The purpose of this study was to develop, implement and evaluate a dedicated metal artefact reduction (MAR) method for flat-detector CT (FDCT). The algorithm uses the multidimensional raw data space to calculate surrogate attenuation values for the original metal traces in the raw data domain. The metal traces are detected automatically by a three-dimensional, threshold-based segmentation algorithm in an initial reconstructed image volume, based on twofold histogram information for calculating appropriate metal thresholds. These thresholds are combined with constrained morphological operations in the projection domain. A subsequent reconstruction of the modified raw data yields an artefact-reduced image volume that is further processed by a combining procedure that reinserts the missing metal information. For image quality assessment, measurements on semi-anthropomorphic phantoms containing metallic inserts were evaluated in terms of CT value accuracy, image noise and spatial resolution before and after correction. Measurements of the same phantoms without prostheses were used as ground truth for comparison. Cadaver measurements were performed on complex and realistic cases and to determine the influences of our correction method on the tissue surrounding the prostheses. The results showed a significant reduction of metal-induced streak artefacts (CT value differences were reduced to below 22 HU and image noise reduction of up to 200%). The cadaver measurements showed excellent results for imaging areas close to the implant and exceptional artefact suppression in these areas. Furthermore, measurements in the knee and spine regions confirmed the superiority of our method to standard one-dimensional, linear interpolation.

Metallic implants are responsible for various artifacts in flat-detector computed tomography visible as streaks and dark areas in the reconstructed volumetric images. In this paper a novel method for a fast reduction of these metal artifacts is presented using a three-step correction procedure to approximate the missing parts of the raw data. In addition to image quality aspects, this paper deals with the problem of high correction latencies by proposing a reconstruction and correction framework, that utilizes the massive computational power of graphics processing units (GPUs). An initial volume is reconstructed, followed by a 3-dimensional metal voxel segmentation algorithm. These metal voxels allow us to identify metal-influenced detector elements by using a simplified geometric forward projection. Consequently, these areas are corrected using a 3D interpolation scheme in the raw data domain, followed by a second reconstruction. This volume is then segmented into three materials with respect to bone structures using a threshold-based algorithm. A forward projection of the obtained tissueclass model substitutes missing or corrupted attenuation values for each detector element affected by metal and is followed by a final reconstruction. The entire process including the initial reconstruction, takes less than a minute (5123 volume with 496 projections of size 1240x960) and offers significant improvements of image quality. The method was evaluated with data from two FD-CT C-arm systems (Artis Zee and Artis Zeego, Siemens Healthcare, Forchheim, Germany).

PURPOSEDue to x-ray beam polychromaticity and scattered radiation, attenuation measurements tend to be underestimated. Cupping and beam hardening artifacts become apparent in the reconstructed CT images. If only one material such as water, for example, is present, these artifacts can be reduced by precorrecting the rawdata. Higher order beam hardening artifacts, as they result when a mixture of materials such as water and bone, or water and bone and iodine is present, require an iterative beam hardening correction where the image is segmented into different materials and those are forward projected to obtain new rawdata. Typically, the forward projection must correctly model the beam polychromaticity and account for all physical effects, including the energy dependence of the assumed materials in the patient, the detector response, and others. We propose a new algorithm that does not require any knowledge about spectra or attenuation coefficients and that does not need to be calibrated. The proposed method corrects beam hardening in single energy CT data.METHODSThe onlya priori knowledge entering EBHC is the segmentation of the object into different materials. Materials other than water are segmented from the original image, e.g., by using simple thresholding. Then, a (monochromatic) forward projection of these other materials is performed. The measured rawdata and the forward projected material-specific rawdata are monomially combined (e.g., multiplied or squared) and reconstructed to yield a set of correction volumes. These are then linearly combined and added to the original volume. The combination weights are determined to maximize the flatness of the new and corrected volume. EBHC is evaluated using data acquired with a modern cone-beam dual-source spiral CT scanner (Somatom Definition Flash, Siemens Healthcare, Forchheim, Germany), with a modern dual-source micro-CT scanner (TomoScope Synergy Twin, CT Imaging GmbH, Erlangen, Germany), and with a modern C-arm CT scanner (Axiom Artis dTA, Siemens Healthcare, Forchheim, Germany). A large variety of phantom, small animal, and patient data were used to demonstrate the data and system independence of EBHC.RESULTSAlthough no physics apart from the initial segmentation procedure enter the correction process, beam hardening artifacts were significantly reduced by EBHC. The image quality for clinical CT, micro-CT, and C-arm CT was highly improved. Only in the case of C-arm CT, where high scatter levels and calibration errors occur, the relative improvement was smaller.CONCLUSIONSThe empirical beam hardening correction is an interesting alternative to conventional iterative higher order beam hardening correction algorithms. It does not tend to over- or undercorrect the data. Apart from the segmentation step, EBHC does not require assumptions on the spectra or on the type of material involved. Potentially, it can therefore be applied to any CT image.

