Tianshuai Liu

Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography

Cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed as a new molecular imaging modality recently. It can obtain both anatomical and functional tomographic images of an object efficiently, with the excitation of nanophosphors in vivo or in vitro by cone-beam X-rays. However, the ill-posedness of the CB-XLCT inverse problem degrades the image quality and makes it difficult to resolve adjacent luminescent targets with different concentrations, which is essential in the monitoring of nanoparticle metabolism and drug delivery. To address this problem, a multi-voltage excitation imaging scheme combined with principal component analysis is proposed in this study. Imaging experiments performed on physical phantoms by a custom-made CB-XLCT system demonstrate that two adjacent targets, with different concentrations and an edge-to-edge distance of 0 mm, can be effectively resolved.

Low-dose CT reconstruction with MRF prior predicted from patch samples of normal-dose CT database

Markov random field (MRF) model-based penalty is widely used in statistical iterative reconstruction (SIR) of low dose CT (LDCT) reconstruction for noise suppression and edge-preserving. In this strategy, normal dose CT scans are usually used as a priori information to further improve the LDCT quality. However, repeated CT scans are needed and registration or segmentation is usually applied first when misalignment between the low-dose and normal-dose scans exists. The study aims to propose a new MRF prior model of SIR based on the NDCT database without registration. In the proposed model, MRF weights are predicted using optimal similar patch samples from the NDCT database. The patch samples are determined by evaluating the similarity with Euclidean distance between patches from NDCT and the target patch of LDCT. The proposed prior term is incorporated into the SIR cost function, which is to be minimized for LDCT reconstruction. The proposed method is tested on an artificial LDCT data based on a high-dose patient data. Preliminary result has proved its potential performance in edge and structure detail preservation.

Cone-beam x-ray luminescence computed tomography reconstruction from single-view based on total variance

As an emerging hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed based on the development of X-ray excitable nanoparticles. Fast three-dimensional (3-D) CB-XLCT imaging has attracted significant attention for the application of XLCT in fast dynamic imaging study. Currently, due to the short data collection time, single-view CB-XLCT imaging achieves fast resolving the three-dimensional (3-D) distribution of X-ray-excitable nanoparticles. However, owing to only one angle projection data is used in the reconstruction, the single-view CB-XLCT inverse problem is inherently ill-conditioned, which makes image reconstruction highly susceptible to the effects of noise and numerical errors. To solve the ill-posed inverse problem, using the sparseness of the X-ray-excitable nanoparticles distribution as the prior, a new reconstruction approach based on total variance is proposed in this study. To evaluate the performance of the proposed approach, a phantom experiment was performed based on a CB-XLCT imaging system. The experiments indicate that the reconstruction from single-view XCLT can provide satisfactory results based on the proposed approach. In conclusion, with the reconstruction approach based on total variance, we implement a fast XLCT reconstruction of high quality with only one angle projection data used, which would be helpful for fast dynamic imaging study. In future, we will focus on how to applying the proposed TV-based reconstruction method and CB-XLCT imaging system to image fast biological distributions of the X-ray excitable nanophosphors in vivo.

Low dose CT reconstruction with nonlocal means-based prior predicted from normal-dose CT database

The penalized weighted least-squares (PWLS) image reconstruction with the widely used edge-preserving nonlocal means (NLM) penalty has shown the potential to significantly improve the image quality for low dose CT (LDCT). Considering the nonlocal weights have significant effects for the smoothness and resolution of the reconstruction, much effort has been made to improve their accuracy. A high quality image of normal dose with less noise and artifacts is sometimes used for the weight’s calculation to further improvement. However, registration should be employed first when misalignment between the low-dose and normal-dose scans cannot be ignored. It will bring an extra work and the effect of registration error on the proposed method are uncertain. The paper aims to propose a new NLM prior model based on normal-dose CT (NDCT) without registration, by predicting nonlocal weights with selecting most similar patch samples from FDCT database. The patch samples are determined by evaluating the similarity between patches from NDCT and the target patch of LDCT. After building up the normal dose based NLM penalty, the PWLS object function is iteratively minimized for reconstruction. Preliminary reconstruction with LDCT data has shown its potential in the structure detail preservation.

