Zhaoying Bian

Iterative Reconstruction for X-Ray Computed Tomography using Prior-Image Induced Nonlocal Regularization

Repeated X-ray computed tomography (CT) scans are often required in several specific applications such as perfusion imaging, image-guided biopsy needle, image-guided intervention, and radiotherapy with noticeable benefits. However, the associated cumulative radiation dose significantly increases as comparison with that used in the conventional CT scan, which has raised major concerns in patients. In this study, to realize radiation dose reduction by reducing the X-ray tube current and exposure time (mAs) in repeated CT scans, we propose a prior-image induced nonlocal (PINL) regularization for statistical iterative reconstruction via the penalized weighted least-squares (PWLS) criteria, which we refer to as “PWLS-PINL”. Specifically, the PINL regularization utilizes the redundant information in the prior image and the weighted least-squares term considers a data-dependent variance estimation, aiming to improve current low-dose image quality. Subsequently, a modified iterative successive overrelaxation algorithm is adopted to optimize the associative objective function. Experimental results on both phantom and patient data show that the present PWLS-PINL method can achieve promising gains over the other existing methods in terms of the noise reduction, low-contrast object detection, and edge detail preservation.

Sparse angular CT reconstruction using non-local means based iterative-correction POCS

In divergent-beam computed tomography (CT), sparse angular sampling frequently leads to conspicuous streak artifacts. In this paper, we propose a novel non-local means (NL-means) based iterative-correction projection onto convex sets (POCS) algorithm, named as NLMIC-POCS, for effective and robust sparse angular CT reconstruction. The motivation for using NLMIC-POCS is that NL-means filtered image can produce an acceptable priori solution for sequential POCS iterative reconstruction. The NLMIC-POCS algorithm has been tested on simulated and real phantom data. The experimental results show that the presented NLMIC-POCS algorithm can significantly improve the image quality of the sparse angular CT reconstruction in suppressing streak artifacts and preserving the edges of the image.

SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography

Radiation dose has raised significant concerns to patients and operators in modern X-ray computed tomography (CT) examinations. A simple and cost-effective means to perform a low-dose CT scan is to lower the milliampere-seconds (mAs) as low as reasonably achievable in data acquisition. However, the associated image quality with lower-mAs scans (or low-dose scans) will be unavoidably degraded due to the excessive data noise, if no adequate noise control is applied during image reconstruction. For image reconstruction with low-dose scans, sinogram restoration algorithms based on modeling the noise properties of measurement can produce an image with noise-induced artifact suppression, but they often suffer noticeable resolution loss. As an alternative technique, the noise-reduction algorithms via edge-preserving image filtering can yield an image without noticeable resolution loss, but they often do not completely eliminate the noise-induced artifacts. With above observations, in this paper, we present a sinogram restoration induced non-local means (SR-NLM) image filtering algorithm to retain the CT image quality by fully considering the advantages of the sinogram restoration and image filtering algorithms in low-dose image reconstruction. Extensive experimental results show that the present SR-NLM algorithm outperforms the existing methods in terms of cross profile, noise reduction, contrast-to-ratio measure, noise-resolution tradeoff and receiver operating characteristic (ROC) curves.

Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior.

X-ray computed tomography (CT) iterative image reconstruction from sparse-view projection data has been an important research topic for radiation reduction in clinic. In this paper, to relieve the requirement of misalignment reduction operation of the prior image constrained compressed sensing (PICCS) approach introduced by Chen et al, we present an iterative image reconstruction approach for sparse-view CT using a normal-dose image induced total variation (ndiTV) prior. The associative objective function of the present approach is constructed under the penalized weighed least-square (PWLS) criteria, which contains two terms, i.e., the weighted least-square (WLS) fidelity and the ndiTV prior, and is referred to as “PWLS-ndiTV”. Specifically, the WLS fidelity term is built based on an accurate relationship between the variance and mean of projection data in the presence of electronic background noise. The ndiTV prior term is designed to reduce the influence of the misalignment between the desired- and prior- image by using a normal-dose image induced non-local means (ndiNLM) filter. Subsequently, a modified steepest descent algorithm is adopted to minimize the associative objective function. Experimental results on two different digital phantoms and an anthropomorphic torso phantom show that the present PWLS-ndiTV approach for sparse-view CT image reconstruction can achieve noticeable gains over the existing similar approaches in terms of noise reduction, resolution-noise tradeoff, and low-contrast object detection.

Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter

Goal: Spectral computed tomography (SCT) images reconstructed by an analytical approach often suffer from a poor signal-to-noise ratio and strong streak artifacts when sufficient photon counts are not available in SCT imaging. In reducing noise-induced artifacts in SCT images, in this study, we propose an average image-induced nonlocal means (aviNLM) filter for each energy-specific image restoration. Methods: The present aviNLM algorithm exploits redundant information in the whole energy domain. Specifically, the proposed aviNLM algorithm yields the restored results by performing a nonlocal weighted average operation on the noisy energy-specific images with the nonlocal weight matrix between the target and prior images, in which the prior image is generated from all of the images reconstructed in each energy bin. Results: Qualitative and quantitative studies are conducted to evaluate the aviNLM filter by using the data of digital phantom, physical phantom, and clinical patient data acquired from the energy-resolved and -integrated detectors, respectively. Experimental results show that the present aviNLM filter can achieve promising results for SCT image restoration in terms of noise-induced artifact suppression, cross profile, and contrast-to-noise ratio and material decomposition assessment. Conclusion and Significance: The present aviNLM algorithm has useful potential for radiation dose reduction by lowering the mAs in SCT imaging, and it may be useful for some other clinical applications, such as in myocardial perfusion imaging and radiotherapy.

A Simple Low-Dose X-Ray CT Simulation From High-Dose Scan

Low-dose X-ray computed tomography (CT) simulation from a high-dose scan is required in optimizing radiation dose to patients. In this paper, we propose a simple low-dose CT simulation strategy in the sinogram domain using the raw data from high-dose scan. Specially, a relationship between the incident fluxes of low- and high-dose scans is first determined according to the repeated projection measurements and analysis. Second, the incident flux level of the simulated low-dose scan is generated by properly scaling the incident flux level of the high-dose scan via the determined relationship in the first step. Third, the low-dose CT transmission data by energy integrating detection is simulated by adding a statistically independent Poisson noise distribution plus a statistically independent Gaussian noise distribution. Finally, a filtered back-projection (FBP) algorithm is implemented to reconstruct the resultant low-dose CT images. The present low-dose simulation strategy is verified on the simulations and real scans by comparing it with the existing low-dose CT simulation tool. Experimental results demonstrated that the present low-dose CT simulation strategy can generate accurate low-dose CT sinogram data from high-dose scans in terms of qualitative and quantitative measurements.

Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations

Cerebral perfusion x-ray computed tomography (PCT) is an important functional imaging modality for evaluating cerebrovascular diseases and has been widely used in clinics over the past decades. However, due to the protocol of PCT imaging with repeated dynamic sequential scans, the associative radiation dose unavoidably increases as compared with that used in conventional CT examinations. Minimizing the radiation exposure in PCT examination is a major task in the CT field. In this paper, considering the rich similarity redundancy information among enhanced sequential PCT images, we propose a low-dose PCT image restoration model by incorporating the low-rank and sparse matrix characteristic of sequential PCT images. Specifically, the sequential PCT images were first stacked into a matrix (i.e., low-rank matrix), and then a non-convex spectral norm/regularization and a spatio-temporal total variation norm/regularization were then built on the low-rank matrix to describe the low rank and sparsity of the sequential PCT images, respectively. Subsequently, an improved split Bregman method was adopted to minimize the associative objective function with a reasonable convergence rate. Both qualitative and quantitative studies were conducted using a digital phantom and clinical cerebral PCT datasets to evaluate the present method. Experimental results show that the presented method can achieve images with several noticeable advantages over the existing methods in terms of noise reduction and universal quality index. More importantly, the present method can produce more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps.