Ring artifacts often appear in flat-detector CT because of imperfect or defect detector elements or calibration. In high-spatial resolution CT images reducing such artifacts becomes a necessity. In this paper, we used the post-processing ring correction in polar coordinates (RCP)1 to eliminate the ring artifacts. The median filter is applied to the uncorrected images in polar coordinates and ring artifacts are extracted from the original images. The algorithm has a very high computational cost due to the time-expensive median filtering and coordinate transformation on CPUs. Graphics processing units (GPUs)ca n be seen as parallel co-processors with high computational power. All steps of the RCP algorithm were implemented with CUDA2(Compute Unified Device Architecture, NVIDIA). We introduced a new GPU-based branchless vectorized median (BVM)filter. 3, 4 This algorithm is based on minmax sorting and keeps track of a sorted array from which values are deleted and to which new values are inserted. For comparison purpose a modified pivot median filter5 on GPUs was presented, which compares a pivot element to all other values and recursively finds the median element. We evaluated the performance of the RCP method using 512 slices, each slice consisted of 512×512 pixels. This post-processing method efficiently reduces ring artifacts in the reconstructed images and improves image quality. Our CUDAbased RCP is up to 13.6 times faster than the optimized CPU-based (single core)r outine. Comparing our two GPU-based median filters showed a performance benefit by roughly 60% when switching from Pivot to BVM code. The main reason is that the BVM algorithm is branchless and makes use of data-level parallelism. The BVM method is better suited to the model of modern graphics processing. A multi-GPU solution showed that the performance scaled nearly linearly.

PURPOSE:Metal-induced artifacts may cause severe problems in clinical computed tomography (CT) imaging and may impair diagnosis as well as overall image quality. Many approaches for reducing these artifacts tackle the problem by simply ignoring or interpolating the metal traces in the raw data, which results in a general information loss and additional artifacts in the corrected image. It was the objective of this study to develop an approach aiming at correcting several physical artifact sources. We have also tried to minimize the impact on spatial resolution and attempted to avoid new artifacts resulting from the correction.MATERIALS AND METHODS:The algorithm works with a first volumetric reconstruction followed by threshold-based metal prostheses segmentation. The segmented metal implants are then forward projected and the resulting sinogram entries are squared and combined, followed by a second reconstruction to yield correction volumes. The resulting volumes are then combined linearly with a combination weight determined to minimize the flatness of the initial image. A directional filtering algorithm following the beam hardening correction applies a nonlinear convolution in the metal traces of the sinogram which reduces existing metal-induced noise artifacts. Phantom measurements on a polyethylene (PE) disc with different inserts and a semi-anthropomorphic hip phantom with optional bone and titanium inserts were used for evaluating the algorithm. Patient datasets containing uni- and bilateral hip endoprostheses verified the applicability and efficiency on realistic clinical cases.RESULTS:Deviations in CT values were reduced to below 3 HU on average. Image noise reduction of up to 70% was achieved (average noise reduction of 37%) with a more homogeneous CT value distribution in soft-tissue areas. A comparison to standard interpolation methods showed superior artifact suppression without producing artifacts caused by interpolation and without the general information loss in the close vicinity to the implants. The impact on spatial resolution was minimized as compared with interpolation algorithms.CONCLUSIONS:Metal artifacts caused by hip-endoprostheses were strongly reduced. Soft tissue areas and skeletal structures surrounding the implants were well restored. The correction works by postprocessing CT datasets and it is applicable to any reconstructed image without a priori knowledge.

The aim of this study was an image quality evaluation for a state-of-the-art robot-driven C-arm CT system. CT values, noise and homogeneity as well as the spacial resolution in three dimensions were evaluated for different pixel binning and acquisition modes. A new feature for imaging large volumes (LV scans) which exceed the field of view of 24 cm in diameter was evaluated and compared in terms of image quality and resolution to standard FD-CT imaging with the same system. Standard FD-CT imaging achieved a maximum resolution of 3.5 lp/mm (10 % MTF value) for 1 × 1 pixel binning. Spacial resolution for LV scans was reduced by about 10%. Mean CT values in water (ideally corresponding to 0 HU) were 17 HU with a standard deviation of 22 HU. Homogeneity calculated to -11 and -8 HU for 220° partial scans and 360° full scans, respectively. Contrast evaluation showed a clear detectability of details of 30 HU contrast and 4 mm diameter (at CTDIW = 48.6 mGy, slice thickness of 0.48 mm). LV scans showed comparable homogeneity and low-contrast detectability. In conclusion, the robot-driven C-arm CT system offered the same performance as standard FD-CT.

