Paper
16 July 2015 Error estimates for universal back-projection-based photoacoustic tomography
Prabodh Kumar Pandey, Naren Naik, Prabhat Munshi, Asima Pradhan
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Abstract
Photo-acoustic tomography is a hybrid imaging modality that combines the advantages of optical as well as ultrasound imaging techniques to produce images with high resolution and good contrast at high penetration depths. Choice of reconstruction algorithm as well as experimental and computational parameters plays a major role in governing the accuracy of a tomographic technique. Therefore error estimates with the variation of these parameters have extreme importance. Due to the finite support, that photo-acoustic source has, the pressure signals are not band-limited, but in practice, our detection system is. Hence the reconstructed image from ideal, noiseless band-limited forward data (for future references we will call this band-limited reconstruction) is the best approximation that we have for the unknown object. In the present study, we report the error that arises in the universal back-projection (UBP) based photo-acoustic reconstruction for planer detection geometry due to sampling and filtering of forward data (pressure signals).Computational validation of the error estimates have been carried out for synthetic phantoms. Validation with noisy forward data has also been carried out, to study the effect of noise on the error estimates derived in our work. Although here we have derived the estimates for planar detection geometry, the derivations for spherical and cylindrical geometries follow accordingly.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Prabodh Kumar Pandey, Naren Naik, Prabhat Munshi, and Asima Pradhan "Error estimates for universal back-projection-based photoacoustic tomography", Proc. SPIE 9539, Opto-Acoustic Methods and Applications in Biophotonics II, 953918 (16 July 2015); https://doi.org/10.1117/12.2183905
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Cited by 2 scholarly publications.
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KEYWORDS
Error analysis

Reconstruction algorithms

Tomography

Signal detection

Sensors

Signal to noise ratio

Detection and tracking algorithms

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