The Effectiveness of a Smart Metal Artifact Reduction Technique in Chest CT of Patients with Cardiac Implantable Electronic Devices

The Effectiveness of a Smart Metal Artifact Reduction Technique in Chest CT of Patients with Cardiac Implantable Electronic Devices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effectiveness of a Smart Metal Artifact Reduction Technique in Chest CT of Patients with Cardiac Implantable Electronic Devices Özgür Genç, Serap Baş This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7842222/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To evaluate whether Smart Metal Artifact Reduction (SMAR) used in combination with adaptive statistical iterative reconstruction (ASIR) improves chest CT image quality in patients with cardiac implantable electronic devices (CIEDs), compared with ASIR alone. Methods In this retrospective study, 30 CIED patients who underwent chest CT with both ASIR and ASIR + SMAR reconstructions were included. Two readers (16 and 10 years of chest CT experience) assessed subjective image quality and artifacts on soft-tissue, bone, and lung windows; objective artifact load was quantified using the standard deviation of Hounsfield Units in regions of interest adjacent to the generator. Scans were acquired on a single-source 512-slice CT system with standard thoracic parameters; SMAR reconstructions used vendor “thorax” settings. Results ASIR + SMAR reduced peri-device artifact magnitude relative to ASIR alone on objective measurements and improved overall diagnostic image quality across evaluated windows (average of two readers). However, far-field lung streaks were observed in 13 out of 30 patients, and in 5 cases, MAR-related artifacts limited assessment, indicating distance-dependent effects. Conclusion SMAR complements ASIR by enhancing depiction of the device and near-to mid-field regions, increasing diagnostic confidence. Radiologists should review ASIR-only images for distant lung parenchyma, where occasional MAR-induced streaks may limit evaluation. artifact pacemaker CT thorax reconstruction image processing image enhancement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiac implantable electronic devices (CIEDs) are increasingly used for the management of rhythm disorders and cardiac dysfunctions. As life expectancy rises, the number of implanted devices continues to grow each year. These systems typically consist of a pulse generator placed beneath the skin in the upper chest and one or more leads extending through the veins into the heart muscle. Despite the development of magnetic resonance imaging models, computed tomography (CT) remains a key imaging modality for thoracic evaluation, providing detailed visualization of the lungs, mediastinum, heart, and skeletal structures. However, the metallic components of the generator can produce image artifacts that obscure adjacent anatomical regions and reduce diagnostic accuracy. Metal artifacts arise as a combination of beam-hardening, photon starvation, and scatter artifacts [ 1 , 2 ]. To overcome metallic artifacts, basic strategies include increasing the tube voltage and current, reducing the detector size, using narrow collimation, employing iterative image reconstructions, using a soft reconstruction kernel instead of a sharp kernel, and reconstructing images from acquired raw data with thicker slices [ 1 ]. These strategies have been applied across various imaging modalities and clinical scenarios to minimize the impact of metal artifacts on diagnostic accuracy [ 10 ]. The second method to reduce metal artifacts is the use of monoenergetic imaging in dual-energy CT (DECT) [ 3 – 5 ]. Spectral CT techniques with MAR software have demonstrated effectiveness in improving visualization around metallic implants [ 6 ]. Virtual monochromatic imaging reduces beam hardening artifacts. The effectiveness of DECT artifact reduction decreases with higher-atomic-number metals, larger implants, and sharp-edged hardware. The third method utilizes metal artifact reduction software [ 7 , 8 ]. In this technique, the corrupt X-ray projections that traversed the metal hardware are removed and replaced with interpolation from unaffected projections. Previous studies have reported that metal artifact reduction techniques in post-surgical chest CT can significantly reduce artifacts and improve image quality [ 1 , 8 ]. Therefore, this study aimed to retrospectively assess the impact of the Smart Metal Artifact Reduction Technique (SMAR) used in combination with the iterative reconstruction technique (ASIR) on the performance of chest CT for evaluating patients with CIED, compared with ASIR alone. Materials and Methods This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The local Institutional Review Board approved the study. The Institutional Review Board waived the requirement for informed consent for this retrospective analysis. Patients with CIED, whose CT imaging records, including both ASIR and SMAR with ASIR reconstructions, were evaluated. A total of 30 patients (mean age: 63.4 ± 14.5 years; range: 25–89 years) were included in this study. The study population consisted of 16 females (53.3%) and 14 males (46.7%). The majority of CIEDs were implanted on the left side, with 90% and 10% on the right side. CT Acquisition and Image Reconstruction The CT examinations were performed using a single-source, 512-slice Multidetector Computed Tomography (MDCT) scanner (Revolution CT, GE Healthcare, USA) [ 11 ]. Scan parameters: tube voltage, 120 kVp; assist mode; tube current, SmartmA mode (100–500 mA); detector coverage, 40 mm; helical pitch, 0.992; rotation time, 0.80 s; slice thickness, 1.25 mm; slice interval, 1.25 mm; and scan FOV, 50 cm. CT images were reconstructed by using ASIR and