Accelerated iterative transmission CT reconstruction using an ordered subsets convex algorithm

Truncation effect reduction for fast iterative reconstruction in cone-beam CT

Background

Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object’s actual projection is larger than a flat panel detector, CBCT images contain truncated data or incomplete projections, which degrade image quality inside the field of view (FOV). In this work, we propose truncation effect reduction for fast iterative reconstruction in CBCT imaging.

Methods

The volume matrix size of the FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with conventional filtered backprojection (FBP). To validate the proposed method, we used a head phantom for evaluation and preliminarily tested on a human dataset.

Results

In the experimental results, the reconstructed images from the head phantom showed enhanced image quality. In addition, fast iterative reconstruction can be run continuously while maintaining a consistent mean-percentage-error value for many iterations. The contrast-to-noise ratio of the soft-tissue images was improved. Visualization of low contrast in the ventricle and soft-tissue images was much improved compared to those from FBP using the same dose index of 5 mGy.

Conclusions

Our proposed method showed satisfactory performance to reduce the truncation effect, especially inside the FOV with better image quality for soft-tissue imaging. The convergence of fast iterative reconstruction tends to be stable for many iterations.

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

Image registration is an important research topic in medical image-guided therapy, which is dedicated to registering the high-dose imaging sequences with low-dose/faster means. Registering computer tomography (CT) scanning sequences with cone beam computer tomography (CBCT) scanning sequences is a typical application and has been widely used in CBCT-guided radiotherapy. The main problem is the difference in image clarity of these two image sequences. To solve this problem, for the single projection image sequence matching tasks encountered in medical practice, a novel local quality based curved section encoding strategy is proposed in this paper, which is called the high-quality curved section (HQCS). As an optimized cross-section regularly encoded along the sequence of image, this curved section could be used in order to solve the matching problem. Referencing the independent ground truth provided by medical image physicians, with an experiment combined with the four most widely used indicators used on image registration, matching performance of HQCS on CT/CBCT datasets was tested with varying clarity. Experimental results show that the proposed HQCS can register the CT/CBCT effectively and outperforms the commonly used methods. Specifically, the proposed HQCS has low time complexity and higher scalability, which indicates that the application enhanced the task of diagnosis.

A novel simulation-driven reconstruction approach for X-ray computed tomography

PURPOSE

Radiation dose reduction is critical to the success of x-ray computed tomography (CT). Many advanced reconstruction techniques have been developed over the years to combat noise resulting from the low-dose CT scans. These algorithms rely on accurate local estimation of the image noise to determine reconstruction parameters or to select inferencing models. Because of difficulties in the noise estimation for heterogeneous objects, the performance of many algorithms is inconsistent and suboptimal. In this paper, we propose a novel approach to overcome such shortcoming.

METHOD

By injecting appropriate amount of noise in the CT raw data, a computer simulation approach is capable of accurately estimating the local statistics of the raw data and the local noise in the reconstructed images. This information is then used to guide the noise reduction process during the reconstruction. As an initial implementation, a scaling map is generated based on the noise predicted from the simulation and the noise estimated from existing reconstruction algorithms. Images generated with existing algorithms are subsequently modified based on the scaling map. In this study, both iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms are evaluated.

RESULTS

Phantom experiments were conducted to evaluate the performance of the simulation-based noise estimation in terms of the standard deviation and noise power spectrum (NPS). Quantitative results have demonstrated that the noise measured from the original image matches well with the noise estimated from the simulation. Clinical datasets were utilized to further confirm the accuracy of the proposed approach under more challenging conditions. To validate the performance of the proposed reconstruction approach, clinical scans were used. Performance comparison was carried out qualitatively and quantitatively. Two existing advanced reconstruction techniques, IR and DLIR, were evaluated against the proposed approach. Results have shown that the proposed approach outperforms existing IR and DLIR algorithms in terms of noise suppression and, equally importantly, noise uniformity across the entire imaging volume. Visual assessment of the images also reveals that the proposed approach does not endure noise texture issues facing some of the existing reconstruction algorithms today.

CONCLUSION

Phantom and clinical results have demonstrated superior performance of the proposed approach with regard to noise reduction as well as noise homogeneity. Visual inspection of the noise texture further confirms the clinical utility of the proposed approach. Future enhancements on the current implementation are explored regarding image quality and computational efficiency. Because of the limited scope of this paper, detailed investigation on these enhancement features will be covered in a separate report. This article is protected by copyright. All rights reserved.

Characterization of tissue-specific pre-log Bayesian CT reconstruction by texture-dose relationship

PURPOSE

Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm.

METHODS

To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics.

RESULTS

Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve.

CONCLUSIONS

This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.

Ordered subsets Non-Local means constrained reconstruction for sparse view cone beam CT system

Sparse-view sampling scans reduce the patient's radiation dose by reducing the total exposure duration. CT reconstructions under such scan mode are often accompanied by severe artifacts due to the high ill-posedness of the problem. In this paper, we use a Non-Local means kernel as a regularization constraint to reconstruct image volumes from sparse-angle sampled cone-beam CT scans. To overcome the huge computational cost of the 3D reconstruction, we propose a sequential update scheme relying on ordered subsets in the image domain. It is shown through experiments on simulated and real data and comparisons with other methods that the proposed approach is robust enough to deal with the number of views reduced up to 1/10. When coupled with a CUDA parallel computing technique, the computation speed of the iterative reconstruction is greatly improved.

Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

Contemporary CBCT diagnostics-discovery of a new artery with possible impact on surgical planning: the anterior superior palatal alveolar artery

PURPOSE

An ongoing clinical trial regarding intra- and post-surgical morbidity in maxillary apicoectomies showed significant higher morbidity for upper canines and palatal roots of upper 1st premolars. Analysis of available presurgical cone beam computed tomography (CBCT)-scans revealed the existence of an unknown bone-canal branching off from the bone-canal or groove of the anterior superior alveolar artery (asaa). Aim of the study was the determination of the contents of this newly found bone canal in human cadaver heads, its prevalence as possible standard anatomical structure and its automatized detection with a contemporary high-resolution TRIUM-CBCT-device in vivo.

METHODS

35 human cadaver heads were dissected, the prevalence of the bone-canal determined and its contents analyzed by histology. 835 consecutive routine high-resolution TRIUM-CBCT-scans from routine patients were analyzed by an automatized detection- and tracing-algorithm for in vivo-determination of prevalence of this bone canal. Automatized detection and additional manual tracing were statistically evaluated by SSPS 20.0 software.

RESULTS

The bone-canal was found in 96% of the anatomical specimens, its content identified as artery not described until now and named after the first finder "Arteria Kurrekii". Automatized tracing of TRIUM-CBCT-scans with additional manual tracing revealed an in vivo prevalence of this newly found artery of 95% (p ≤ 0.05).

CONCLUSIONS

The newly found anterior superior palatal alveolar artery (aspaa-"Arteria Kurrekii") might have the same clinical impact for surgical procedures in the maxilla as the posterior superior alveolar artery (psaa). Its first detection was enabled by high-resolution TRIUM-CBCT devices and prevalence as standard anatomical structure proven in vivo by automatized CBCT-scan analysis.

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle

Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm

BACKGROUND

Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time.

OBJECTIVE

The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV).

METHODS

Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs.

RESULTS

All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality.

CONCLUSION

The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

Model-based Iterative CT Image Reconstruction on GPUs

Computed Tomography (CT) Image Reconstruction is an important technique used in a variety of domains, including medical imaging, electron microscopy, non-destructive testing and transportation security. Model-based Iterative Reconstruction (MBIR) using Iterative Coordinate Descent (ICD) is a CT algorithm that produces state-of-the-art results in terms of image quality. However, MBIR is highly computationally intensive and challenging to parallelize, and has traditionally been viewed as impractical in applications where reconstruction time is critical. We present the first GPU-based algorithm for ICD-based MBIR. The algorithm leverages the recently-proposed concept of SuperVoxels, and efficiently exploits the three levels of parallelism available in MBIR to better utilize the GPU hardware resources. We also explore data layout transformations to obtain more coalesced accesses and several GPU-specific optimizations for MBIR that boost performance. Across a suite of 3200 test cases, our GPU implementation obtains a geometric mean speedup of 4.43X over a state-of-the-art multi-core implementation on a 16-core iso-power CPU.

Implementation and CT sampling characterization of a third-generation SPECT-CT system for dedicated breast imaging

Stand-alone cone beam computed tomography (CT) and single-photon emission computed tomography (SPECT) systems capable of complex acquisition trajectories have previously been developed for breast imaging. Fully three-dimensional (3-D) motions of SPECT systems provide views into the chest wall and throughout the entire volume. The polar tilting capability of the CBCT system has shown improvement in sampling close to the chest wall, while eliminating cone beam artifacts. Here, a single hybrid SPECT-CT system, with each individual modality capable of independently traversing complex trajectories around a common pendant breast volume, was developed. We present the practical implementation of this design and preliminary results of the CT system. The fully 3-D SPECT was nested inside the suspended CT gantry and oriented perpendicular to the CT source-detector pair. Both subsystems were positioned on a rotation stage, with the combined polar and azimuthal motions enabling spherical trajectories. Six trajectories were used for initial evaluation of the tilt capable CT system. The developed system can achieve polar tilt angles with a [Formula: see text] positioning error and no hysteresis. Initial imaging results demonstrate that additional off-axis projection views of various geometric resolution phantoms facilitate more complete sampling, more consistent attenuation value recovery, and markedly improved reconstructions. This system could have various applications in diagnostic or therapeutic breast imaging.

A comparison of linear interpolation models for iterative CT reconstruction

PURPOSE

Recent reports indicate that model-based iterative reconstruction methods may improve image quality in computed tomography (CT). One difficulty with these methods is the number of options available to implement them, including the selection of the forward projection model and the penalty term. Currently, the literature is fairly scarce in terms of guidance regarding this selection step, whereas these options impact image quality. Here, the authors investigate the merits of three forward projection models that rely on linear interpolation: the distance-driven method, Joseph's method, and the bilinear method. The authors' selection is motivated by three factors: (1) in CT, linear interpolation is often seen as a suitable trade-off between discretization errors and computational cost, (2) the first two methods are popular with manufacturers, and (3) the third method enables assessing the importance of a key assumption in the other methods.

METHODS

One approach to evaluate forward projection models is to inspect their effect on discretized images, as well as the effect of their transpose on data sets, but significance of such studies is unclear since the matrix and its transpose are always jointly used in iterative reconstruction. Another approach is to investigate the models in the context they are used, i.e., together with statistical weights and a penalty term. Unfortunately, this approach requires the selection of a preferred objective function and does not provide clear information on features that are intrinsic to the model. The authors adopted the following two-stage methodology. First, the authors analyze images that progressively include components of the singular value decomposition of the model in a reconstructed image without statistical weights and penalty term. Next, the authors examine the impact of weights and penalty on observed differences.