Dynamic cone beam x-ray luminescence computed tomography with principal component analysis

Cone beam X-ray luminescence computed tomography (CB-XLCT) has recently been proposed as a new molecular imaging modality for various biomedical applications. It utilizes X-ray excitable nanophosphors to produce visible or near-infrared (NIR) luminescence and combines the high sensitivity of optical imaging with the high spatial resolution of X-ray imaging. With the development of the nanophosphors and reconstruction methods, dynamic XLCT imaging, which can reflect the dynamic course of absorption, distribution, and elimination of the nanophosphors in vivo, has demonstrated its initial prospect in biological and biochemical studies. However, challenges remain in resolving nanophosphors (drug) distributions inside the imaging object due to the high light scattering and complex dynamics of nanophosphor’s delivery. Considering that target with different functions may have different kinetic behaviors, in this paper we present a method to resolve targets with different kinetics by utilizing principal component analysis (PCA). The metabolic processes of nanophosphors (Y2O3:Eu3+) of two targets were simulated and imaged using a CB-XLCT system, with two targets located at different edge-to-edge distances of 0.12 cm. Simulation and experiment studies validate the performance of the proposed algorithm. The results suggest that two adjacent targets of different kinetic behaviors can be extracted and illustrated by the proposed method, at an edge-to-edge distance of 0.12 cm.

Direct prior regularization from anatomical images for cone beam x-ray luminescence computed tomography reconstruction

Cone beam X-ray luminescence computed tomography (CB-XLCT) has recently been proposed as a new imaging modality for biological imaging application. Compared with other XLCT systems such as pencil beam XLCT and narrow beam XLCT, CB-XLCT can achieve fast imaging, where the speed is essential to small animal in vivo imaging studies. However, due to the high degree of light scattering in biological tissues, the CB-XLCT reconstruction is an ill-posed problem, which can result in poor image quality such as low spatial resolution. As a hybrid CT/optical imaging technique, the image quality is conjected to be improved substantially with the structural guidance from the anatomical images of the CT component. For that purpose, in this paper, a direct prior regularization method is proposed by introducing anatomical information directly into the CB-XLCT reconstruction. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Phantom experiments with different edge-to-edge distance (EED) were performed to realize the proposed approachs feasibility. Phantom experiments results indicate that the proposed direct regularization method can separate two luminescent targets with an EED of 0 mm. Compared with no-prior reconstruction methods such as ART and adaptive Tikhonov methods, the proposed method can significantly improve the imaging resolution of CB-XLCT.

Ultra-high FRET efficiency NaGdF4: Tb3+-Rose Bengal biocompatible nanocomposite for X-ray excited photodynamic therapy application

The limitation of light penetration depth invalidates the application of photodynamic therapy in deep-seated tumors. X-ray excited photodynamic therapy (X-PDT), which is based on X-rays excited luminescent nanoparticles (XLNP), provides a new strategy for PDT in deep tissues. However, the high X-ray dosage used and non-specific cytotoxicity of the nanoparticle-photosensitizer nanocomposite (NPs-PS) hamper in-vivo X-PDT applications. To address these problems, a simple and efficient NPs-PS nanocomposite using β-NaGdF4: Tb3+ nanoparticles and widely used PS called Rose Bengal (RB) was designed. With perfectly matched spectrum of NPs emission and RB absorption upon X-ray excitation and covalent conjugation of a large amount of RB on NP surfaces to minimize the energy transfer distance, the system demonstrated ultra-high FRET efficiency up to 99.739%, which leads to maximum production of singlet oxygen for PDT with significantly increased anti-tumor efficacy. By 2-aminoethylphosphonic acid surface modification of NPs, excellent biocompatibility was achieved even at a high concentration of 1 mg/mL. The in-vivo X-PDT efficacy was found around 90% of HepG2 tumor growth inhibition with X-ray dose of only 1.5 Gy, which shows the best anti-tumor efficacy at same X-ray dose level reported so far. The present work provides a promising platform for in-vivo X-PDT in deep tumors.

Computer simulation of low-dose CT with clinical lung image database: a preliminary study

Large samples of raw low-dose CT (LDCT) projections on lungs are needed for evaluating or designing novel and effective reconstruction algorithms suitable for lung LDCT imaging. However, there exists radiation risk when getting them from clinical CT scanning. To avoid the problem, a new strategy for producing large samples of lung LDCT projections with computer simulations is proposed in this paper. In the simulation, clinical images from the publicly available medical image database-the Lung Image Database Consortium(LIDC) and Image Database Resource Initiative (IDRI) database (LIDC/IDRI) are used as the projected object to form the noise-free sinogram. Then by adding a Poisson distributed quantum noise plus Gaussian distributed electronic noise to the projected transmission data calculated from the noise-free sinogram, different noise levels of LDCT projections are obtained. At last the LDCT projections are used for evaluating two reconstruction strategies. One is the conventional filtered back projection (FBP) algorithm and the other is FBP reconstruction from the filtered sinogram with penalized weighted least square criterion (PWLS-FBP). Images reconstructed with the LDCT simulations have shown that the PWLS-FBP algorithm performs better than the FBP algorithm in reducing streaking artifacts and preserving resolution. Preliminary results indicate that the feasibility of the proposed lung LDCT simulation strategy for helping to determine advanced reconstruction algorithms.