Dynamic Positron Emission Tomography Image Restoration via a Kinetics-Induced Bilateral Filter

Dynamic positron emission tomography (PET) imaging is a powerful tool that provides useful quantitative information on physiological and biochemical processes. However, low signal-to-noise ratio in short dynamic frames makes accurate kinetic parameter estimation from noisy voxel-wise time activity curves (TAC) a challenging task. To address this problem, several spatial filters have been investigated to reduce the noise of each frame with noticeable gains. These filters include the Gaussian filter, bilateral filter, and wavelet-based filter. These filters usually consider only the local properties of each frame without exploring potential kinetic information from entire frames. Thus, in this work, to improve PET parametric imaging accuracy, we present a kinetics-induced bilateral filter (KIBF) to reduce the noise of dynamic image frames by incorporating the similarity between the voxel-wise TACs using the framework of bilateral filter. The aim of the proposed KIBF algorithm is to reduce the noise in homogeneous areas while preserving the distinct kinetics of regions of interest. Experimental results on digital brain phantom and in vivo rat study with typical 18F-FDG kinetics have shown that the present KIBF algorithm can achieve notable gains over other existing algorithms in terms of quantitative accuracy measures and visual inspection.

Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization

PURPOSE Cerebral perfusion computed tomography (PCT) imaging as an accurate and fast acute ischemic stroke examination has been widely used in clinic. Meanwhile, a major drawback of PCT imaging is the high radiation dose due to its dynamic scan protocol. The purpose of this work is to develop a robust perfusion deconvolution approach via structure tensor total variation (STV) regularization (PD-STV) for estimating an accurate residue function in PCT imaging with the low-milliampere-seconds (low-mAs) data acquisition. METHODS Besides modeling the spatio-temporal structure information of PCT data, the STV regularization of the present PD-STV approach can utilize the higher order derivatives of the residue function to enhance denoising performance. To minimize the objective function, the authors propose an effective iterative algorithm with a shrinkage/thresholding scheme. A simulation study on a digital brain perfusion phantom and a clinical study on an old infarction patient were conducted to validate and evaluate the performance of the present PD-STV approach. RESULTS In the digital phantom study, visual inspection and quantitative metrics (i.e., the normalized mean square error, the peak signal-to-noise ratio, and the universal quality index) assessments demonstrated that the PD-STV approach outperformed other existing approaches in terms of the performance of noise-induced artifacts reduction and accurate perfusion hemodynamic maps (PHM) estimation. In the patient data study, the present PD-STV approach could yield accurate PHM estimation with several noticeable gains over other existing approaches in terms of visual inspection and correlation analysis. CONCLUSIONS This study demonstrated the feasibility and efficacy of the present PD-STV approach in utilizing STV regularization to improve the accuracy of residue function estimation of cerebral PCT imaging in the case of low-mAs.

High quality 4D cone-beam CT reconstruction using motion-compensated total variation regularization

Four dimensional cone-beam computed tomography (4D-CBCT) has great potential clinical value because of its ability to describe tumor and organ motion. But the challenge in 4D-CBCT reconstruction is the limited number of projections at each phase, which result in a reconstruction full of noise and streak artifacts with the conventional analytical algorithms. To address this problem, in this paper, we propose a motion compensated total variation regularization approach which tries to fully explore the temporal coherence of the spatial structures among the 4D-CBCT phases. In this work, we additionally conduct motion estimation/motion compensation (ME/MC) on the 4D-CBCT volume by using inter-phase deformation vector fields (DVFs). The motion compensated 4D-CBCT volume is then viewed as a pseudo-static sequence, of which the regularization function was imposed on. The regularization used in this work is the 3D spatial total variation minimization combined with 1D temporal total variation minimization. We subsequently construct a cost function for a reconstruction pass, and minimize this cost function using a variable splitting algorithm. Simulation and real patient data were used to evaluate the proposed algorithm. Results show that the introduction of additional temporal correlation along the phase direction can improve the 4D-CBCT image quality.