the prototype SMAR algorithm (chest parameters) [ 4 ]. Iterative reconstruction techniques such as ASIR have been shown to improve image quality in chest CT imaging [ 14 ]. SMAR was performed using a vendor-specified "thorax" setting, which entails predetermined SMAR reconstruction parameters tailored to chest anatomy and hardware [ 7 ]. ASIR and ASIR + MAR images were reviewed side-by-side using: soft-tissue window (WW 400 HU; WL 35 HU), bone window (WW 2500 HU; WL 480 HU), and lung window (WW 1500 HU; WL − 600 HU). Images were evaluated in the axial plane, without the use of multiplanar reformations. After reconstructions, images were loaded onto Advantage Windows Workstation 4.7 (GE Healthcare, Milwaukee, Wisconsin/USA) for viewing. Subjective evaluation criteria and image analysis Two independent readers evaluated subjective image quality and image artifacts, with 16 and 10 years of experience in reading chest CT scans, respectively. For overall image quality, a single score was given regarding metallic hardware, soft tissue, osseous structures, and lung parenchyma. The scale was rated as follows: 1) Severe artifact with invisibility of surrounding structures. 2) Obvious artifacts with significant distortion and insufficient identification of surrounding structures. 3) Moderate artifacts that allow identification of surrounding structures. 4) Mild artifacts with blurring of surrounding structures. 5) No artifacts. Furthermore, MAR-related artifacts were rated separately for soft tissue, bone window, and lung parenchyma settings, respectively, in three categories: 3cm closer than the device, between 3 and 5 cm away from the device, and more than 5 cm away from the device (Fig. 6 ). A 3-point scale was used to evaluate the presence of MAR-related artifact from the CIED: 1 = no artifact, 2 = mild artifact that still allowed evaluation of anatomical structures, and 3 = severe artifact that precluded evaluation. A MAR-related artifact was defined as an artifact visible in MAR-reconstructed images but not in ASIR-only images. Objective image analysis Objective assessment of image quality is essential for quantifying the effectiveness of artifact reduction techniques [ 13 ]. An objective analysis of SMAR was conducted in accordance with previous studies [ 12 ]. Oval region of interest (diameter:1–2 cm) measurements were performed in three different locations adjacent to CIED, and standard deviations (SD; in Hounsfield Units, HU) were recorded. Regions of interest were measured on slices with the strongest artifact level as judged visually. The mean SD across the three ROIs was used as an objective artifact metric; higher SD indicates higher artifact load. Statistical Analysis Statistical analysis was performed using SPSS 22.0 (SPSS Inc., Chicago, IL). The measurement data are expressed as the means ± standard deviation (SD). Inter-reader agreement for subjective scores was assessed with Cohen's kappa (0–0.20 poor, 0.21–0.40 fair, 0.41–0.60 moderate, 0.61–0.80 substantial, 0.81–1.00 almost perfect). P < 0.05 was considered statistically significant. Results Study Population and Baseline Characteristics This retrospective study included a total of 30 patients with cardiac implantable electronic devices (CIEDs) who underwent chest CT with both ASIR and ASIR + SMAR reconstructions. The mean age of the study population was 63.4 ± 14.5 years (range: 25–89 years), with 53.3% (n = 16) female and 46.7% (n = 14) male patients. The majority of CIEDs were implanted in the left pectoral region (90%, n = 27). In qualitative assessment, ASIR + SMAR consistently reduced peri-device streak and beam-hardening artifacts on the soft-tissue window, improving delineation of adjacent pectoral soft tissues and vascular structures compared with ASIR (Fig. 1 ). On the bone window, ASIR + SMAR decreased metallic artifacts and clarified cortical margins and thoracic osseous structures near the generator (Fig. 2 ). On the lung parenchymal window, ASIR + SMAR reduced near-field artifacts; however, distance-dependent far-field streaks were occasionally observed within the lung parenchyma (Fig. 3 ). Objective Image Quality Assessment Objective artifact measurements demonstrated that the SMAR application significantly reduced artifact load. In ASIR + SMAR reconstructions, the standard deviation of Hounsfield Units (HU) in regions adjacent to the device was statistically significantly lower than in ASIR reconstructions (mean OAD: 43.60 ± 17.33 vs. 90.40 ± 42.11, p < 0.001) (Fig. 4 ). Similarly, measurements of hyperdense and hypodense artifacts were significantly reduced with SMAR (p < 0.001 for both). Subjective Image Quality Assessment ASIR + SMAR reconstructions received higher overall subjective image quality scores from both readers compared with images obtained using ASIR alone (Fig. 5 ). The improvement in image quality for soft tissue (ST) adjacent to the device and the device itself (AD) was statistically significant (p 0.05). In contrast, for lung parenchyma (LP) evaluation, quality scores in ASIR + SMAR images were statistically significantly lower than those in ASIR images (p < 0.05) (Table 1 ). Inter-reader agreement was generally good. Table 1 Subjective image quality scores for ASIR versus ASIR + SMAR across soft-tissue, bone, and lung windows, averaged over two readers Parameter ASIR Alone (mean ± SD) SMAR + ASIR (mean ± SD) P-value Overall Image Quality 2.92 ± 0.47 3.84 ± 0.48 < 0.001* Zone-Specific Assessment Near-field (< 3 cm from device) Soft tissue window (AD) 1.75 ± 0.51 3.82 ± 0.51 < 0.001* Mid-field (3–5 cm from device) Osseous structures (OS) 3.88 ± 0.67 4.05 ± 0.63 0.18 Soft tissue (ST) 1.73 ± 0.63 3.60 ± 0.68 5 cm from device) Lung parenchyma (LP) 4.34 ± 0.79 3.88 ± 1.00 0.016† Inter-reader Agreement (κ) 0.72 0.75 - P < 0.05 considered significant. Abbreviations: ASIR, adaptive statistical iterative reconstruction; SMAR, smart metal artifact reduction; SD, standard deviation. SMAR-Related New Artifacts New MAR-induced artifacts were observed in 13 of 30 patients (43.3%). All of these artifacts were located in far-field lung parenchyma (>5 cm from the device) and were severe (Score 3), precluding evaluation in 5 of these 13 patients. These artifacts were most frequently and severely detected in the lung parenchyma located more than 5 cm from the device (Fig. 6). In 5 patients, MAR-related artifacts limited assessment. Discussion In this study, we quantitatively and qualitatively assessed the impact of the Smart Metal Artifact Reduction Technique (SMAR), used in combination with the iterative reconstruction technique (ASIR), on the performance of chest CT for evaluating patients with CIED, compared with ASIR alone. SMAR, in combination with ASIR, led to a significant decrease in the degree of artifact compared to ASIR alone. Overall image quality was significantly increased with SMAR compared with ASIR. Detailed analysis revealed that SMAR led to more substantial artifact reduction in soft tissue (less than 3 cm away from the device). The image quality improvement of osseous structures (3-5cm away from the device) is less than that of soft tissue. Significant image improvement of lung parenchyma (more than 5cm away from the device) is not detected in this study. About one-third of the patients had newly induced artifacts that impaired image quality, which were detected in the lung parenchyma. These effects were most pronounced in peri-device soft tissues and less marked in osseous structures; far-field lung parenchyma showed occasional MAR-induced streaks. From a clinical perspective, these findings suggest that MAR algorithms are particularly effective for evaluating the soft tissue and osseous structures surrounding cardiac implantable electronic devices. The suppression of peri-device artifacts improves visualization of adjacent vascular, muscular, and skeletal tissues, which may prevent missed diagnoses of soft-tissue or bony pathologies such as infection, hematoma, or cortical erosion. However, the occasional appearance of newly induced streaks in distant lung parenchyma emphasizes the need for cautious interpretation of MAR-reconstructed lung images. In clinical routine, radiologists should therefore use MAR images primarily for near- and mid-field assessment while reviewing ASIR-only images in parallel for accurate evaluation of distant parenchymal regions. Metal-artifact severity depends on the hardware material, size, and geometry. Mitigation strategies include protocol optimization (kVp/mA, collimation, kernels), DECT with virtual monoenergetic images, and projection-completion MAR algorithms that interpolate corrupted sinogram data. The combination of virtual monoenergetic imaging and MAR algorithms has shown promising results in reducing artifacts from various metallic implants [9]. Vendors provide MAR software under different commercial names, including SEMAR (single-energy MAR, Canon Medical Systems, Otawara, Japan), O-MAR (orthopedic MAR, Philips Healthcare, Best, Netherlands), SMAR and MARS (Smart MAR and MAR Sequence, respectively, GE Healthcare, Milwaukee, WI), and MARIS and iMAR (MAR in Image Space and iterative MAR, respectively, Siemens Healthineers, Erlangen, Germany) [7]. Standardized frameworks for objective performance assessment of MAR algorithms are needed to facilitate comparison across different techniques and implementations [20]. MAR algorithms have found applications beyond diagnostic imaging, including treatment planning in radiation therapy [21]. There are only a few reports that spotlight metal artifacts in chest CT in patients with CIEDs [16,18]. Metal artifacts arising from metal elements of CIEDs are a result of two processes: a beam hardening phenomenon due to the dense metallic component and the exponential edge-gradient effect due to disparity between the high-density metal and the low-density surrounding tissue [15]. Schalla et al. [16] demonstrated that advanced CT techniques can effectively reduce metal artifacts from CIED leads, thereby improving the visualization of cardiac structures in patients with cardiac devices. Emerging technologies such as photon-counting detector CT show potential for further reducing beam hardening artifacts from metallic implants [17]. Pennig et al. [5] evaluated the reduction of CT artifacts from CIED using a combination of virtual monoenergetic images (VMI) and the MAR algorithm. They stated that the combination of VMI and MAR, as well as MAR as a standalone approach, provides an effective reduction of artifacts from CIEDs. In their study, they evaluated the diagnostic assessment of the pectoral soft tissue surrounding the device, as well as the heart and major vessels adjacent to the leads. They did not assess the lung parenchymal images and MAR-induced artifacts. Previous phantom studies have shown that MAR techniques can improve pulmonary nodule detection in the presence of pacemaker artifacts [1]. MAR algorithms can improve image quality. However, they may blur peri-metal data and introduce new artifacts that obscure parts of the hardware. In our study, new artifacts were detected in the lung parenchyma (more than 5cm away from the device), so further evaluation is necessary. Huang et al. [8] evaluated three different MAR techniques (O-MAR and monochromatic gemstone spectral imaging with or without MAR post-processing) and found that both MAR post-processing algorithms (O-MAR and MAR post-processing for gemstone spectral imaging) induced new artifacts in chest CT. In patients with shoulder arthroplasties, Shim