RESULTS

Image quality metrics were investigated for 16 different fan-beam imaging scenarios that enabled probing various aspects of all models. The metrics include a surrogate for computational cost, as well as bias, noise, and an estimation task, all at matched resolution. The analysis revealed fundamental differences in terms of both bias and noise. Task-based assessment appears to be required to appreciate the differences in noise; the estimation task the authors selected showed that these differences balance out to yield similar performance. Some scenarios highlighted merits for the distance-driven method in terms of bias but with an increase in computational cost. Three combinations of statistical weights and penalty term showed that the observed differences remain the same, but strong edge-preserving penalty can dramatically reduce the magnitude of these differences.

CONCLUSIONS

In many scenarios, Joseph's method seems to offer an interesting compromise between cost and computational effort. The distance-driven method offers the possibility to reduce bias but with an increase in computational cost. The bilinear method indicated that a key assumption in the other two methods is highly robust. Last, strong edge-preserving penalty can act as a compensator for insufficiencies in the forward projection model, bringing all models to similar levels in the most challenging imaging scenarios. Also, the authors find that their evaluation methodology helps appreciating how model, statistical weights, and penalty term interplay together.

Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

X-ray detectors in clinical computed tomography (CT) usually operate in current-integrating mode. Their complicated signal statistics often lead to intractable likelihood functions for practical use in model-based image reconstruction (MBIR). It is therefore desirable to design simplified statistical models without losing the essential factors. Depending on whether the CT transmission data are logarithmically transformed, pre-log and post-log models are two major categories of choices in CT MBIR. Both being approximations, it remains an open question whether one model can notably improve image quality over the other on real scanners. In this study, we develop and compare several pre-log and post-log MBIR algorithms under a unified framework. Their reconstruction accuracy based on simulation and clinical datasets are evaluated. The results show that pre-log MBIR can achieve notably better quantitative accuracy than post-log MBIR in ultra-low-dose CT, although in less extreme cases, post-log MBIR with handcrafted pre-processing remains a competitive alternative. Pre-log MBIR could play a growing role in emerging ultra-low-dose CT applications.

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach.

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

PURPOSE

The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT.

METHODS

Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units.

RESULTS

The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min.

CONCLUSIONS

The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.

High performance model based image reconstruction

Computed Tomography (CT) Image Reconstruction is an important technique used in a wide range of applications, ranging from explosive detection, medical imaging to scientific imaging. Among available reconstruction methods, Model Based Iterative Reconstruction (MBIR) produces higher quality images and allows for the use of more general CT scanner geometries than is possible with more commonly used methods. The high computational cost of MBIR, however, often makes it impractical in applications for which it would otherwise be ideal. This paper describes a new MBIR implementation that significantly reduces the computational cost of MBIR while retaining its benefits. It describes a novel organization of the scanner data into super-voxels (SV) that, combined with a super-voxel buffer (SVB), dramatically increase locality and prefetching, enable parallelism across SVs and lead to an average speedup of 187 on 20 cores.

Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares

Background

In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L₂-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction.

Methods

In this paper, we replaced the L₂-norm regularization term with the L₁-norm one. It is expected that the proposed L₁-DL method could alleviate the over-smoothing effect of the L₂-minimization and reserve more image details. The proposed algorithm solves the L₁-minimization problem by a weighting strategy, solving the new weighted L₂-minimization problem based on IRLS (iteratively reweighted least squares).

Results

Through the numerical simulation, the proposed algorithm is compared with the existing DL method (adaptive dictionary based statistical iterative reconstruction, ADSIR) and other two typical compressed sensing algorithms. It is revealed that the proposed algorithm is more accurate than the other algorithms especially when further reducing the sampling rate or increasing the noise.

Conclusion

The proposed L₁-DL algorithm can utilize more prior information of image sparsity than ADSIR. By transforming the L₂-norm regularization term of ADSIR with the L₁-norm one and solving the L₁-minimization problem by IRLS strategy, L₁-DL could reconstruct the image more exactly.

Improving Low-dose Cardiac CT Images based on 3D Sparse Representation

Cardiac computed tomography (CCT) is a reliable and accurate tool for diagnosis of coronary artery diseases and is also frequently used in surgery guidance. Low-dose scans should be considered in order to alleviate the harm to patients caused by X-ray radiation. However, low dose CT (LDCT) images tend to be degraded by quantum noise and streak artifacts. In order to improve the cardiac LDCT image quality, a 3D sparse representation-based processing (3D SR) is proposed by exploiting the sparsity and regularity of 3D anatomical features in CCT. The proposed method was evaluated by a clinical study of 14 patients. The performance of the proposed method was compared to the 2D spares representation-based processing (2D SR) and the state-of-the-art noise reduction algorithm BM4D. The visual assessment, quantitative assessment and qualitative assessment results show that the proposed approach can lead to effective noise/artifact suppression and detail preservation. Compared to the other two tested methods, 3D SR method can obtain results with image quality most close to the reference standard dose CT (SDCT) images.

GPU-accelerated regularized iterative reconstruction for few-view cone beam CT

PURPOSE

The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames.

METHODS

The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it is implemented on a graphics processing unit, using parallelization to accelerate computations.

RESULTS

The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1-2 min and are compatible with the typical clinical workflow for nonreal-time applications.

CONCLUSIONS

Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.

Multigrid reconstruction with block-iterative updates for breast tomosynthesis

PURPOSE

The authors wish to evaluate the possible advantages of using a multigrid approach to maximum-a-posteriori reconstruction in digital breast tomosynthesis together with block-iterative updates in the form of either plane-by-plane updates or ordered subsets.

