Objective: The aim of this study was to evaluate commercial metal artefact reduction (MAR) techniques in X-ray CT imaging of hip prostheses. Methods: Monoenergetic reconstructions of dual-energy CT (DECT) data and several different MAR algorithms, combined with single-energy CT or DECT, were evaluated by imaging a bilateral hip prosthesis phantom. The MAR images were compared with uncorrected images based on CT number accuracy and noise in different regions of interest. Results: The three MAR algorithms studied implied a general noise reduction (up to 67%, 74% and 77%) and an improvement in CT number accuracy, both in regions close to the prostheses and between the two prostheses. The application of monoenergetic reconstruction, without any MAR algorithm, did not decrease the noise in the regions close to the prostheses to the same extent as did the MAR algorithms and even increased the noise in the region between the prostheses. Conclusion: The MAR algorithms evaluated generally improved CT number accuracy and substantially reduced the noise in the hip prostheses phantom images, both close to the prostheses and between the two prostheses. The study showed that the monoenergetic reconstructions evaluated did not sufficiently reduce the severe metal artefact caused by large orthopaedic implants. Advances in knowledge: This study evaluates several commercially available MAR techniques in CT imaging of large orthopaedic implants.Images degraded by metal artefacts are a common problem in X-ray CT imaging. Artefacts caused by the presence of metallic implants in the CT scanned volume, such as hip prostheses or dental fillings, appear as dark and bright streaks across the reconstructed image. Metal artefacts can severely degrade the image quality and hence limit the diagnostic value of a CT scan. 1 Hip prostheses cause severe artefacts when present in a CT scanned volume, and the resulting degradation of image quality leads to difficulties in diagnosing fractures, implant loosening or pathology in organs or soft tissue in the pelvic area. If CT images containing hip prostheses are used in radiotherapy treatment planning for tissue heterogeneity correction, the metal artefacts may introduce inaccuracies in dose calculations.Metal artefacts in CT imaging are mainly caused by beam hardening and photon starvation. Photon starvation artefacts are created when X-rays traverse materials with high attenuation coefficients, which leads to an insufficient amount of photons reaching the detectors and results in very noisy projections. The noise is magnified in the reconstruction process and the resulting streaks can be seen in the reconstructed image. Beam hardening refers to the fact that low-energy photons are attenuated to a greater degree than high-energy photons when passing through the scanned volume. This effect is more pronounced when the X-ray beam passes through high-density materials such as metals.

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The purpose of this study was to evaluate the accuracy and reproducibility of phase contrast imaging using the sensitivity encoding (SENSE) method at different reduction factors. Analytical expressions were derived that state how reproducibility is influenced for velocity and flow measurements. Computer simulations, and in vitro and in vivo studies were performed in order to validate these expressions and to assess how accuracy is affected when different reduction factors are applied. It was shown that reproducibility depends on the reduction and geometry factors. Since the geometry factor varies spatially, so does the reproducibility for phase contrast imaging. In areas with high geometry factors, the standard deviation (SD) may become so large that aliasing occurs. The accuracy of phase contrast imaging is not influenced directly when SENSE is used, but may be indirectly influenced due to high SDs of the measured phase that may subsequently cause aliasing. The current results show that it is possible to achieve accurate flow measurements even at high reduction factors. By taking the geometry factor into account, it may be possible to find areas where phase contrast imaging is accurate even at high reduction factors. The development of parallel acquisition strategies in magnetic resonance imaging (MRI) has created a potential for dramatic decreases in scan times. Several approaches for reconstructing images from individual coil elements have been proposed, based on combining the signal from individual coil elements in either the Fourier (1,2) or spatial (3,4) domain. The sensitivity encoding (SENSE) method has been shown to be a robust approach to image reconstruction (5), and has been implemented in many different pulse sequences, including non-Cartesian sampling patterns (6). With SENSE, the reduction in scan time can be selected depending on the number of coils used for parallel data acquisition. The SENSE method can also be used to suppress pulsation artifacts, with the same scan times as used in conventional acquisition methods (7).Phase contrast imaging is an established method that allows the velocities of moving spins to be measured (8 -12), and is widely used to quantify blood flow (13) and cardiac motion (14,15). The accuracy and reproducibility of phase contrast imaging have been thoroughly investigated, and a number of factors that influence the measured phase values have been identified (16). With compensation for these known artifacts, the phase contrast method has been shown to be very accurate (12,17,18). The acquisition time of phase contrast imaging can be relatively long, however, especially when all three velocity components are measured. Significant reductions in scan time would be highly useful, and may be achieved by using SENSE in combination with phase contrast imaging.The aim of this study was to determine what impact the use of SENSE at different reduction factors would have on the accuracy and reproducibility of phase contrast imaging. Computer simulations, and in vitro and in vivo s...

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O-MAR and iMAR significantly improved image quality in terms of anatomical visualization for target and OAR delineation in dental implant patient images. WET calculations along several directions, all outside the metallic regions, showed that both uncorrected and MAR images contained metal artifacts which could potentially lead to unacceptable errors in proton treatment planning. ΔWET was reduced by MAR in some areas, while increased or unchanged deviations were seen for other path directions. The proton treatment plans created for the phantom images showed overall acceptable dose distributions differences when compared to the reference cases, both for the uncorrected and MAR images. However, substantial dose distribution differences in the areas of most severe artifacts were seen for some plans, which were reduced by MAR in some cases but not all. In conclusion, MAR could be beneficial to use for proton treatment planning; however, case-by-case evaluations of the metal artifact-degraded images are always recommended.

Qualitative evaluation of the usefulness of several MAR techniques from different vendors in CT imaging of hip prosthesis.

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.

In this paper, we introduce a method for identification of bacteria in human blood culture samples using an electronic nose. The method uses features, which capture the static (steady state) and dynamic (transient) properties of the signal from the gas sensor array and proposes a means to ensemble results from consecutive samples. The underlying mechanism for ensembling is based on an estimation of posterior probability, which is extracted from a support vector machine classifier. A large dataset representing ten different bacteria cultures has been used to validate the presented methods. The results detail the performance of the proposed algorithm and show that through ensembling decisions on consecutive samples, significant reliability in classification accuracy can be achieved.