Junyan Rong

Prior image based anisotropic edge guided TV minimization for few-view CT reconstruction

To improve the spatial resolution of the image reconstructed by the conventional total variation (TV) algorithm, we propose a prior image based anisotropic edge guided TV minimization (PIEGTV) algorithm for few-view CT reconstruction. In this study, an anisotropic edge of the prior image is detected using the proposed edge detector. Then the weights of the TV discretization term for the to-be-estimated image are updated by the anisotropic edge information. To solve the minimization problem of the PIEGTV reconstruction, a similar TV-based minimization implementation is developed to deal with the raw data fidelity and other constraints. The results with computer simulations for the Shepp-Logan phantom and experimental data from a physical phantom demonstrate that the proposed algorithm can yield images with noticeable gains in edge preserving and shape preserving for small structures, compared to the conventional and a few modified TV algorithms.

CT reconstruction from few-views by edge guided TV minimization

Based on compressed sensing (CS) recovery theory, total variation (TV) minimization has been successfully used in computed tomography (CT) reconstruction for sparse or limited angle data. When the number of projection views is much smaller or noise exists in the projection data, a conventional TV minimization algorithm often suffers from the decrease of spatial resolution especially in the edge area. Considering that the edge is an important index for image quality and it reflects the sparsity of an image to some extent, in this paper, we propose an edge guided TV (EGTV) minimization reconstruction algorithm for better edge preservation in CT reconstruction. EGTV with both isotropic and anisotropic weights of the TV discretization term is derived by importing edge information into TV calculating process. When an edge of the to-be-estimated image is detected, the associated weight of the TV additive element is adjusted. To solve the EGTV minimization reconstruction problem, a similar TV-based minimization implementation was developed to deal with the raw data fidelity and other constraints. The results with computer simulation reveals that EGTV minimization algorithm can improve the image quality and preserve the edge characteristics compared to conventional TV minimization algorithm.

Adaptive non‐local means on local principle neighborhood for noise/artifacts reduction in low‐dose CT images

Purpose Low‐dose CT (LDCT) technique can reduce the x‐ray radiation exposure to patients at the cost of degraded images with severe noise and artifacts. Non‐local means (NLM) filtering has shown its potential in improving LDCT image quality. However, currently most NLM‐based approaches employ a weighted average operation directly on all neighbor pixels with a fixed filtering parameter throughout the NLM filtering process, ignoring the non‐stationary noise nature of LDCT images. In this paper, an adaptive NLM filtering scheme on local principle neighborhoods (PC‐NLM) is proposed for structure‐preserving noise/artifacts reduction in LDCT images. Methods Instead of using neighboring patches directly, in the PC‐NLM scheme, the principle component analysis (PCA) is first applied on local neighboring patches of the target patch to decompose the local patches into uncorrelated principle components (PCs), then a NLM filtering is used to regularize each PC of the target patch and finally the regularized components is transformed to get the target patch in image domain. Especially, in the NLM scheme, the filtering parameter is estimated adaptively from local noise level of the neighborhood as well as the signal‐to‐noise ratio (SNR) of the corresponding PC, which guarantees a “weaker” NLM filtering on PCs with higher SNR and a “stronger” filtering on PCs with lower SNR. The PC‐NLM procedure is iteratively performed several times for better removal of the noise and artifacts, and an adaptive iteration strategy is developed to reduce the computational load by determining whether a patch should be processed or not in next round of the PC‐NLM filtering. Results The effectiveness of the presented PC‐NLM algorithm is validated by experimental phantom studies and clinical studies. The results show that it can achieve promising gain over some state‐of‐the‐art methods in terms of artifact suppression and structure preservation. Conclusions With the use of PCA on local neighborhoods to extract principal structural components, as well as adaptive NLM filtering on PCs of the target patch using filtering parameter estimated based on the local noise level and corresponding SNR, the proposed PC‐NLM method shows its efficacy in preserving fine anatomical structures and suppressing noise/artifacts in LDCT images.

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.

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.