Performance of PROPELLER FSE T2WI in Reducing Metal Artifacts for Patients with Various Material Porcelain Fused To Metal Crown: A Preliminary Study

This study aimed to compare MRI quality between common fast spin echo T2 weighted imaging (FSE T2WI) with periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) FSE T2WI for patients with various porcelain fused to metal (PFM) crown and analyze the value of PROPELLER technique in reducing metal artifacts. Common FSE T2WI and PROPELLER FSE T2WI sequences for axial imaging of head were applied in participants with different PFM crowns: cobalt-chromium (Co-Cr) alloy, pure titanium (Ti), gold-palladium (Au-Pd) alloy. Two radiologists evaluated overall image quality of section in PFM using a 5-point scale qualitatively and measured the maximum artifact area and artifact signal-to-noise ratio (SNR) quantitatively. The metal crown with the least artifacts and the optimum image quality shown in common FSE T2WI and PROPELLER FSE T2WI were in Au–Pd alloy, Ti, and Co–Cr alloy order. PROPELLER FSE T2WI was superior to common FSE T2WI in improving image quality and reducing artifact area for Co-Cr alloy (17.0±0.2% smaller artifact area, p＜0.001) and Ti (11.6± 0.7 % smaller artifact area, p=0.005), but had similar performance compared to FSE T2WI for Au-Pd alloy. For all PFMs, PROPELLER FSE T2WI significantly reduced the signal-to-noise ratio (SNR) of artifact (393.57±89.75 VS. 214.05±70.45, p < 0.001) when compared to common FSE T2WI.Therefore, the different PFM crown generate varying degrees of metal artifacts in MRI, and the PROPELLER can effectively reduce metal artifacts especially in the PFM crown of Co-Cr alloy.

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Metal objects in X-ray computed tomography can cause severe artifacts. The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods. This paper proposes a projection-domain algorithm to reduce the metal artifacts. In this algorithm, the unknowns are the metal-affected projections, while the objective function is set up in the image domain. The data fidelity term is not utilized in the objective function. The objective function of the proposed algorithm consists of two terms: the total variation of the metal-removed image and the energy of the negative-valued pixels in the image. After the metal-affected projections are modified, the final image is reconstructed via the filtered backprojection algorithm. The feasibility of the proposed algorithm has been verified by real experimental data.

End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction

Metal artifacts are common in CT-guided interventions due to the presence of metallic instruments. These artifacts often obscure clinically relevant structures, which can complicate the intervention. In this work, we present a deep learning CT reconstruction called iCTU-Net for the reduction of metal artifacts. The network emulates the filtering and back projection steps of the classical filtered back projection (FBP). A U-Net is used as post-processing to refine the back projected image. The reconstruction is trained end-to-end, i.e., the inputs of the iCTU-Net are sinograms and the outputs are reconstructed images. The network does not require a predefined back projection operator or the exact X-ray beam geometry. Supervised training is performed on simulated interventional data of the abdomen. For projection data exhibiting severe artifacts, the iCTU-Net achieved reconstructions with SSIM = 0.970±0.009 and PSNR = 40.7±1.6. The best reference method, an image based post-processing network, only achieved SSIM = 0.944±0.024 and PSNR = 39.8±1.9. Since the whole reconstruction process is learned, the network was able to fully utilize the raw data, which benefited from the removal of metal artifacts. The proposed method was the only studied method that could eliminate the metal streak artifacts.

Analysis of objective quality metrics in computed tomography images affected by metal artifacts

Image quality (IQ) assessment plays an important role in the medical world. New methods to evaluate image quality have been developed, but their application in the context of computer tomography is yet limited. In this paper the performance of fifteen well-known full reference (FR) IQ metrics is compared with human judgment using images affected by metal artifacts and processed with metal artifact reduction methods from a phantom. Five region of interest with different sizes were selected. IQ was evaluated by seven experienced radiologists completely blinded to the information. To measure the correlation between FR-IQ, and the score assigned by radiologists non-parametric Spearman rank-order correlation coefficient and Kendall’s Rank-order Correlation coefficient were used; so as root mean square error and the mean absolute error to measure the prediction accuracy. Cohen’s kappa was employed with the purpose of assessing inter-observer agreement. The metrics GMSD, IWMSE, IWPSNR, WSNR and OSS-PSNR were the best ranked. Inter-observer agreement was between 0.596 and 0.954, with p<0.001 in all study. The objective scores predicted by these methods correlate consistently with the subjective evaluations. The application of this metrics will make possible a better evaluation of metal artifact reduction algorithms in future works.

