Suppression of intensity transition artifacts in statistical x-ray computer tomography reconstruction through Radon inversion initialization

Modelling the physics in the iterative reconstruction for transmission computed tomography

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.

Modelling the physics in the iterative reconstruction for transmission computed tomography

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.

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.

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.

Comparison of methods for suppressing edge and aliasing artefacts in iterative x-ray CT reconstruction

X-ray CT images obtained with iterative reconstruction (IR) can be hampered by the so-called edge and aliasing artefacts, which appear as interference patterns and severe overshoots in the areas of sharp intensity transitions. Previously, we have demonstrated that these artefacts are caused by discretization errors during the projection simulation step in IR. Although these errors are inherent to IR, they can be adequately suppressed by reconstruction on an image grid that is finer than that typically used for analytical methods such as filtered back-projection. Two other methods that may prevent edge artefacts are: (i) smoothing the projections prior to reconstruction or (ii) using an image representation different from voxels; spherically symmetric Kaiser-Bessel functions are a frequently employed example of such a representation. In this paper, we compare reconstruction on a fine grid with the two above-mentioned alternative strategies for edge artefact reduction. We show that the use of a fine grid results in a more adequate suppression of artefacts than the smoothing of projections or using the Kaiser-Bessel image representation.

Cardiac cone-beam CT volume reconstruction using ART

Modern computed tomography systems allow volume imaging of the heart. Up to now, approximately two-dimensional (2D) and 3D algorithms based on filtered backprojection are used for the reconstruction. These algorithms become more sensitive to artifacts when the cone angle of the x-ray beam increases as it is the current trend of computed tomography (CT) technology. In this paper, we investigate the potential of iterative reconstruction based on the algebraic reconstruction technique (ART) for helical cardiac cone-beam CT. Iterative reconstruction has the advantages that it takes the cone angle into account exactly and that it can be combined with retrospective cardiac gating fairly easily. We introduce a modified ART algorithm for cardiac CT reconstruction. We apply it to clinical cardiac data from a 16-slice CT scanner and compare the images to those obtained with a current analytical reconstruction method. In a second part, we investigate the potential of iterative reconstruction for a large area detector with 256 slices. For the clinical cases, iterative reconstruction produces excellent images of diagnostic quality. For the large area detector, iterative reconstruction produces images superior to analytical reconstruction in terms of cone-beam artifacts.