et al. [19] showed that O-MAR tends to degrade the depiction of bone trabeculae and bone cortex, generating new artifacts, including a pseudo-cemented appearance and scapular pseudo-notching. They suggest using O-MAR in conjunction with non-O-MAR images, rather than as a replacement. In the literature, MAR algorithm-related artifacts have been explained by the fact that although the MAR algorithm can effectively eliminate blooming of the metal, it can also introduce far-field artifacts of a dark star type on axial images, likely representing photon starvation. We also observed a thick band streak in the lung parenchyma, which was far from the CIED, in 13 of 30 patients in our study. In 5 patients, MAR-related artifacts preclude the evaluation. However, not all studies have reported newly induced artifacts, likely reflecting differences among MAR implementations and study populations. In conclusion, our study is important as it is the first clinical study to evaluate the image quality of patients with CIED using the MAR algorithm in lung parenchyma, soft tissue, and bone tissue, although it has limitations. Limitations First, our study followed a retrospective study design. Only a small number of patients were included in our study. Secondly, we only evaluated the metal artifact reduction of the CIED generator, not the leads. Thirdly, the performance of various metal artifact reduction algorithms from different vendors may differ. Comparison studies analyzing these various algorithms might be needed. Moreover, we did not compare our results to those of non-iterative MAR algorithms or dual-energy CT with monoenergetic reconstructions. Lastly, we only evaluated the effect of SMAR in subjects without chest pathology. Further study on the ability of SMAR to evaluate pathologic conditions is warranted. Conclusion In conclusion, MAR reduces CIED-related artifacts and improves delineation of the device and near-field regions, especially in soft-tissue and bone windows, thereby increasing diagnostic confidence. Radiologists should, however, interpret far-field lung parenchyma with caution, as occasional MAR-induced streaks may limit assessment; in practice, MAR is best used complementarily to ASIR, prioritizing near- and mid-field evaluation while reviewing ASIR-only images for distant parenchyma. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest The authors declare that they have no conflicts of interest related to this study. Human Ethics and Consent to Participate This retrospective study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Istanbul Yeniyuzyil University (Approval No.: 2021/04-653). The requirement for informed consent was waived due to the retrospective design and use of de-identified data. Consent for publication Not applicable. This article does not contain any individual participant data in any form (including images, videos, or other personal information). Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Author Contributions Özgür Genç: Conceptualization, methodology, data curation, formal analysis, investigation, visualization, writing—original draft preparation. Serap Baş: Supervision, validation, resources, writing—review and editing. References Selles M, van Osch JAC, Maas M, Boomsma MF, Wellenberg RHH (2024) Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques. Eur J Radiol. ;170:111276. https://doi.org/10.1016/j.ejrad.2023.111276 . PMID: 38142571 Kalisz K, Buethe J, Saboo SS, Abbara S, Halliburton S, Rajiah P Artifacts at Cardiac CT: Physics and Solutions. Radiographics. 2016 Nov-Dec;36(7):2064–2083. https://doi.org/10.1148/rg.2016160079 . PMID: 27768543 Fang J, Zhang D, Wilcox C, Heidinger B, Raptopoulos V, Brook A, Brook OR (2017) Metal implants on CT: comparison of iterative reconstruction algorithms for reduction of metal artifacts with single energy and spectral CT scanning in a phantom model. Abdom Radiol (NY). ;42(3):742–748. https://doi.org/10.1007/s00261-016-1023-1 . PMID: 28078382 Schwartz FR, Tailor T, Gaca JG, Kiefer T, Harrison K, Hughes GC, Ramirez-Giraldo JC, Marin D, Hurwitz LM (2020) Impact of dual energy cardiac CT for metal artefact reduction post aortic valve replacement. Eur J Radiol. ;129:109135. https://doi.org/10.1016/j.ejrad.2020.109135 . PMID: 32590257 Pennig L, Zopfs D, Gertz R, Bremm J, Zaeske C, Große Hokamp N, Celik E, Goertz L, Langenbach M, Persigehl T, Gupta A, Borggrefe J, Lennartz S, Laukamp KR (2021) Reduction of CT artifacts from cardiac implantable electronic devices using a combination of virtual monoenergetic images and post-processing algorithms. Eur Radiol 31(9):7151–7161. https://doi.org/10.1007/s00330-021-07746-8 PMID: 33630164; PMCID: PMC8379133 Brook OR, Gourtsoyianni S, Brook A, Mahadevan A, Wilcox C, Raptopoulos V (2012) Spectral CT with metal artifacts reduction software for improvement of tumor visibility in the vicinity of gold fiducial markers. Radiology. ;263(3):696–705. https://doi.org/10.1148/radiol.12111170 . PMID: 22416251 Chou R, Chi HY, Lin YH, Ying LK, Chao YJ, Lin CH (2020) Comparison of quantitative measurements of four manufacturer's metal artifact reduction techniques for CT imaging with a self-made acrylic phantom. Technol Health Care 28(S1):273–287. https://doi.org/10.3233/THC-209028 PMID: 32364160; PMCID: PMC7369061 Wellenberg RH, Hakvoort ET, Slump CH, Boomsma MF, Maas M, Streekstra GJ (2018) Metal artifact reduction techniques in musculoskeletal CT-imaging. Eur J Radiol. ;107:60–69. https://doi.org/10.1016/j.ejrad.2018.08.010 . PMID: 30292289 Huang JY, Kerns JR, Nute JL, Liu X, Balter PA, Stingo FC, Followill DS, Mirkovic D, Howell RM, Kry SF (2015) An evaluation of three commercially available metal artifact reduction methods for CT imaging. Phys Med