Photoacoustic tomography (PAT) is a hybrid imaging modality where we intend to reconstruct optical properties of heterogeneous media from measured ultrasound signals generated by the photoacoustic effect. In recent years, there have been considerable interests in using PAT to image two-photon absorption, in addition to the usual single-photon absorption, inside diffusive media. We present a mathematical model for quantitative image reconstruction in two-photon photoacoustic tomography (TP-PAT). We propose a computational strategy for the reconstruction of the optical absorption coefficients and provide some numerical evidences based on synthetic photoacoustic acoustic data to demonstrate the feasibility of quantitative reconstructions in TP-PAT.
It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data (e.g., multispectral or multifrequency methods), or reduce the number of unknowns (e.g., using prior structural information from other imaging modalities). We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.
Optical tomography of small tissue volumes, as they are encountered in rodent or finger imaging, holds great promise as the signal-to-noise levels are usually high and the spatial resolutions are much better than that of large imaging domains. To accurately model the light propagation in these small domains, radiative transport equations have to be solved directly. In the study at hand, we use the frequency-domain equation of radiative transfer (ERT) to perform a sensitivity study. We determine optimal source-modulation frequencies for which amplitude and phase of the measured signal. These results will be useful in designed experiments and optical tomographic imaging system.
It is well know that the inverse problem in optical tomography is highly ill-posed. The image reconstruction
process is often unstable and non-unique, because the number of the boundary measurements data is far fewer
than the number of the unknown parameters (optical properties) to be reconstructed. To overcome this problem
one can either increase the number of measurement data (e.g. multi-spectral or multi-frequency methods), or
reduce the number of unknows (e.g. using prior structural information from other imaging modalities). In
this paper, we introduce a novel approach for reducing the unknown parameters in the reconstruction process.
The discrete cosine transform (DCT), which has long been used in image compression, is here employed to
parameterize the reconstructed image. In general, only a few DCT coefficient are needed to describe the main
features in an image, and the number of unknowns in the image reconstruction process can be drastically
reduced. Numerical as well as experimental examples are shown that illustrate the performance of the new
code.
We have developed a model-based iterative image reconstruction scheme based on the equation of radiative transfer in the frequency domain for the applications in small animal optical tomographic imaging. To test the utility of such a code in small animal imaging we have furthermore developed a numerical phantom of a mouse. In simulation studies using this and other phantoms, we found that to make truly use of phase information in the reconstruction process modulation frequencies well above 100 MHz are necessary. Only at these higher frequencies the phase shifts introduced by the lesions of interest are large enough to be measured. For smaller frequencies no substantial improvements over steady-state systems are achieved in small geometries typical for small animal imaging.
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