Jean-Baptiste Thibault

A three-dimensional statistical approach to improved image quality for multislice helical CT.

Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.

Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization

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 novel reconstruction algorithm to extend the CT scan field‐of‐view

For various reasons, a projection dataset acquired on a computed tomography (CT) scanner can be truncated. That is, a portion of the scanned object is positioned outside the scan field-of-view (SFOV) and the line integrals corresponding to those regions are not measured. A projection truncation problem causes imaging artifacts that lead to suboptimal image quality. In this paper, we propose a reconstruction algorithm that enables an adequate estimation of the projection outside the SFOV. We make use of the fact that the total attenuation of each ideal projection in a parallel sampling geometry remains constant over views. We use the magnitudes and slopes of the projection samples at the location of truncation to estimate water cylinders that can best fit to the projection data outside the SFOV. To improve the robustness of the algorithm, continuity constraints are placed on the fitting parameters. Extensive phantom and patient experiments were conducted to test the robustness and accuracy of the proposed algorithm.

Recent Advances in CT Image Reconstruction

Over the past two decades, rapid system and hardware development of x-ray computed tomography (CT) technologies has been accompanied by equally exciting advances in image reconstruction algorithms. The algorithmic development can generally be classified into three major areas: analytical reconstruction, model-based iterative reconstruction, and application-specific reconstruction. Given the limited scope of this chapter, it is nearly impossible to cover every important development in this field; it is equally difficult to provide sufficient breadth and depth on each selected topic. As a compromise, we have decided, for a selected few topics, to provide sufficient high-level technical descriptions and to discuss their advantages and applications.

Model-Based Iterative Reconstruction for Dual-Energy X-Ray CT Using a Joint Quadratic Likelihood Model

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.

A recursive filter for noise reduction in statistical iterative tomographic imaging

Computed Tomography (CT) screening and pediatric imaging, among other applications, demand the development of more efficient reconstruction techniques to diminish radiation dose to the patient. While many methods are proposed to limit or modulate patient exposure to x-ray at scan time, the resulting data is excessively noisy, and generates image artifacts unless properly corrected. Statistical iterative reconstruction (IR) techniques have recently been introduced for reconstruction of low-dose CT data, and rely on the accurate modeling of the distribution of noise in the acquired data. After conversion from detector counts to attenuation measurements, however, noisy data usually deviate from simple Gaussian or Poisson representation, which limits the ability of IR to generate artifact-free images. This paper introduces a recursive filter for IR, which conserves the statistical properties of the measured data while pre-processing attenuation measurements. A basic framework for inclusion of detector electronic noise into the statistical model for IR is also presented. The results are shown to successfully eliminate streaking artifacts in photon-starved situations.

TU‐A‐201B‐03: Dose Reduction and Image Quality Benefits Using Model Based Iterative Reconstruction (MBIR) Technique for Computed Tomography

Purpose: To demonstrate the image‐quality benefits and potential for significant dose reduction with Model‐Based Iterative Reconstruction (MBIR) technique incorporating physical model of computed tomography(CT) systems. Method and Materials: A model based iterative reconstruction (MBIR), a maximum a posteriori (MAP) estimate with edge‐preserving prior, has been developed for x‐ray CTimage reconstruction. It utilizes a more accurate physical model of the imaging chain accounting for system‐optics, noise and non‐idealities in the data, hence improves image quality compared to conventional filtered backprojection (FBP) at significantly reduced dose levels. In this work, a GE multi‐slice CT system was used to acquire a set of multi‐dose data and standard FBP reconstruction. For resolution assessment, a Catphan600® phantom was scanned at three dose levels (40, 20, and 10 mGy with 120kVp spectrum), and images were reconstructed using two methods: FBP with ASiR, and the MBIR. For artifact and image‐quality evaluations, an anthropomorphic CT abdomen phantom (Kyoto Kagaku Co., Ltd) was scanned at four dose levels (120kVp spectrum with 225, 112, 54, and 27 mAs), and a comparative image‐quality study between standard FBP and MBIR in slice and multi‐planar reformat (MPR) modes was made. In addition, few clinical case studies were also used to compare the imaging performance in actual clinical data. Results: From the resolution study, we found that even at 1/4th dose, MBIR images have improved resolution at significantly reduced noise compared to standard state‐of‐the‐art FBP with ASiR. Use of ASIR provides up to 50% dose reduction with equivalent FBP image‐quality. For anthropomorphic phantom, even below 1/8th dose, MBIR images outperformed the corresponding FBP images in both, slice and MPR modes, demonstrating immense potential for dose reduction, yet improved image quality, in clinical CT.Conclusion: Results of the MBIR method demonstrated significant potential for dose reduction and image‐quality improvements in clinical CT.

Block-based iterative coordinate descent

In the context of x-ray computed tomography (CT), the iterative coordinate descent (ICD) algorithm is a reconstruction algorithm that computes image updates on a voxel-by-voxel basis [1]. This algorithm in turn can form the basis of powerful model-based iterative reconstruction frameworks for CT reconstruction [2]. In this paper, we will explore a blockbased version of ICD (B-ICD) that computes an update for a block of N voxels simultaneously while accounting for the correlation among the N voxels. Previous studies investigating grouped updates in a coordinate descent (GCD) framework include updating a group of potentially correlated or coupled voxels using an under-relaxation factor that preserves convergence [3], [4]. For the B-ICD method, however, we form and solve a linear system corresponding to a block of voxels in which we directly account for the correlation. Using this framework, we can update highly correlated voxels whereas with GCD algorithms it is preferable in terms of the resultant relaxation factors to update voxels with little to no correlation.

Spatial resolution enhancement in CT iterative reconstruction

Iterative reconstruction (IR) has recently been proposed to improve multiple aspects of image quality over conventional filtered backprojection (FBP) in X-ray computed tomography (CT). FBP reconstruction and its corresponding reconstruction kernels have been optimized for decades to provide the best possible image quality. IR does not have the notion of reconstruction kernels but uses other mechanisms to change the image resolution and image noise. This paper presents one computationally efficient technique to enhance the spatial resolution of IR images reconstructed from high resolution scans, based on the introduction of an enlarged voxel footprint in the forward model, combined with a band-suppression filter designed to eliminate any undesirable over- or under-shoot artifacts that may arise from the use of the enlarged voxels. The proposed technique achieves higher spatial resolution than high resolution FBP with significantly lower noise. Results are shown on both phantom and clinical patient data.

Statistical x-ray computed tomography imaging from photon-starved measurements

Dose reduction in clinical X-ray computed tomography (CT) causes low signal-to-noise ratio (SNR) in photonsparse situations. Statistical iterative reconstruction algorithms have the advantage of retaining image quality while reducing input dosage, but they meet their limits of practicality when significant portions of the sinogram near photon starvation. The corruption of electronic noise leads to measured photon counts taking on negative values, posing a problem for the log() operation in preprocessing of data. In this paper, we propose two categories of projection correction methods: an adaptive denoising filter and Bayesian inference. The denoising filter is easy to implement and preserves local statistics, but it introduces correlation between channels and may affect image resolution. Bayesian inference is a point-wise estimation based on measurements and prior information. Both approaches help improve diagnostic image quality at dramatically reduced dosage.