METHODS

The authors previously developed a penalized maximum likelihood reconstruction algorithm with resolution model dedicated to breast tomosynthesis [K. Michielsen et al., "Patchwork reconstruction with resolution modeling for digital breast tomosynthesis," Med. Phys. 40, 031105 (10pp.) (2013)]. This algorithm was extended with ordered subsets and multigrid updates, and the effects on the convergence and on limited angle artifact appearance were evaluated on a mathematical phantom and patient data. To ensure a fair comparison, the analysis was performed at the same computational cost for all methods. To assess convergence and artifact creation in the phantom reconstructions, the authors looked at posterior likelihood, sum of squared residuals, contrast of identical calcifications at different positions, and the standard deviation between the contrasts of these calcifications. For the patient cases, the authors calculated posterior likelihood, measured the signal difference to noise ratio of subtle microcalcifications, and visually evaluated the reconstructions.

RESULTS

The authors selected multigrid sequences scoring in the best 10% of the four evaluated parameters, except for the reconstructions with subsets where a low standard deviation of the contrast was incompatible with the three other parameters. In further evaluation of phantom reconstructions from noisy data and patient data, the authors found improved convergence and a reduction in artifacts for our chosen multigrid reconstructions compared to the single grid reconstructions with equivalent computational cost, although there was a diminishing return for an increasing number of subsets.

CONCLUSIONS

Multigrid reconstruction improves upon reconstruction with a fixed grid when evaluated at a fixed computational cost. For multigrid reconstruction, using plane-by-plane updates or applying ordered subsets resulted in similar performance.

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning

In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.

Technical aspects of dental CBCT: state of the art

As CBCT is widely used in dental and maxillofacial imaging, it is important for users as well as referring practitioners to understand the basic concepts of this imaging modality. This review covers the technical aspects of each part of the CBCT imaging chain. First, an overview is given of the hardware of a CBCT device. The principles of cone beam image acquisition and image reconstruction are described. Optimization of imaging protocols in CBCT is briefly discussed. Finally, basic and advanced visualization methods are illustrated. Certain topics in these review are applicable to all types of radiographic imaging (e.g. the principle and properties of an X-ray tube), others are specific for dental CBCT imaging (e.g. advanced visualization techniques).

Artifact suppressed dictionary learning for low-dose CT image processing

Low-dose computed tomography (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called "artifact suppressed dictionary learning (ASDL)." In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation operation based on these dictionaries. Then, a general dictionary learning processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems.

Ordered subsets convex algorithm for 3D terahertz transmission tomography

We investigate in this paper a new reconstruction method in order to perform 3D Terahertz (THz) tomography using a continuous wave acquisition setup in transmission mode. This method is based on the Maximum Likelihood for TRansmission tomography (ML-TR) first developed for X-ray imaging. We optimize the Ordered Subsets Convex (OSC) implementation of the ML-TR by including the Gaussian propagation model of THz waves and take into account the intensity distributions of both blank calibration scan and dark-field measured on THz detectors. THz ML-TR reconstruction quality and accuracy are discussed and compared to other tomographic reconstructions.

Characterization of statistical prior image constrained compressed sensing (PICCS): II. Application to dose reduction

PURPOSE

The ionizing radiation imparted to patients during computed tomography exams is raising concerns. This paper studies the performance of a scheme called dose reduction using prior image constrained compressed sensing (DR-PICCS). The purpose of this study is to characterize the effects of a statistical model of x-ray detection in the DR-PICCS framework and its impact on spatial resolution.

METHODS

Both numerical simulations with known ground truth and in vivo animal dataset were used in this study. In numerical simulations, a phantom was simulated with Poisson noise and with varying levels of eccentricity. Both the conventional filtered backprojection (FBP) and the PICCS algorithms were used to reconstruct images. In PICCS reconstructions, the prior image was generated using two different denoising methods: a simple Gaussian blur and a more advanced diffusion filter. Due to the lack of shift-invariance in nonlinear image reconstruction such as the one studied in this paper, the concept of local spatial resolution was used to study the sharpness of a reconstructed image. Specifically, a directional metric of image sharpness, the so-called pseudopoint spread function (pseudo-PSF), was employed to investigate local spatial resolution.

RESULTS

In the numerical studies, the pseudo-PSF was reduced from twice the voxel width in the prior image down to less than 1.1 times the voxel width in DR-PICCS reconstructions when the statistical model was not included. At the same noise level, when statistical weighting was used, the pseudo-PSF width in DR-PICCS reconstructed images varied between 1.5 and 0.75 times the voxel width depending on the direction along which it was measured. However, this anisotropy was largely eliminated when the prior image was generated using diffusion filtering; the pseudo-PSF width was reduced to below one voxel width in that case. In the in vivo study, a fourfold improvement in CNR was achieved while qualitatively maintaining sharpness; images also had a qualitatively more uniform noise spatial distribution when including a statistical model.

CONCLUSIONS

DR-PICCS enables to reconstruct CT images with lower noise than FBP and the loss of spatial resolution can be mitigated to a large extent. The introduction of statistical modeling in DR-PICCS may improve some noise characteristics, but it also leads to anisotropic spatial resolution properties. A denoising method, such as the directional diffusion filtering, has been demonstrated to reduce anisotropy in spatial resolution effectively when it was combined with DR-PICCS with statistical modeling.

