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      Efficiency of Iterative Metal Artifact Reduction Algorithm (iMAR) Applied to Brain Volume Perfusion CT in the Follow-up of Patients after Coiling or Clipping of Ruptured Brain Aneurysms

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          Abstract

          Metal artifacts resulting from coiling or clipping of a brain aneurysm degrade image quality and reduce diagnostic usefulness of computed tomography perfusion CTP. Our aim was to assess the diagnostic value of the iterative metal artifact reduction algorithm (iMAR) in CTP studies after coiling or clipping of ruptured intracranial aneurysms. Fifty-eight CTP exams performed in 32 patients were analysed. iMAR was applied to the source images from the CT scanner. Perfusion maps were generated from datasets both with and without iMAR, and both datasets were compared qualitatively and quantitatively. Qualitative analysis included evaluation of intensity of artifacts, image quality, presence of new artifacts, and the reader’s confidence in their diagnosis as well as diagnostic impression. Quantitative analysis included evaluation of tissue attenuation curves, evaluation of region of interest (ROI)-based measurement of perfusion values at levels that do and do not contain metal, compared to previously published reference ranges of perfusion values. Our results showed that application of iMAR reduced artifacts and significantly improved image quality. New artifacts were observed adjacent to metallic implants, but did not limit the evaluation of other regions. After correction for artifact readers’ confidence in their diagnosis increased from 41.3% to 87.9%, and the diagnostic impression changed in 31% of the exams. No difference between tissue attenuation curves was found. For slices without metal, no difference was noted between values measured before and after iMAR, and the total number of ROIs in the reference range of perfusion values was unchanged. At the level of the metal implant, 89.85% of ROIs obtained before using iMAR showed calculation errors. After using iMAR, only 1.7% showed errors. Before iMAR 3.1% of values were in the reference range, whereas after iMAR this increased to 33.1%. In conclusion, our results show that iMAR is an excellent tool for reducing artifacts in CTP. It is therefore recommended for use in clinical practice, particularly when severe artifacts are present, or when hypoperfusion is suspected at the level of the coil or clip. After the application of iMAR, the perfusion values at the level of the metal can be better calculated, but may not lie within the reference range; therefore, quantitative analysis at the level of artifacts is not advisable.

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          Normalized metal artifact reduction (NMAR) in computed tomography.

          While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so-called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization. In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization. Promising results for clinical spiral cone-beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate. NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting-based MAR method.
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            Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography

            In the presence of metal implants, metal artifacts are introduced to x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this work, we develop a convolutional neural network (CNN) based open MAR framework, which fuses the information from the original and corrected images to suppress artifacts. The proposed approach consists two phases. In the CNN training phase, we build a database consisting of metal-free, metal-inserted and pre-corrected CT images, and image patches are extracted and used for CNN training. In the MAR phase, the uncorrected and pre-corrected images are used as the input of the trained CNN to generate a CNN image with reduced artifacts. To further reduce the remaining artifacts, water equivalent tissues in a CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal-affected projections, followed by the FBP reconstruction. The effectiveness of the proposed method is validated on both simulated and real data. Experimental results demonstrate the superior MAR capability of the proposed method to its competitors in terms of artifact suppression and preservation of anatomical structures in the vicinity of metal implants.
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              Current and Novel Techniques for Metal Artifact Reduction at CT: Practical Guide for Radiologists

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                Author and article information

                Contributors
                arsany_hakim@yahoo.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 December 2019
                19 December 2019
                2019
                : 9
                : 19423
                Affiliations
                [1 ]University Institute of Diagnostic and Interventional Neuroradiology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland
                [2 ]Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland
                [3 ]Department of Neurosurgery, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland
                Author information
                http://orcid.org/0000-0001-9431-1069
                http://orcid.org/0000-0002-5684-4419
                Article
                55792
                10.1038/s41598-019-55792-6
                6923436
                81ac2f38-6902-43ce-b646-585501f98c07
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 January 2019
                : 2 December 2019
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                brain imaging,medical research,cerebrovascular disorders
                Uncategorized
                brain imaging, medical research, cerebrovascular disorders

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