Biol. ;60(3):1047-67. https://doi.org/10.1088/0031-9155/60/3/1047 . PMID: 25585685 Kidoh M, Nakaura T, Nakamura S, Tokuyasu S, Osakabe H, Harada K, Yamashita Y (2014) Reduction of dental metallic artefacts in CT: value of a newly developed algorithm for metal artefact reduction (O-MAR). Clin Radiol. ;69(11):e11-6. https://doi.org/10.1016/j.crad.2014.08.008 . PMID: 25239794 Subhas N, Primak AN, Obuchowski NA, Gupta A, Polster JM, Krauss A, Iannotti JP, McCollough CH (2014) Iterative metal artifact reduction: evaluation and optimization of technique. Skeletal Radiol. ;43(12):1729-35. https://doi.org/10.1007/s00256-014-1987-2 . PMID: 25120200 Gjesteby L, De Man B, Jin Y, Paganetti H, Verburg J, Giantsoudi D, Wang G (2016) Metal Artifact Reduction in CT: Where Are We After Four Decades? IEEE Access 4:5826–5849. https://doi.org/10.1109/ACCESS.2016.2608621 Boos J, Lanzman RS, Meineke A, Heusch P, Aissa J, Schleich C, Kröpil P, Antoch G, Kröger JR (2015) Dose monitoring using the DICOM structured report: assessment of the relationship between cumulative radiation exposure and BMI in abdominal CT. Clin Radiol. ;70(2):176 – 82. https://doi.org/10.1016/j.crad.2014.11.002 . PMID: 25468640 Katsura M, Sato J, Akahane M, Kunimatsu A, Abe O (2018) Mar-Apr;38(2):450–461 Current and Novel Techniques for Metal Artifact Reduction at CT: Practical Guide for Radiologists. Radiographics. https://doi.org/10.1148/rg.2018170102 . PMID: 29528826 Bongers MN, Schabel C, Thomas C, Raupach R, Notohamiprodjo M, Nikolaou K, Bamberg F (2015) Comparison and combination of dual-energy- and iterative-based metal artefact reduction on hip prosthesis and dental implants. PLoS ONE 10(11):e0143584. https://doi.org/10.1371/journal.pone.0143584 PMID: 26571123; PMCID: PMC4646649 Lell MM, Kachelrieß M Recent and Upcoming Technological Developments in Computed Tomography: High Speed, Low Dose, Learning D (2020) Multienergy. Invest Radiol. ;55(1):8–19. https://doi.org/10.1097/RLI.0000000000000601 . PMID: 31688657 Willemink MJ, Noël PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29(5):2185–2195. https://doi.org/10.1007/s00330-018-5810-7 PMID: 30377791; PMCID: PMC6434931 Geyer LL, Schoepf UJ, Meinel FG, Nance JW Jr, Bastarrika G, Leipsic JA, Paul NS, Rengo M, Laghi A, De Cecco CN (2015) State of the Art: Iterative CT Reconstruction Techniques. Radiology. ;276(2):339 – 57. https://doi.org/10.1148/radiol.2015132766 . PMID: 26203706 Morsbach F, Bickelhaupt S, Wanner GA, Krauss A, Schmidt B, Alkadhi H (2013) Reduction of metal artifacts from hip prostheses on CT images of the pelvis: value of iterative reconstructions. Radiology. ;268(1):237 – 44. https://doi.org/10.1148/radiol.13122089 . PMID: 23513242 Lee MJ, Kim S, Lee SA, Song HT, Huh YM, Kim DH, Han SH, Suh JS Overcoming artifacts from metallic orthopedic implants at high-field-strength MR imaging and multi-detector CT. Radiographics. 2007 May-Jun;27(3):791–803. https://doi.org/10.1148/rg.273065087 . PMID: 17495292 Bamberg F, Dierks A, Nikolaou K, Reiser MF, Becker CR, Johnson TR (2011) Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. Eur Radiol. ;21(7):1424-9. https://doi.org/10.1007/s00330-011-2062-1 . PMID: 21249371 Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract600.tiff.jpg Graphical abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7842222","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536849358,"identity":"dd0f6d1a-324b-4344-b7ba-7ec217489513","order_by":0,"name":"Özgür Genç","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDCCAwgm4wMgwcNHihZmA5AWNlK0sEmASUI6+G4fPvzxa9s9u37p5meVX3PsZNgYmB8+uoFHi+S5tARj2bbi5Jlzjpndlt2WDHQYm7FxDh4tBmd4DJIl2xKSDW7ksN2W3MYM1MLDJk1Iy2GQFnuglmLJbfVEaTFs/NiWYGcgkcPG+HHbYcJaJM+wJTMznEtIkLiRZizNuO04DxszAb/wnWE+/PFHWYI9/4zkhx9/bqu252dvfvgYnxYQYOZhYEhsgDKAJAHlIMD4g4HBHsYYBaNgFIyCUYABAAwIRZmFAkonAAAAAElFTkSuQmCC","orcid":"","institution":"Istanbul Aydın University","correspondingAuthor":true,"prefix":"","firstName":"Özgür","middleName":"","lastName":"Genç","suffix":""},{"id":536849359,"identity":"3f832ac2-5f07-4428-bcce-f9017d7311e9","order_by":1,"name":"Serap Baş","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Serap","middleName":"","lastName":"Baş","suffix":""}],"badges":[],"createdAt":"2025-10-12 17:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7842222/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7842222/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95264160,"identity":"69318fee-a304-40a6-800d-0f8c01fe16e7","added_by":"auto","created_at":"2025-11-06 05:23:56","extension":"tiff","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1168436,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1300.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/363f7f864a3ec368fa2824e9.tiff"},{"id":95264309,"identity":"c47d55cd-baa9-468f-bb8e-cc49528d5c3b","added_by":"auto","created_at":"2025-11-06 05:24:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3313148,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/e7ad6c1d74845d507a7865c1.docx"},{"id":95264258,"identity":"2f513739-3e5a-4585-8882-41b1143a93f8","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":975288,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2300.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/00a56145e72508e407fb2b5c.tiff"},{"id":95264217,"identity":"07638665-0973-44fe-8015-054e3e08af21","added_by":"auto","created_at":"2025-11-06 05:24:02","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15554,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/0549ee1153dd0148ce1ee604.docx"},{"id":95264273,"identity":"7402f34d-f0e3-4482-957c-252f07c58481","added_by":"auto","created_at":"2025-11-06 05:24:05","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2145606,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3300.