Information-theoretic discrepancy based iterative reconstructions (IDIR) for polychromatic x-ray tomography

PURPOSE

X-ray photons generated from a typical x-ray source for clinical applications exhibit a broad range of wavelengths, and the interactions between individual particles and biological substances depend on particles' energy levels. Most existing reconstruction methods for transmission tomography, however, neglect this polychromatic nature of measurements and rely on the monochromatic approximation. In this study, we developed a new family of iterative methods that incorporates the exact polychromatic model into tomographic image recovery, which improves the accuracy and quality of reconstruction.

METHODS

The generalized information-theoretic discrepancy (GID) was employed as a new metric for quantifying the distance between the measured and synthetic data. By using special features of the GID, the objective function for polychromatic reconstruction which contains a double integral over the wavelength and the trajectory of incident x-rays was simplified to a paraboloidal form without using the monochromatic approximation. More specifically, the original GID was replaced with a surrogate function with two auxiliary, energy-dependent variables. Subsequently, the alternating minimization technique was applied to solve the double minimization problem. Based on the optimization transfer principle, the objective function was further simplified to the paraboloidal equation, which leads to a closed-form update formula. Numerical experiments on the beam-hardening correction and material-selective reconstruction were conducted to compare and assess the performance of conventional methods and the proposed algorithms.

RESULTS

The authors found that the GID determines the distance between its two arguments in a flexible manner. In this study, three groups of GIDs with distinct data representations were considered. The authors demonstrated that one type of GIDs that comprises "raw" data can be viewed as an extension of existing statistical reconstructions; under a particular condition, the GID is equivalent to the Poisson log-likelihood function. The newly proposed GIDs of the other two categories consist of log-transformed measurements, which have the advantage of imposing linearized penalties over multiple discrepancies. For all proposed variants of the GID, the aforementioned strategy was used to obtain a closed-form update equation. Even though it is based on the exact polychromatic model, the derived algorithm bears a structural resemblance to conventional methods based on the monochromatic approximation. The authors named the proposed approach as information-theoretic discrepancy based iterative reconstructions (IDIR). In numerical experiments, IDIR with raw data converged faster than previously known statistical reconstruction methods. IDIR with log-transformed data exhibited superior reconstruction quality and faster convergence speed compared with conventional methods and their variants.

CONCLUSIONS

The authors' new framework for tomographic reconstruction allows iterative inversion of the polychromatic data model. The primary departure from the traditional iterative reconstruction was the employment of the GID as a new metric for quantifying the inconsistency between the measured and synthetic data. The proposed methods outperformed not only conventional methods based on the monochromatic approximation but also those based on the polychromatic model. The authors have observed that the GID is a very flexible means to design an objective function for iterative reconstructions. Hence, the authors expect that the proposed IDIR framework will also be applicable to other challenging tasks.

Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing

In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.

Characterization of statistical prior image constrained compressed sensing. I. Applications to time-resolved contrast-enhanced CT

PURPOSE

Prior image constrained compressed sensing (PICCS) is an image reconstruction framework that takes advantage of a prior image to improve the image quality of CT reconstructions. An interesting question that remains to be investigated is whether or not the introduction of a statistical model of the photon detection in the PICCS reconstruction framework can improve the performance of the algorithm when dealing with high noise projection datasets. The goal of the research presented in this paper is to characterize the noise properties of images reconstructed using PICCS with and without statistical modeling. This paper investigates these properties in the clinical context of time-resolved contrast-enhanced CT.

METHODS

Both numerical phantom studies and an Institutional Review Board approved human subject study were used in this research. The conventional filtered backprojection (FBP), and PICCS with and without the statistical model were applied to each dataset. The prior image used in PICCS was generated by averaging over FBP reconstructions from different time frames of the time-resolved CT exam, thus reducing the noise level. Numerical studies were used to evaluate if the noise characteristics are altered for varying levels of noise, as well as for different object shapes. The dataset acquired in vivo was used to verify that the conclusions reached from numerical studies translate adequately to a clinical case. The results were analyzed using a variety of qualitative and quantitative metrics such as the universal image quality index, spatial maps of the noise standard deviations, the noise uniformity, the noise power spectrum, and the model-observer detectability.

RESULTS

The noise characteristics of PICCS were shown to depend on the noise level contained in the data, the level of eccentricity of the object, and whether or not the statistical model was applied. Most differences in the characteristics were observed in the regime of low incident x-ray fluence. No substantial difference was observed between PICCS with and without statistics in the high fluence domain. Objects with a semi-major axis ratio below 0.85 were more accurately reconstructed with lower noise using the statistical implementation. Above that range, for mostly circular objects, the PICCS implementation without the statistical model yielded more accurate images and a lower noise level. At all levels of eccentricity, the noise spatial distribution was more uniform and the model-observer detectability was greater for PICCS with the statistical model. The human subject study was consistent with the results obtained using numerical simulations.

CONCLUSIONS

For mildly eccentric objects in the low noise regime, PICCS without the noise model yielded equal or better noise level and image quality than the statistical formulation. However, in a vast majority of cases, images reconstructed using statistical PICCS have a noise power spectrum that facilitated the detection of model lesions. The inclusion of a statistical model in the PICCS framework does not always result in improved noise characteristics.

Toward truly combined PET/CT imaging using PET detectors and photon counting CT with iterative reconstruction implementing physical detector response

PURPOSE

This paper intends to demonstrate the feasibility of truly combined PET/CT imaging and addresses some of the major challenges raised by this dual modality approach. A method is proposed to retrieve maximum accuracy out of limited resolution computed tomography (CT) scans acquired with positron emission tomography (PET) detectors.