Projection-to-image transform frame: a lightweight block reconstruction network (LBRN) for computed tomography

To solve the problem of learning based computed tomography (CT) reconstruction, several reconstruction networks were invented. However, applying neural network to tomographic reconstruction still remains challenging due to unacceptable memory space requirement. In this study, we presents a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which, respectively, correspond to the filter and back-projection of FBP method. The first module of LBRN decouples the relationship of Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection module, can use the block reconstruction strategy. Due to each image block is only connected with part filtered projection data, the network structure is greatly simplified and the parameters of the whole network is dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest (ROI), metal artifacts reduction and real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.

Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images

In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, which, however, lead to secondary artifacts. This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at each angle, improving the accuracy of the complemented sinogram data. Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction results and various evaluation metrics reveal that the proposed method can accurately repair missing data and reduce metal artifacts in reconstructed CT images.

NEW FINDS OF THE KOBAN-KOLCHIS METAL ARTIFACTS IN THE NORTHEASTERN BLACK SEA COASTLINE AREA (KRASNODAR REGION). EARLIER UNKNOWN AREA OF THE KOBAN-KOLCHIS BRONZES

В статье вводятся в научный оборот случайные находки последних десятилетий из района города-курорта Сочи и прилегающей части Туапсинского района, относящиеся к периоду поздней бронзы - раннего железа. До недавнего времени этот регион оставался своего рода «белым пятном» на археологической карте. Культурная принадлежность населения этого региона для рассматриваемого периода также оставалась неясной. Представленная коллекция случайных находок относится к нескольким хронологическим горизонтам - кон. II тыс. до н. э., VIII-VII вв. до н. э., VI-IV вв. до н. э. Для VIII-IV вв. до н. э. имеются определенные основания предполагать существование на этой территории самостоятельной археологической культуры в рамках кобано-колхидской культурно-исторической общности. Культура эта характеризуется синкретичностью, наличием как ярких кобано-колхидских, так и протомеотских и, в дальнейшем, меотских черт. Рассматривать этот регион как контактную зону представляется затруднительным из-за наличия некоторых ярких культурных маркеров, а именно слабо представленных в соседних ареалах бронзовых дуговидных фибул с кольцевыми утолщениями по краям дужки. Уверенно выделить новую, своеобразную культуру Восточного Причерноморья раннего железного века можно будет только после проведения новых широкомасштабных археологических исследований. The paper introduces into scientific discourse chance finds of recent decades from the district of the Sochi resort-city and the adjacent part of the Tuapse district dating to the Late Bronze Age - Early Iron Age. Until recently, this region remained something of a ‘blank spot’ on the archaeological map. Cultural attribution of the population in this region also remained unclear. The published assemblage of chance finds is dated to several chronological horizons: late II mill. BC, 8th-th cc. BC, 6th-4th cc. BC. Regarding the 8th-4th cc. BC, there are grounds to believe that bearers of a distinctive archaeological culture forming part of the overall Koban-Kolchis cultural unity inhabited this area. The culture is characterized by syncretism and presence of both salient Koban-Kolchis features and proto-Maeotian features and, subsequently, Maeotian features. It is difficult to consider this region as a contact zone due to presence of some impressive cultural markers, namely, bronze arched fibulae with ring thickened parts along the hoop. It will be possible to single out a distinctive Early Iron Age culture of the eastern Black Sea coastline region only after large-scale archaeological excavations and research.

Reduction of metal artifacts from knee tumor prostheses on CT images: value of the single energy metal artifact reduction (SEMAR) algorithm

Background To evaluate the effect of the single energy metal artifact reduction (SEMAR) algorithm with a multidetector CT (MDCT) for knee tumor prostheses. Methods First, a phantom of knee tumor prosthesis underwent a MDCT scan. The raw data was reconstructed by iterative reconstruction (IR) alone and IR plus SEMAR. The mean value of the CT number and the image noise were measured around the prosthesis at the stem level and articular level. Second, 95 consecutive patients with knee tumor prostheses underwent MDCT scans. The raw data were also reconstructed by the two methods. Periprosthetic structures were selected at the similar two levels. Four radiologists visually graded the image quality on a scale from 0 to 5. Additionally, the readers also assessed the presence of prosthetic complication and tumor recurrence on a same scale. Results In the phantom, when the SEMAR was used, the CT numbers were closer to normal value and the noise of images using soft and sharper kernel were respectively reduced by up to 77.1% and 43.4% at the stem level, and by up to 82.2% and 64.5% at the articular level. The subjective scores increased 1 ~ 3 points and 1 ~ 4 points at the two levels, respectively. Prosthetic complications and tumor recurrence were diagnosed in 66 patients. And the SEMAR increased the diagnostic confidence of prosthetic complications and tumor recurrence (4 ~ 5 vs. 1 ~ 1.5). Conclusions The SEMAR algorithm can significantly reduce the metal artifacts and increase diagnostic confidence of prosthetic complications and tumor recurrence in patients with knee tumor prostheses.