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/c3c1718d03c8ab48a9a6c1b6.tiff"},{"id":95264213,"identity":"72e1679c-7204-45a2-a334-d593e43438b0","added_by":"auto","created_at":"2025-11-06 05:24:02","extension":"tiff","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":544458,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/a67985341747b1197a65986b.tiff"},{"id":95264245,"identity":"cf7a6ebe-4908-4081-baf7-ec20c215d76a","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"tiff","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":331602,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/de06e5e0c9d6b6e921027960.tiff"},{"id":95264247,"identity":"29f0e66d-9a94-461f-88d2-909f309bedfb","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"tiff","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":583648,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/5b98127ac941754a6d7a73cc.tiff"},{"id":95264259,"identity":"07dbd455-65a5-47c0-8e10-32cacf0a8c33","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"tiff","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79032,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/68559fc304881c36aaa6cede.tiff"},{"id":95264262,"identity":"46b59b05-c789-40b3-8235-ad81cfc4e290","added_by":"auto","created_at":"2025-11-06 05:24:05","extension":"json","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4489,"visible":true,"origin":"","legend":"","description":"","filename":"fea481d98f69493ea497525d86fb5c76.json","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/3fdb74533b2bcdeb6073c3f9.json"},{"id":95264208,"identity":"924e72cd-6f36-4cf7-a763-75eccff6e10d","added_by":"auto","created_at":"2025-11-06 05:24:01","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84491,"visible":true,"origin":"","legend":"","description":"","filename":"fea481d98f69493ea497525d86fb5c761enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/008b84cb87062a77fb8546b8.xml"},{"id":95264207,"identity":"0ca8176b-e8c7-4475-8e87-a76570a9741d","added_by":"auto","created_at":"2025-11-06 05:24:01","extension":"tiff","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1168436,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1300.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/21b56871b9dadf46217913ee.tiff"},{"id":95264162,"identity":"4b2a16ea-d0ff-4f27-9589-2625c534a779","added_by":"auto","created_at":"2025-11-06 05:23:57","extension":"tiff","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":975288,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2300.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/efc3175a2d19bfe017179188.tiff"},{"id":95264205,"identity":"a7b1666f-a9e9-46c7-a13e-f35fbbf64928","added_by":"auto","created_at":"2025-11-06 05:24:01","extension":"tiff","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2145606,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3300.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/68843b0346686d9113baedce.tiff"},{"id":95264201,"identity":"48add468-1598-4b96-a20c-79e038c5978d","added_by":"auto","created_at":"2025-11-06 05:24:01","extension":"tiff","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":544458,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/c3b73c8b5c2e9b2376bb8417.tiff"},{"id":95264250,"identity":"cc05fa0b-6536-4b7a-951e-6907048a9b0e","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"tiff","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":331602,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/ad59d13219e9b92b9743fbc9.tiff"},{"id":95264212,"identity":"ebba65d0-e3b1-4fc4-8243-062be57b8047","added_by":"auto","created_at":"2025-11-06 05:24:02","extension":"tiff","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":583648,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/55ee9e17f525adce29e6c030.tiff"},{"id":95264196,"identity":"337c954e-2f64-4925-ac6d-505567b931dd","added_by":"auto","created_at":"2025-11-06 05:24:00","extension":"tiff","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79032,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract600.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/87d0716abffb3e5af07bd903.tiff"},{"id":95264246,"identity":"14a6f586-0469-4ff8-9683-6c71424267f7","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":714516,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/aa2054a83421bbe4da5e2a0e.jpeg"},{"id":95264291,"identity":"e698e863-9747-4002-bf70-b0b12ba7f1a7","added_by":"auto","created_at":"2025-11-06 05:24:06","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":634368,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/84224a4a6c69607be09d6f5d.jpeg"},{"id":95264226,"identity":"4838f52b-fd72-43af-a8b7-fed464ba4dab","added_by":"auto","created_at":"2025-11-06 05:24:03","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1245598,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/1b092cd03ea563fc77994206.jpeg"},{"id":95313014,"identity":"c25f65c3-3413-47a7-9f56-54ca5156a6b3","added_by":"auto","created_at":"2025-11-06 15:50:45","extension":"jpeg","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201490,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/3b496a4a3304db8597170363.jpeg"},{"id":95264176,"identity":"15ded349-dbbf-4c01-bb25-8a83569eabe1","added_by":"auto","created_at":"2025-11-06 05:23:58","extension":"jpeg","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":214376,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/d8fd89da99c4f66f968d5337.jpeg"},{"id":95264287,"identity":"efe37b05-83ba-47d8-8045-b94af9bff283","added_by":"auto","created_at":"2025-11-06 05:24:06","extension":"jpeg","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":253738,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/d9037e6c0eff07193039f114.jpeg"},{"id":95264163,"identity":"5df9342d-45d4-406d-a40e-89b23cee4613","added_by":"auto","created_at":"2025-11-06 05:23:57","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":797939,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1300.