METHODS

A PET/CT simulator was built using the LabPET™ detectors and front-end electronics. Acquisitions of energy-binned data sets were made using this low spatial resolution CT system in photon counting mode. To overcome the limitations of the filtered back-projection technique, an iterative reconstruction library was developed and tested for the counting mode CT. Construction of the system matrix is based on a preregistered raster scan from which the experimental detector response is obtained. PET data were obtained sequentially with CT in a conventional manner.

RESULTS

A meticulous description of the system geometry and misalignment corrections is imperative and was incorporated into the matrix definition to achieve good image quality. Using this method, no sinogram precorrection or interpolation is necessary and measured projections can be used as raw input data for the iterative reconstruction algorithm. Genuine dual modality PET/CT images of phantoms and animals were obtained for the first time using the same detection platform.

CONCLUSIONS

CT and fused PET/CT images show that LabPET™ detectors can be successfully used as individual X-ray photon counting devices for low-dose CT imaging of the anatomy in a molecular PET imaging context.

Low-dose X-ray CT reconstruction via dictionary learning

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.

4D micro-CT using fast prospective gating

Micro-CT is currently used in preclinical studies to provide anatomical information. But, there is also significant interest in using this technology to obtain functional information. We report here a new sampling strategy for 4D micro-CT for functional cardiac and pulmonary imaging. Rapid scanning of free-breathing mice is achieved with fast prospective gating (FPG) implemented on a field programmable gate array. The method entails on-the-fly computation of delays from the R peaks of the ECG signals or the peaks of the respiratory signals for the triggering pulses. Projection images are acquired for all cardiac or respiratory phases at each angle before rotating to the next angle. FPG can deliver the faster scan time of retrospective gating (RG) with the regular angular distribution of conventional prospective gating for cardiac or respiratory gating. Simultaneous cardio-respiratory gating is also possible with FPG in a hybrid retrospective/prospective approach. We have performed phantom experiments to validate the new sampling protocol and compared the results from FPG and RG in cardiac imaging of a mouse. Additionally, we have evaluated the utility of incorporating respiratory information in 4D cardiac micro-CT studies with FPG. A dual-source micro-CT system was used for image acquisition with pulsed x-ray exposures (80 kVp, 100 mA, 10 ms). The cardiac micro-CT protocol involves the use of a liposomal blood pool contrast agent containing 123 mg I ml(-1) delivered via a tail vein catheter in a dose of 0.01 ml g(-1) body weight. The phantom experiment demonstrates that FPG can distinguish the successive phases of phantom motion with minimal motion blur, and the animal study demonstrates that respiratory FPG can distinguish inspiration and expiration. 4D cardiac micro-CT imaging with FPG provides image quality superior to RG at an isotropic voxel size of 88 μm and 10 ms temporal resolution. The acquisition time for either sampling approach is less than 5 min. The radiation dose associated with the proposed method is in the range of a typical micro-CT dose (256 mGy for the cardiac study). Ignoring respiration does not significantly affect anatomic information in cardiac studies. FPG can deliver short scan times with low-dose 4D micro-CT imaging without sacrificing image quality. FPG can be applied in high-throughput longitudinal studies in a wide range of applications, including drug safety and cardiopulmonary phenotyping.

Image reconstruction for hybrid true-color micro-CT

X-ray micro-CT is an important imaging tool for biomedical researchers. Our group has recently proposed a hybrid "true-color" micro-CT system to improve contrast resolution with lower system cost and radiation dose. The system incorporates an energy-resolved photon-counting true-color detector into a conventional micro-CT configuration, and can be used for material decomposition. In this paper, we demonstrate an interior color-CT image reconstruction algorithm developed for this hybrid true-color micro-CT system. A compressive sensing-based statistical interior tomography method is employed to reconstruct each channel in the local spectral imaging chain, where the reconstructed global gray-scale image from the conventional imaging chain served as the initial guess. Principal component analysis was used to map the spectral reconstructions into the color space. The proposed algorithm was evaluated by numerical simulations, physical phantom experiments, and animal studies. The results confirm the merits of the proposed algorithm, and demonstrate the feasibility of the hybrid true-color micro-CT system. Additionally, a "color diffusion" phenomenon was observed whereby high-quality true-color images are produced not only inside the region of interest, but also in neighboring regions. It appears harnessing that this phenomenon could potentially reduce the color detector size for a given ROI, further reducing system cost and radiation dose.

A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction

Statistical image reconstruction using penalized weighted least-squares (PWLS) criteria can improve image-quality in X-ray computed tomography (CT). However, the huge dynamic range of the statistical weights leads to a highly shift-variant inverse problem making it difficult to precondition and accelerate existing iterative algorithms that attack the statistical model directly. We propose to alleviate the problem by using a variable-splitting scheme that separates the shift-variant and ("nearly") invariant components of the statistical data model and also decouples the regularization term. This leads to an equivalent constrained problem that we tackle using the classical method-of-multipliers framework with alternating minimization. The specific form of our splitting yields an alternating direction method of multipliers (ADMM) algorithm with an inner-step involving a "nearly" shift-invariant linear system that is suitable for FFT-based preconditioning using cone-type filters. The proposed method can efficiently handle a variety of convex regularization criteria including smooth edge-preserving regularizers and nonsmooth sparsity-promoting ones based on the l(1)-norm and total variation. Numerical experiments with synthetic and real in vivo human data illustrate that cone-filter preconditioners accelerate the proposed ADMM resulting in fast convergence of ADMM compared to conventional (nonlinear conjugate gradient, ordered subsets) and state-of-the-art (MFISTA, split-Bregman) algorithms that are applicable for CT.

