analysis was performed on the imaging operator to provide insight into the system’s sensitivity to objects of complex geometry. As well, insight was provided regarding the sensitivity of the imaging operator to noise. Ultimately, both techniques provided information that could be used to

understand any PA system and could provide a means to improve future iterations of the imaging hardware.

3.6 References

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9. K. P. Kostli, D. Frauchiger, J. J. Niederhauser, G. Paltauf, H. P. Weber, and M. Frenz, “Optoacoustic imaging using a three-dimensional reconstruction algorithm,” IEEE J. Sel. Top. Quantum Electron. 7(6), 918–923 (2001).

10. P. Ephrat, L. Keenliside, A. Seabrook, F. S. Prato, and J. J. L. Carson, “Three- dimensional photoacoustic imaging by sparse-array detection and iterative image reconstruction,” J. Biomed. Opt. 13(5), 054052 (2008).

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photoacoustic imaging of moving targets,” Opt. Express 16(26), 21570–21581 (2008). 15. M. Roumeliotis, P. Ephrat, J. Patrick, and J. J. L. Carson, “Development and characterization of an omnidirectional photoacoustic point source for calibration of a staring 3D photoacoustic imaging system,” Opt. Express 17(17), 15228–15238 (2009). 16. H. H. Barrett, J. L. Denny, R. F. Wagner, and K. J. Myers, “Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance,” J. Opt. Soc. Am. A 12(5), 834–852 (1995).

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17. A. A. Oraevsky, V. G. Andreev, A. A. Karabutov, and R. O. Esenaliev, Two- dimensional opto-acoustic tomography transducer array and image reconstruction algorithm,” Proc SPIE Int Soc Opt Eng 3601, 256–267 (1999).

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