Limited view cone-beam x-ray luminescence tomography based on depth compensation and group sparsity prior

Abstract

Significance: As a promising hybrid imaging technique with x-ray excitable nanophosphors, cone-beam x-ray luminescence computed tomography (CB-XLCT) has been proposed for in-depth biological imaging applications. In situations in which the full rotation of the imaging object (or x-ray source) is inapplicable, the x-ray excitation is limited by geometry, or a lower x-ray excitation dose is mandatory, limited view CB-XLCT reconstruction would be essential. However, this will result in severe ill-posedness and poor image quality. Aim: The aim is to develop a limited view CB-XLCT imaging strategy to reduce the scanning span and a corresponding reconstruction method to achieve robust imaging performance. Approach: In this study, a group sparsity-based reconstruction method is proposed with the consideration that nanophosphors usually cluster in certain regions, such as tumors or major organs such as the liver. In addition, depth compensation (DC) is adopted to avoid the depth inconsistency caused by a limited view strategy. Results: Experiments using numerical simulations and physical phantoms with different edgeto-edge distances were carried out to illustrate the validity of the proposed method. The reconstruction results showed that the proposed method outperforms conventional methods in terms of localization accuracy, target shape, image contrast, and spatial resolution with two perpendicular projections. Conclusions: A limited view CB-XLCT imaging strategy with two perpendicular projections and a reconstruction method based on DC and group sparsity, which is essential for fast CB-XLCT imaging and for some practical imaging applications, such as imaging-guided surgery, is proposed.

Reconstruction of X-Ray Fluorescence Computed Tomography From Sparse-View Projections via L1-Norm Regularized EM Algorithm

“…Sparse-view strategy with benefits of reducing imaging dose and reducing scan time has been widely studied and applied in transmission X-ray CT and X-ray luminescence CT [37][38][39]. In this study, we explored the potential of sparse-view strategy in pencil-beam XFCT imaging.…”

Review of in vivo optical molecular imaging and sensing from x-ray excitation

“…In XLCT, anatomical prior information can be provided by either x-ray imaging or Cherenkov surface data. 44,[92][93][94] Since the measurements are taken from multiple views, XLCT in-vivo imaging is similar to CT imaging in that the image is always better Fig. 5 Cherenkov radioluminescence measurement geometries based on Linac MLC delivery and corresponding non-model-based image reconstructions.…”

“…To address this issue, a lot of effort has focused on sparse-view XLCT by taking advantage of improved measurement geometry and algorithms. 92,95,96 6 Radioluminescent Reporters: Scintillators and Fluorophores…”

Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation

“…Various methods, including the L1-TV method [21], robust elastic net-ℓ1ℓ2 method [22], and T-FISTA method [23], have been utilized to achieve this goal. With the utilization of sparsity or group sparsity as a prior, our research team has successfully shown that CB-XLCT has the capability to differentiate between two targets with an edge-to-edge distance (EED) of 0.1 cm by employing two imaging views [24]. Although the acquisition time is notably decreased by using two views, it is important to note that the imaging object undergoes inevitable rotation during the view span, which is deemed unacceptable in numerous clinical scenarios.…”

“…In the conducted physical phantom experiments, a cylinder phantom was utilized, wherein two transparent tubes containing NPs (Y 2 O 3 :Eu 3+ ) were immersed. Two algorithms previously proposed by our research group, namely T-FISTA [23] and DC-FL [24], were utilized for the purpose of comparing the sparse-view and limited-view CB-XLCT imaging techniques. Our findings demonstrated that the accurate resolution of luminescent targets is achievable from a single-view image.…”