To avoid severe limited-view artifacts in reconstructed CT images, current multi-row detector CT (MDCT) scanners with a single x-ray source-detector assembly need to limit table translation speeds such that the pitch p (viz., normalized table translation distance per gantry rotation) is lower than 1.5. When , it remains an open question whether one can reconstruct clinically useful helical CT images without severe artifacts. In this work, we show that a synergistic use of advanced techniques in conventional helical filtered backprojection, compressed sensing, and more recent deep learning methods can be properly integrated to enable accurate reconstruction up to p=4 without significant artifacts for single source MDCT scans.

Dual-energy X-ray CT (DECT) has the potential to improve contrast and reduce artifacts as compared to traditional CT. Moreover, by applying model-based iterative reconstruction (MBIR) to dual-energy data, one might also expect to reduce noise and improve resolution. However, the direct implementation of dual-energy MBIR requires the use of a nonlinear forward model, which increases both complexity and computation. Alternatively, simplified forward models have been used which treat the material-decomposed channels separately, but these approaches do not fully account for the statistical dependencies in the channels. In this paper, we present a method for joint dual-energy MBIR (JDE-MBIR), which simplifies the forward model while still accounting for the complete statistical dependency in the material-decomposed sinogram components. The JDE-MBIR approach works by using a quadratic approximation to the polychromatic log-likelihood and a simple but exact nonnegativity constraint in the image domain. We demonstrate that our method is particularly effective when the DECT system uses fast kVp switching, since in this case the model accounts for the inaccuracy of interpolated sinogram entries. Both phantom and clinical results show that the proposed model produces images that compare favorably in quality to previous decomposition-based methods, including FBP and other statistical iterative approaches.

Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.

Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

A basic assumption in the classic computed tomography (CT) theory is that an object remains stationary in an entire scan. In biomedical CT/micro-CT, this assumption is often violated. To produce high-resolution images, such as for our recently proposed clinical micro-CT (CMCT) prototype, it is desirable to develop a precise motion estimation and image reconstruction scheme. In this paper, we first extend the Helgason-Ludwig consistency condition (HLCC) from parallel-beam to fan-beam geometry when an object is subject to a translation. Then, we propose a novel method to estimate the motion parameters only from sinograms based on the HLCC. To reconstruct the moving object, we formulate two generalized fan-beam reconstruction methods, which are in filtered backprojection and backprojection filtering formats, respectively. Furthermore, we present numerical simulation results to show that our approach is accurate and robust.

In this letter, we present a new fan-beam CT formula, based on which we discuss the relation between the filtered backprojection (FBP) algorithm and the backprojection (BP) algorithm. Specifically, the FBP algorithm can be expressed in a series with its first-order approximation being the BP algorithm. As a result, we identify a link between X-ray CT and number theory.

Computed tomography (CT) has been shown to be effective in providing guidance to operators who perform interventional procedures. In these procedures, commonly called CT fluoroscopy (CTF), a patient is continuously scanned at one or multiple locations while the interventional instrument is inserted. One of the key performance parameters in such a system is the temporal response. In this paper, we first present a mathematical expression that accurately characterizes the system response to an needle type instrument. Based on the model, an optimized reconstruction algorithm is presented that enables an improved temporal resolution and a reduced system delay. The effectiveness of the algorithm is demonstrated by phantom experiments and verified with the mathematical model.

Much research has been performed in recent years to investigate the causes and corrections for 3D image artifacts in computed tomography (CT). In this paper, we present an investigation into the cause of a sawtooth pattern artifact in reformatted images. We show that the artifact is caused by the interaction of the projection weighting and the CT system iso-center misalignment. Several approaches are presented to combat the artifact. The effectiveness and robustness of the correction schemes are demonstrated by results from computer simulations and phantom experiments.

In recent years, the use of computer graphic techniques to produce three-dimensional (3-D) and reformatted images from a set of axial computed tomography (CT) images has gained significant interest. In most cases, the CT images are generated with the projection data set weighted prior to reconstruction, to combat motion artifacts, data inconsistency, or redundant data samples. In this paper, we investigate the potential bias introduced to the reconstruction as a result of the interaction of the projection weights and the isocenter misalignment (ISM). We demonstrate that when the weights applied to the conjugate rays are significantly different, bias will result which favors the sample with a higher weight. Although the error is not easily detected in axial CT images, it can be quite visible in 3-D or multiplanar reformatted (MPR) images. In this paper, we first present a theoretical framework to analyze and predict the bias. The theoretical prediction is validated by both computer simulations and phantom experiments. Several schemes to combat this artifact are subsequently presented; and their effectiveness is demonstrated.

Helical computed tomography (HCT) has become the preferred protocol in routine clinical applications. The advantages of HCT include its capability of scanning a complete anatomical volume in a single breath-hold, and the capability of generating images at arbitrary locations. However, studies have indicated various HCT-related image artifacts in 3D applications. In this paper, we perform a detailed analysis on the cause of one of the artifacts in the 3D shaded surface display (SSD): the "grooving" artifact. We developed an analytical model to show that the artifact is induced by the angular-dependent edge response of the HCT reconstruction. The accuracy of the model is demonstrated by both computer simulations and phantom experiments. Based on the model, we present several correction algorithms to combat the "grooving" artifacts. Computer simulations and phantom experiments have shown the effectiveness and thoroughness of the correction.

Many applications have been found for computed tomography fluoroscopy (CTF) in interventional procedures, such as biopsy. The advantages of CTF is its ability to provide accurate anatomical information at near "real time" speed. However, some studies have indicated that the inherent time lag and delay of the CTF is quite significant. We propose an optimized halfscan algorithm which improves the temporal response of the CTF system. The impact of this algorithm on artifact suppression is also illustrated.

In the computed tomography (CT) head scans, dark streaks connecting the petrous bones are often observed. These streaks can lead to severely degraded image quality and render the image unusable for diagnosis. In this paper, we analyze one the main sources of the streaking artifact the nonlinear partial volume. We have developed an algorithm that effectively combats these artifacts. The algorithm estimates the degree of partial voluming by calculating the variations of the bony structures between adjacent slices. It then performs the correction based on a quadratic error model. Various experiments were conducted to demonstrate the effectiveness of the correction scheme.

The projection set in a helical computed tomography scan is inherently incomplete and inconsistent. This is caused by the constant patient translation during the data acquisition process. We present an interpolative algorithm using the projections acquired within 2/spl pi/ view angle. We compare the image quality of this algorithm with one of the best helical reconstruction algorithms available today and demonstrate its superior performance.

To accurately model the complex behavior of computed tomography (CT) systems, generalized stochastic Petri nets (GSPNs) are used. This approach makes it possible to examine the concurrence and synchronization characteristics, as well as the reliability and performance of the system. To overcome the difficulties of the state space explosion associated with GSPN, an approximate superposition technique is presented. Utilizing the flexibility of the GSPN technique, the model is able to consider not only the failure of each individual processor, but also the complicated failure recovery schemes and communication protocols among processors.>

A new model is presented for evaluating the performance of a degradable computer communication network with blocking, wherein network nodes are modeled with the generalized stochastic petri net (GSPN) method. The authors introduce into the GSPN a concept called the blocking arc to enable proper simulation of the blocking phenomenon. The advantages of GSPN, such as concurrency and parallelism, are exploited. Performance analyses are given for a degradable five-server blocking system which exactly match and extend previous results.>