Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

Abstract

Abstract. Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and illconditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. In this paper, a new approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach's feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method. We have compared the proposed kernel method with the soft prior method thoroughly and found that the kernel method without target segmentation is able to achieve similar anatomical guided results as the soft prior method.

Intercomparison of MR-Informed Methods for PET Image Reconstruction

“…To discourage overly piecewise constant images, Gaussian smoothed random structures were incorporated into the simulated PET phantom, in accordance with Eq. (20), producing more varied tissue structure:…”

“…Therefore, in relatively uniform MR regions where the MR intensity values are similar, spatially close voxels will be selected over more disparate voxels, thereby helping recovery of PET-unique features. More generally, KEM has been applied to a range of reconstruction problems, [12][13][14][15][16][17][18][19][20][21] and is an example of a broader cohort of algorithms that reparameterize the emission image into an alternative set of basis functions. [22][23][24] In contrast to reparameterizing the reconstruction process, MR information can alternatively be included into the reconstruction process through the addition of a regularizing term in either a Bayesian maximum a posteriori (MAP) or penalized maximum likelihood (PL) framework.…”

Recent methodology advances in fluorescence molecular tomography

“…A small signal disturbance may lead to a large reconstruction error. Therefore, researchers apply regularization techniques to FMT reconstruction to constrain the reconstruction process and reduce morbidity [8,9,28,30,63,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115]. The main principle of regularization is as follows:…”

Optical-CT Dual-Modality Mapping Base on DRR Registration

“…Combining FMT with computerized tomography (CT), both functional signals and anatomical information can be obtained. However, when FMT/CT dual-modality imaging technology is used to calculate internal bioluminescent source's size and location, it is necessary to establish a mapping between two-dimensional (2D) optical images and three-dimensional (3D) CT data in order to reconstruct 3D energy distribution on surface of the imaging object [6,7].…”