Abstract
BackgroundFluorescent molecular tomography (FMT) aims at reconstructing the spatial map of optical and fluorescence parameters from fluence measurements. Basically, solving large-scale matrix equations is computationally expensive for image reconstruction of FMT. Despite the reconstruction quality can be improved with more sources, it may result in higher computational costs for reconstruction. This article presents a novel method in the wavelet domain with rotated sources illumination.MethodsWe use the finite element method for the computation of the forward model. The global inverse problem is solved based on wavelet in conjunction with principal component analysis. The iterative reconstruction is implemented with sources rotated in a certain angle. The original excitation light sources are used to reconstruct the image in the first iteration. Then, upon the sources are rotated by a certain angle, they are employed for the next iteration of reconstruction.ResultsSimulation results demonstrate that our method can considerably reduce the time taken for the computation of inverse problem in FMT. Furthermore, the approach proposed is also shown to largely outperform the traditional method in terms of the precision of inverse solutions.ConclusionsOur method has the capability to locate the inclusions. The proposed method can significantly speed up the reconstruction process with the high reconstruction quality.
Highlights
Fluorescent molecular tomography (FMT) aims at reconstructing the spatial map of optical and fluorescence parameters from fluence measurements
Among the optical molecular imaging, fluorescent molecular tomography (FMT) is a promising tool, which is expected to have a substantial impact on the prevention and treatment of cancer and of other lethal diseases [3]
Correia et al introduced a method with wavelet-based data and solution compression to improve the efficiency of image reconstruction for fluorescence diffuse optical tomography [13]
Summary
Diffusion model As it has been stated earlier, the forward model is used to predict the observable states at the measurement locations from knowledge of the excitation light source and spatial distribution of optical and fluorescent properties. Image reconstruction with the wavelet‐based principal component analysis We solve the inverse problem in the wavelet domain. To this aim, we take the wavelet transform on both sides of Eq (24). By means of increased number of sources, the image quality can be improved Such a strategy may result in the matrix system with larger scale and higher computational complexity. This process is repeated until some stopping criteria are satisfied This strategy is motivated by the fact that the excitation light sources from different angles can provide more information than those from some fixed angle during the iteration process, and the quality of reconstructed results can be improved. In order to compare the reconstructed object with the true one, we define an image quality metric by introducing the mean square error (MSE), given as
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