Iterative reconstruction methods in X-ray CT

Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray computed tomography (CT). They were successfully used in the early years of CT, but given up when the amount of measured data increased because of the higher computational demands of IR compared to analytical methods. The availability of large computational capacities in normal workstations and the ongoing efforts towards lower doses in CT have changed the situation; IR has become a hot topic for all major vendors of clinical CT systems in the past 5 years. This review strives to provide information on IR methods and aims at interested physicists and physicians already active in the field of CT. We give an overview on the terminology used and an introduction to the most important algorithmic concepts including references for further reading. As a practical example, details on a model-based iterative reconstruction algorithm implemented on a modern graphics adapter (GPU) are presented, followed by application examples for several dedicated CT scanners in order to demonstrate the performance and potential of iterative reconstruction methods. Finally, some general thoughts regarding the advantages and disadvantages of IR methods as well as open points for research in this field are discussed.

Recent advances in iterative reconstruction for clinical SPECT/PET and CT

Statistical iterative reconstruction is now widely used in clinical practice and has contributed to significant improvement in image quality in recent years. Although primarily used for reconstruction in emission tomography (both single photon emission computed tomography (SPECT) and positron emission tomography (PET)) there is increasing interest in also applying similar algorithms to x-ray computed tomography (CT). There is increasing complexity in the factors that are included in the reconstruction, a demonstration of the versatility of the approach. Research continues with exploration of methods for further improving reconstruction quality with effective correction for various sources of artefact.

Statistical interior tomography

This paper presents a statistical interior tomography (SIT) approach making use of compressed sensing (CS) theory. With the projection data modeled by the Poisson distribution, an objective function with a total variation (TV) regularization term is formulated in the maximization of a posteriori (MAP) framework to solve the interior problem. An alternating minimization method is used to optimize the objective function with an initial image from the direct inversion of the truncated Hilbert transform. The proposed SIT approach is extensively evaluated with both numerical and real datasets. The results demonstrate that SIT is robust with respect to data noise and down-sampling, and has better resolution and less bias than its deterministic counterpart in the case of low count data.

Detailed Characterization of 2D and 3D Scatter-to-Primary Ratios of Various Breast Geometries Using a Dedicated CT Mammotomography System

With a dedicated breast CT system using a quasi-monochromatic x-ray source and flat-panel digital detector, the 2D and 3D scatter to primary ratios (SPR) of various geometric phantoms having different densities were characterized in detail. Projections were acquired using geometric and anthropomorphic breast phantoms. Each phantom was filled with 700ml of 5 different water-methanol concentrations to simulate effective boundary densities of breast compositions from 100% glandular (1.0g/cm(3)) to 100% fat (0.79g/cm(3)). Projections were acquired with and without a beam stop array. For each projection, 2D scatter was determined by cubic spline interpolating the values behind the shadow of each beam stop through the object. Scatter-corrected projections were obtained by subtracting the scatter, and the 2D SPRs were obtained as a ratio of the scatter to scatter-corrected projections. Additionally the (un)corrected data were individually iteratively reconstructed. The (un)corrected 3D volumes were subsequently subtracted, and the 3D SPRs obtained from the ratio of the scatter volume-to-scatter-corrected (or primary) volume. Results show that the 2D SPR values peak in the center of the volumes, and were overall highest for the simulated 100% glandular composition. Consequently, scatter corrected reconstructions have visibly reduced cupping regardless of the phantom geometry, as well as more accurate linear attenuation coefficients. The corresponding 3D SPRs have increased central density, which reduces radially. Not surprisingly, for both 2D and 3D SPRs there was a dependency on both phantom geometry and object density on the measured SPR values, with geometry dominating for 3D SPRs. Overall, these results indicate the need for scatter correction given different geometries and breast densities that will be encountered with 3D cone beam breast CT.

Small field-of-view dedicated cardiac SPECT systems: impact of projection truncation

PURPOSE

Small field-of-view (FOV) dedicated cardiac SPECT systems suffer from truncated projection data. This results in (1) neglect of liver activity that otherwise could be used to estimate (and subsequently correct) the amount of scatter in the myocardium by model-based scatter correction, and (2) distorted attenuation maps. In this study, we investigated to what extent truncation impacts attenuation correction and model-based scatter correction in the cases of (99m)Tc, (201)Tl, and simultaneous (99m)Tc/(201)Tl studies. In addition, we evaluated a simple correction method to mitigate the effects of truncation.

METHODS

Digital thorax phantoms of different sizes were used to simulate the full FOV SPECT projections for (99m)Tc, (201)Tl, and simultaneous (99m)Tc/(201)Tl studies. Small FOV projections were obtained by artificially truncating the full FOV projections. Deviations from ideal heart positioning were simulated by axially shifting projections resulting in more severe liver truncation. Effects of truncation on SPECT images were tested for ordered subset (OS) expectation maximization reconstruction with (1) attenuation correction and detector response modelling (OS-AD), and (2) with additional Monte-Carlo-based scatter correction (OS-ADS). To correct truncation-induced artefacts, we axially extended truncated projections on both sides by duplicating pixel values on the projection edge.

