Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization

Fluorescence molecular tomography (FMT) as a noninvasive imaging modality has been widely used for biomedical preclinical applications. However, FMT reconstruction suffers from severe ill-posedness, especially when a limited number of projections are used. In order to improve the quality of FMT reconstruction results, a discrete cosine transform (DCT) based reweighted L1-norm regularization algorithm is proposed. In each iteration of the reconstruction process, different reweighted regularization parameters are adaptively assigned according to the values of DCT coefficients to suppress the reconstruction noise. In addition, the permission region of the reconstructed fluorophores is adaptively constructed to increase the convergence speed. In order to evaluate the performance of the proposed algorithm, physical phantom and in vivo mouse experiments with a limited number of projections are carried out. For comparison, different L1-norm regularization strategies are employed. By quantifying the signal-to-noise ratio (SNR) of the reconstruction results in the phantom and in vivo mouse experiments with four projections, the proposed DCT-based reweighted L1-norm regularization shows higher SNR than other L1-norm regularizations employed in this work. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE

Fast reconstruction of fluorescence molecular tomography via a permissible region extraction strategy

In order to obtain precise reconstruction results in fluorescence molecular tomography (FMT), large-scale matrix equations would be solved in the inverse problem generally. Thus, much time and memory needs to be consumed. In this paper, a permissible region extraction strategy is proposed to solve this problem. First, a preliminary result is rapidly reconstructed using the weight matrix compressed by principal component analysis or uniform sampling. And then the reconstructed target area in this preliminary result is considered as the a priori permissible region to guide the final reconstruction. Phantom experiments with double fluorescent targets are performed to test the performance of the strategy. The results illustrate that the proposed strategy can significantly accelerate the image reconstruction in FMT almost without quality degradation. (C) 2014 Optical Society of Americ

Iterative Correction Scheme Based on Discrete Cosine Transform and L1 Regularization for Fluorescence Molecular Tomography With Background Fluorescence

Goal: High-intensity background fluorescence is generally encountered in fluorescence molecular tomography (FMT), because of the accumulation of fluorescent probes in nontarget tissues or the existence of autofluorescence in biological tissues. The reconstruction results are affected or even distorted by the background fluorescence, especially when the distribution of fluorescent targets is relatively sparse. The purpose of this paper is to reduce the negative effect of background fluorescence on FMT reconstruction. Methods: After each iteration of the Tikhonov regularization algorithm, 3-D discrete cosine transform is adopted to filter the intermediate results. And then, a sparsity constraint step based on L1 regularization is applied to restrain the energy of the objective function. Results: Phantom experiments with different fluorescence intensities of homogeneous and heterogeneous background are carried out to validate the performance of the proposed scheme. The results show that the reconstruction quality can be improved with the proposed iterative correction scheme. Conclusion and Significance: The influence of background fluorescence in FMT can be reduced effectively because of the filtering of the intermediate results, the detail preservation, and noise suppression of L1 regularization

Spectral selective fluorescence molecular imaging with volume holographic imaging system

A compact volume holographic imaging (VHI) method that can detect fluorescence objects located in diffusive medium in spectral selective imaging manner is presented. The enlargement of lateral field of view of the VHI system is realized by using broadband illumination and demagnification optics. Each target spectrum of fluorescence emitting from a diffusive medium is probed by tuning the inclination angle of the transmission volume holographic grating (VHG). With the use of the single transmission VHG, fluorescence images with different spectrum are obtained sequentially and precise three-dimensional (3D) information of deep fluorescent objects located in a diffusive medium can be reconstructed from these images. The results of phantom experiments demonstrate that two fluorescent objects with a sub-millimeter distance can be resolved by spectral selective imaging

Compressed sensing MR image reconstruction via a deep frequency-division network

International audienceCompressed sensing MRI (CS-MRI) is considered as a powerful technique for decreasing the scan time of MRI while ensuring the image quality. However, state of the art reconstruction algorithms are still subjected to two challenges including terrible parameters tuning and image details loss resulted from over-smoothing. In this paper, we propose a deep frequency-division network (DFDN) to face these two image reconstruction issues. The proposed DFDN approach applies a deep iterative reconstruction network (DIRN) to replace the regularization terms and the corresponding parameters by a stacked convolution neural network (CNN). And then multiple DIRN blocks are cascaded continuously as one deeper neural network. Data consistency (DC) layer is incorporated after each DIRN block to correct the k-space data of intermediate results. Image content loss is computed after each DC layer and frequency-division loss is gained by weighting the high frequency loss and low frequency loss after each DIRN block. The combination of image content loss and frequency-division loss is considered as the total loss for constraining the network training procedure. Validations of the proposed method have been performed on two brain datasets. Visual results and quantitative evaluations show that the proposed DFDN algorithm has better performance in sparse MRI reconstruction than other comparative methods

Spectral selective fluorescence molecular imaging with volume holographic imaging system

A compact volume holographic imaging (VHI) method that can detect fluorescence objects located in diffusive medium in spectral selective imaging manner is presented. The enlargement of lateral field of view of the VHI system is realized by using broadband illumination and demagnification optics. Each target spectrum of fluorescence emitting from a diffusive medium is probed by tuning the inclination angle of the transmission volume holographic grating (VHG). With the use of the single transmission VHG, fluorescence images with different spectrum are obtained sequentially and precise three-dimensional (3D) information of deep fluorescent objects located in a diffusive medium can be reconstructed from these images. The results of phantom experiments demonstrate that two fluorescent objects with a sub-millimeter distance can be resolved by spectral selective imaging