Abstract
With the development of charge-coupled device (CCD) camera based non-contact fluorescence molecular tomography (FMT) imaging systems, multi projections and densely sampled fluorescent measurements used in subsequent image reconstruction can be easily obtained. However, challenges still remain in fast image reconstruction because of the large computational burden and memory requirement in the inverse problem. In this work, an accelerated image reconstruction method in FMT using principal components analysis (PCA) is presented to reduce the dimension of the inverse problem. Phantom experiments are performed to verify the feasibility of the proposed method. The results demonstrate that the proposed method can accelerate image reconstruction in FMT almost without quality degradation.
Highlights
Over the past decade, fluorescence molecular tomography (FMT) has been developed into a powerful in vivo imaging method with the advantage of non invasive and non ionizing imaging [1]
As each measurement corresponds to a source-detector map which is a row vector of the weight matrix, a large measured fluorescent data set leads to a large weight matrix which results in an inherently large computational burden in the inverse problem of FMT
An accelerated image reconstruction method in FMT based on reducing the dimension of the weight matrix using principal components analysis (PCA) is presented
Summary
Fluorescence molecular tomography (FMT) has been developed into a powerful in vivo imaging method with the advantage of non invasive and non ionizing imaging [1]. The inverse problem of FMT is to solve an ill-posed linear equation of which the weight matrix maps unknown distributions of the fluorescent markers inside small animals onto the fluorescent measurements over the surface. As each measurement corresponds to a source-detector map which is a row vector of the weight matrix, a large measured fluorescent data set leads to a large weight matrix which results in an inherently large computational burden in the inverse problem of FMT. A matrix-free method [17, 18] is proposed to avoid explicit calculation and storage of the large weight matrix by replacing it using matrix-vector products It can effectively reduce the computational cost and memory requirements for the FMT reconstruction. A comparison is made between the proposed dimension reduction method based on PCA (PCA method) and the data compression method based on wavelet transformation (wavelet transformation method) [14]
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