RESULTS

For both (99m)Tc and (201)Tl, differences in the reconstructed myocardium between full FOV and small FOV projections were negligible. In the nine myocardial segments, the maximum deviations of the average pixel values were 1.3% for OS-AD and 3.5% for OS-ADS. For the simultaneous (99m)Tc/(201)Tl studies, reconstructed (201)Tl SPECT images from full FOV and small FOV projections showed clearly different image profiles due to truncation. The maximum deviation in defected segments was found to be 49% in the worst-case scenario. However, artificially extending projections reduced deviations in defected segments to a few percent.

CONCLUSION

Our results indicate that, for single isotope studies, using small FOV systems has little impact on attenuation correction and model-based scatter correction. For simultaneous (99m)Tc/(201)Tl studies, artificial projection extension almost fully eliminates the adverse effects of projection truncation.

Three-dimensional imaging properties of rotation-free square and hexagonal micro-CT systems

We study the 3-D imaging properties of a rotation-free micro-computed tomography (CT) system with square and hexagonal geometries. These systems use linear arrays of carbon-nanotube-based X-ray sources that are individually addressable. The source arrays and area detectors in the square and the hexagonal geometries form the sides of a polygon. The tomographic angular sampling for both geometries requires no motion of the sources, the detectors, or the subject. We demonstrate that the hexagonal geometry has improved angular coverage as compared to the square geometry. The ordered-subset convex iterative algorithm is implemented in both geometries for reconstructions from cone-beam projection data. Simulation studies show that both geometries can be effectively reconstructed with polychromatic or monochromatic source spectra. As a result of the incomplete tomographic sampling of the two geometries, some streaking artifacts appear in the reconstructed images. The hexagonal geometry, in general, produces fewer streaking artifacts than the square geometry. On the other hand, the two geometries perform quite similarly in resolution-noise trade-off, so we conclude that the proposed geometries are comparably effective for the rotation-free micro-CT and the hexagonal geometry is superior in reducing streaking artifacts.

Parallelism of iterative CT reconstruction based on local reconstruction algorithm

An iterative algorithm is suited to reconstruct CT images from noisy or truncated projection data. However, as a disadvantage, the algorithm requires significant computational time. Although a parallel technique can be used to reduce the computational time, a large amount of communication overhead becomes an obstacle to its performance (Li et al. in J. X-Ray Sci. Technol. 13:1-10, 2005). To overcome this problem, we proposed an innovative parallel method based on the local iterative CT reconstruction algorithm (Wang et al. in Scanning 18:582-588, 1996 and IEEE Trans. Med. Imaging 15(5):657-664, 1996). The object to be reconstructed is partitioned into a number of subregions and assigned to different processing elements (PEs). Within each PE, local iterative reconstruction is performed to recover the subregion. Several numerical experiments were conducted on a high performance computing cluster. And the FORBILD head phantom (Lauritsch and Bruder http://www.imp.uni-erlangen.de/phantoms/head/head.html) was used as benchmark to measure the parallel performance. The experimental results showed that the proposed parallel algorithm significantly reduces the reconstruction time, hence achieving a high speedup and efficiency.

A faster ordered-subset convex algorithm for iterative reconstruction in a rotation-free micro-CT system

We present a faster iterative reconstruction algorithm based on the ordered-subset convex (OSC) algorithm for transmission CT. The OSC algorithm was modified such that it calculates the normalization term before the iterative process in order to save computational cost. The modified version requires only one backprojection per iteration as compared to two required for the original OSC. We applied the modified OSC (MOSC) algorithm to a rotation-free micro-CT system that we proposed previously, observed its performance, and compared with the OSC algorithm for 3D cone-beam reconstruction. Measurements on the reconstructed images as well as the point spread functions show that MOSC is quite similar to OSC; in noise-resolution trade-off, MOSC is comparable with OSC in a regular-noise situation and it is slightly worse than OSC in an extremely high-noise situation. The timing record shows that MOSC saves 25-30% CPU time, depending on the number of iterations used. We conclude that the MOSC algorithm is more efficient than OSC and provides comparable images.

Development of novel statistical reconstruction algorithms for poly-energetic X-ray computed tomography

A beam-hardening effect is a common problem affecting the quantitative aspects of X-ray computed tomography (CT). We have developed two statistical reconstruction algorithms for poly-energetic X-ray CT that can effectively reduce the beam-hardening effect. Phantom tests were used to evaluate our approach in comparison with traditional correction methods. Unlike previous methods, our algorithm utilizes multiple energy-corresponding blank scans to estimate the attenuation map for a particular energy spectrum. Therefore, our algorithm is an energy-selective reconstruction. In addition to benefits over other statistical algorithms for poly-energetic reconstruction, our algorithm has the advantage of not requiring prior knowledge of the object material, the energy spectrum of the source and the energy sensitivity of the detector. The results showed an improvement in coefficient of variation, uniformity and signal-to-noise ratio; overall, this novel approach produces a better beam-hardening correction.

Computational analysis and improvement of SIRT

Iterative X-ray computed tomography (CT) algorithms have the potential for producing high-quality images but are computationally very demanding, especially when applied to high-resolution problems. Focusing on simultaneous iterative reconstruction technique (SIRT), we provide an eigenvalue based scheme for automatically determining a near-optimal value of the relaxation parameter. This accelerates the convergence rate of SIRT to the point where only half the number of iterations normally required is needed. We also modify the way SIRT uses preconditioning to solve a weighted least squares problem. The resulting algorithm, which we call PSIRT, is associated with a smaller memory footprint and calls for less data to be communicated in a distributed-memory implementation. Experimental residual norm and timing results are provided based on cone-beam micro-CT mouse data, including for an ordered subsets study.