An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.

/pdf/image-reconstruction-in-circular-cone-beam-computed-4wndbkpv6w.pdf

Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization

The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography.

Object reconstruction from a series of projection images, such as in computed tomography (CT), is a popular tool in many different application fields. Existing commercial software typically provides sufficiently accurate and convenient-to-use reconstruction tools to the end-user. However, in applications where a non-standard acquisition protocol is used, or where advanced reconstruction methods are required, the standard software tools often are incapable of computing accurate reconstruction images. This article introduces the ASTRA Toolbox. Aimed at researchers across multiple tomographic application fields, the ASTRA Toolbox provides a highly efficient and highly flexible open source set of tools for tomographic projection and reconstruction. The main features of the ASTRA Toolbox are discussed and several use cases are presented.

Fast and flexible X-ray tomography using the ASTRA toolbox.

Iterative reconstruction methods in X-ray CT

For a given radiation dose, virtual monochromatic spectral images at approximately 70 keV reconstructed from split 80- and 140-kVp data showed less image noise and a higher contrast-to-noise ratio than did 120-kVp CT images.

Virtual monochromatic spectral imaging with fast kilovoltage switching: Improved image quality as compared with that obtained with conventional 120-kVp CT

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.

Computational Analysis and Improvement of SIRT

Recently there has been significant interest in dual energy CT imaging with several acquisition methods being
actively pursued. Here we investigate fast kVp switching where the kVp alternates between low and high kVp
every view. Fast kVp switching enables fine temporal registration, helical and axial acquisitions, and full field
of view. It also presents several processing challenges. The rise and fall of the kVp, which occurs during the
view integration period, is not instantaneous and complicates the measurement of the effective spectrum for low
and high kVp views. Further, if the detector digital acquisition system (DAS) and generator clocks are not fully
synchronous, jitter is introduced in the kVp waveform relative to the view period.
In this paper we develop a method for estimation of the resulting spectrum for low and high kVp views. The
method utilizes static kVp acquisitions of air with a small bowtie filter as a basis set. A fast kVp acquisition of
air with a small bowtie filter is performed and the effective kVp is estimated as a linear combination of the basis
vectors. The effectiveness of this method is demonstrated through the reconstruction of a water phantom acquired
with a fast kVp acquisition. The impact of jitter due to the generator and detector DAS clocks is explored via
simulation. The error is measured relative to spectrum variation and material decomposition accuracy.

Dual energy CT via fast kVp switching spectrum estimation

A method and apparatus include acquisition of a view dataset based on x-rays received by a detector corresponding to a energy level, reconstruction of an initial image using the view dataset, the initial image comprising a plurality of metal voxels at respective metal voxel locations, and generation of a metal mask corresponding to the plurality of metal voxels within the initial image. The method and apparatus also include forward projection of the metal mask onto the view dataset to identify metal dexels in the view dataset, performance of a weighted interpolation based on the identified metal dexels to generate a completed view dataset, reconstruction of a final image using the completed view dataset, the final image comprising a plurality of image voxels corresponding to the metal voxel locations, and replacement of a portion of the plurality of image voxels corresponding to the metal voxel locations with smoothed metal values.

Method and apparatus for reduction of metal artifacts in CT images

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.

Block-based iterative coordinate descent

Reducing radiation dose associated with X-ray computed tomography (CT) scans is of key clinical interest. As such, there is active research in methods of generating diagnostically meaningful images using lower dose data, including data pre-processing and advanced reconstruction algorithms. When developing such procedures, it would be ideal from a benchmarking perspective to have multiple acquisitions at varying dose levels or protocols. While such data can be reasonably acquired for phantoms, comparable clinical data is less available. Therefore, it would be very beneficial to develop a method of synthetically adding noise to existing scans in such a way that the resulting data sets yield reasonably accurate noise realizations. We present such a method in this paper that synthetically adds noise directly in the raw data domain (i.e., before the negative log and associated preprocessing).

Synthetic CT noise emulation in the raw data domain

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