BACKGROUND AND PURPOSE:Metallic implants induce massive artifacts in CT images which deteriorate image quality and often superimpose structures of interest. The purpose of this study was to apply and evaluate a dedicated MAR method for neuroradiologic intracranial clips and detachable platinum coiling events. We here report the first clinical results for MAR in FDCT.MATERIALS AND METHODS:FDCT volume scans of several patients treated with endovascular coiling or intracranial clipping were corrected by using a dedicated FDCT MAR correction algorithm combined with an edge-preserving attenuation-normalization method in the projection space. Corrected and uncorrected images were compared by 2 experienced radiologists and evaluated for several image-quality features.RESULTS:After application of our algorithm, implant delineation and visibility were highly improved. CT values compared with values in metal artifact-unaffected areas showed good agreement (average correction of 1300 HU). Image noise was reduced overall by 27%. Intracranial hemorrhage in the direct surroundings of the implanted coil or clip material was displayed without worrisome metal artifacts, and our algorithm even allowed diagnosis in areas where extensive information losses were seen. The high spatial resolution provided by FDCT imaging was well preserved.CONCLUSIONS:Our MAR method provided metal artifact-reduced images in every studied case. It reduced image noise and corrected CT values to levels comparable with images measured without metallic implants. An overall improvement of brain tissue modeling and implant visibility was achieved. MAR in neuroradiologic FDCT imaging is a promising step forward for better image quality and diagnosis in the presence of metallic implants.

In high-resolution micro CT using flat detectors (FD), imperfect or defect detector elements may cause concentric-ring artifacts due to their continuous over- or underestimation of attenuation values, which often disturb image quality. We here present a dedicated image-based ring artifact correction method for high-resolution micro CT, based on median filtering of the reconstructed image and working on a transformed version of the reconstructed images in polar coordinates. This post-processing method reduced ring artifacts in the reconstructed images and improved image quality for phantom and in in vivo scans. Noise and artifacts were reduced both in transversal and in multi-planar reformations along the longitudinal axis.

In flat-detector CT, imperfect or defect detector elements may cause concentric ring artifacts due to their continuous over- or underestimation of attenuation values, which often disturb image quality. Especially due to the demand for high-spatial resolution images and the necessary pixel read-out without arbitrary pixel-binning, ring artifacts become more pronounced and the reduction of these artifacts becomes a necessity. We here present a comparison of two dedicated ring artifact correction methods for flat-detector CT, on the basis of different median and mean filterings of the reconstructed image but each working in different geometric planes. While the first method works in Cartesian coordinates, the second method performs a transformation to polar coordinates. Both post-processing methods efficiently reduce ring artifacts in the reconstructed images and improve image quality. The transformation to polar coordinates turned out to be a necessary step for efficient ring artifact correction, since correction in Cartesian coordinates suffers from newly introduced artifacts as well as insufficient correction of artifacts close to the center of rotation.

Metallic implants generate streak-like artifacts in flat-detector computed tomography (FD-CT) reconstructed volumetric images. This study presents a novel method for reducing these disturbing artifacts by inserting discarded information into the original rawdata using a three-step correction procedure and working directly with each detector element. Computation times are minimized by completely implementing the correction process on graphics processing units (GPUs). First, the original volume is corrected using a three-dimensional interpolation scheme in the rawdata domain, followed by a second reconstruction. This metal artifact-reduced volume is then segmented into three materials, i.e. air, soft-tissue and bone, using a threshold-based algorithm. Subsequently, a forward projection of the obtained tissue-class model substitutes the missing or corrupted attenuation values directly for each flat detector element that contains attenuation values corresponding to metal parts, followed by a final reconstruction. Experiments using tissue-equivalent phantoms showed a significant reduction of metal artifacts (deviations of CT values after correction compared to measurements without metallic inserts reduced typically to below 20 HU, differences in image noise to below 5 HU) caused by the implants and no significant resolution losses even in areas close to the inserts. To cover a variety of different cases, cadaver measurements and clinical images in the knee, head and spine region were used to investigate the effectiveness and applicability of our method. A comparison to a three-dimensional interpolation correction showed that the new approach outperformed interpolation schemes. Correction times are minimized, and initial and corrected images are made available at almost the same time (12.7 s for the initial reconstruction, 46.2 s for the final corrected image compared to 114.1 s and 355.1 s on central processing units (CPUs)).