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/c19c7df12a4c92d10f81a3fb.png"},{"id":95264257,"identity":"c2854065-9ef5-491f-80a3-cca1f1030088","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":685429,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2300.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/18cf2a6959fef239054350af.png"},{"id":95264255,"identity":"252a5b3c-dd3b-4295-8445-b597b46353f9","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1305447,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3300.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/f8da603dbc96154e52136caa.png"},{"id":95313412,"identity":"671313a5-1904-4504-b048-eda58f931487","added_by":"auto","created_at":"2025-11-06 15:51:22","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132739,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4600.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/325beefe9320a8447123a84a.png"},{"id":95264279,"identity":"797cc4d4-c338-46de-aeec-800d8abfd964","added_by":"auto","created_at":"2025-11-06 05:24:05","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100645,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5600.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/440d8854c149b36023418ccf.png"},{"id":95313425,"identity":"faf87156-d2fa-447e-9305-f5348d8cb353","added_by":"auto","created_at":"2025-11-06 15:51:23","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119861,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure6600.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/9d47704242a2e6c7c70c4687.png"},{"id":95264218,"identity":"a4668215-f1da-464a-951a-4721d9a047ec","added_by":"auto","created_at":"2025-11-06 05:24:02","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27015,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineGraphicalAbstract600.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/7cb0860e0ff0591845a0f283.png"},{"id":95264195,"identity":"574e96ae-e985-4977-b1c8-2d3411995eb7","added_by":"auto","created_at":"2025-11-06 05:23:59","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":406116,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/f93bbed96f929a95af712472.png"},{"id":95264199,"identity":"a9822bab-d02f-4e03-af49-683fbbfef926","added_by":"auto","created_at":"2025-11-06 05:24:01","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":365254,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/fe80b55fa0e80a907f736204.png"},{"id":95264203,"identity":"a31c550f-4479-4d70-9e6e-79e324fb42ed","added_by":"auto","created_at":"2025-11-06 05:24:01","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":690376,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/5f7ede4b9661a1c20848cbd9.png"},{"id":95264316,"identity":"95730e42-bf38-4227-8c3a-f19fa041f0f3","added_by":"auto","created_at":"2025-11-06 05:24:08","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25491,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/a4a2d02e5597d0a4243248cc.png"},{"id":95313419,"identity":"bc6bca43-3703-455c-98f1-3c13b89353e6","added_by":"auto","created_at":"2025-11-06 15:51:23","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20967,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/74fc1b21d60038fe16e60594.png"},{"id":95264283,"identity":"f051bbc2-f909-47d9-b20f-50e6978ed8b3","added_by":"auto","created_at":"2025-11-06 05:24:05","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35133,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/5480e957c8706c34142de66f.png"},{"id":95264251,"identity":"b8c5f03d-7671-4553-b2ee-7a37b94512bc","added_by":"auto","created_at":"2025-11-06 05:24:04","extension":"xml","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82294,"visible":true,"origin":"","legend":"","description":"","filename":"fea481d98f69493ea497525d86fb5c761structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/58724584c7830396eb3a237b.xml"},{"id":95264282,"identity":"3fe43129-37cc-442c-bcea-2b91f3dc355d","added_by":"auto","created_at":"2025-11-06 05:24:05","extension":"html","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95612,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/0267f0cb7076b34493a2bbe8.html"},{"id":95264268,"identity":"ed15f331-f39a-48b4-8f38-d581acb7ab43","added_by":"auto","created_at":"2025-11-06 05:24:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":678639,"visible":true,"origin":"","legend":"\u003cp\u003eSoft-tissue window in a CIED patient: ASIR+SMAR reduces peri-device streak and beam-hardening and improves depiction of adjacent soft tissues and vascular structures compared with ASIR\u003c/p\u003e","description":"","filename":"Figure1300.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/9a5407878f4933760bd4c1a9.jpg"},{"id":95264158,"identity":"b941eb56-878b-4631-81b5-92eca2d53b8c","added_by":"auto","created_at":"2025-11-06 05:23:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":592793,"visible":true,"origin":"","legend":"\u003cp\u003eBone window: ASIR+SMAR lessens metallic artifacts and clarifies cortical bone and thoracic skeleton near the generator compared with ASIR\u003c/p\u003e","description":"","filename":"Figure2300.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/9f2914570f3d806d6d7a92b1.jpg"},{"id":95264159,"identity":"5a737296-ef6b-4a31-9cd1-056230b8142f","added_by":"auto","created_at":"2025-11-06 05:23:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1072794,"visible":true,"origin":"","legend":"\u003cp\u003eLung parenchymal window: ASIR+SMAR reduces near-field artifacts while revealing distance-dependent far-field streaks within the lung\u003c/p\u003e","description":"","filename":"Figure3300.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/d614969171c29473c33c23d9.jpg"},{"id":95264194,"identity":"b7aab692-d097-41b8-909d-67fea420132e","added_by":"auto","created_at":"2025-11-06 05:23:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1935475,"visible":true,"origin":"","legend":"\u003cp\u003eObjective artifact load: ROI-based SD (HU) adjacent to the generator is lower on ASIR+SMAR than on ASIR\u003c/p\u003e","description":"","filename":"Figure4600.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/7163499958b0c6980dd8c6d2.jpg"},{"id":95264211,"identity":"dffee83b-4742-4301-865f-6e5b8d59b2ef","added_by":"auto","created_at":"2025-11-06 05:24:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1095804,"visible":true,"origin":"","legend":"\u003cp\u003eOverall diagnostic image quality: reader-averaged scores in 30 patients show improvement with SMAR across soft-tissue, bone, and lung Windows\u003c/p\u003e","description":"","filename":"Figure5600.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/3381bf4a40e9c9345a90c31a.jpg"},{"id":95264193,"identity":"95b90172-d503-4e70-b5df-ea2f9f4346b5","added_by":"auto","created_at":"2025-11-06 05:23:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1349783,"visible":true,"origin":"","legend":"\u003cp\u003eDistance-dependent MAR pattern: visual scores show reduced near- and mid-field artifacts with occasional far-field streaks\u003c/p\u003e","description":"","filename":"Figure6600.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/46c6436c85860335c3454654.jpg"},{"id":100359519,"identity":"d88f24a6-f549-4744-b5c3-826adae48694","added_by":"auto","created_at":"2026-01-16 07:22:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7368344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/8f00d57b-a581-4b9c-adb9-3cbcb07eae6e.pdf"},{"id":95264264,"identity":"31cd4ee4-647a-4d7b-9b5f-fa9cfd411f36","added_by":"auto","created_at":"2025-11-06 05:24:05","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":253214,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"GraphicalAbstract600.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7842222/v1/5008885bb3f3c971171b235f.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effectiveness of a Smart Metal Artifact Reduction Technique in Chest CT of Patients with Cardiac Implantable Electronic Devices","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiac implantable electronic devices (CIEDs) are increasingly used for the management of rhythm disorders and cardiac dysfunctions. As life expectancy rises, the number of implanted devices continues to grow each year. These systems typically consist of a pulse generator placed beneath the skin in the upper chest and one or more leads extending through the veins into the heart muscle. Despite the development of magnetic resonance imaging models, computed tomography (CT) remains a key imaging modality for thoracic evaluation, providing detailed visualization of the lungs, mediastinum, heart, and skeletal structures. However, the metallic components of the generator can produce image artifacts that obscure adjacent anatomical regions and reduce diagnostic accuracy.\u003c/p\u003e\u003cp\u003eMetal artifacts arise as a combination of beam-hardening, photon starvation, and scatter artifacts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. To overcome metallic artifacts, basic strategies include increasing the tube voltage and current, reducing the detector size, using narrow collimation, employing iterative image reconstructions, using a soft reconstruction kernel instead of a sharp kernel, and reconstructing images from acquired raw data with thicker slices [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These strategies have been applied across various imaging modalities and clinical scenarios to minimize the impact of metal artifacts on diagnostic accuracy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The second method to reduce metal artifacts is the use of monoenergetic imaging in dual-energy CT (DECT) [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Spectral CT techniques with MAR software have demonstrated effectiveness in improving visualization around metallic implants [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Virtual monochromatic imaging reduces beam hardening artifacts. The effectiveness of DECT artifact reduction decreases with higher-atomic-number metals, larger implants, and sharp-edged hardware. The third method utilizes metal artifact reduction software [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this technique, the corrupt X-ray projections that traversed the metal hardware are removed and replaced with interpolation from unaffected projections.\u003c/p\u003e\u003cp\u003ePrevious studies have reported that metal artifact reduction techniques in post-surgical chest CT can significantly reduce artifacts and improve image quality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to retrospectively assess the impact of the Smart Metal Artifact Reduction Technique (SMAR) used in combination with the iterative reconstruction technique (ASIR) on the performance of chest CT for evaluating patients with CIED, compared with ASIR alone.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The local Institutional Review Board approved the study. The Institutional Review Board waived the requirement for informed consent for this retrospective analysis.\u003c/p\u003e\u003cp\u003ePatients with CIED, whose CT imaging records, including both ASIR and SMAR with ASIR reconstructions, were evaluated. A total of 30 patients (mean age: 63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5 years; range: 25\u0026ndash;89 years)\u003c/p\u003e\u003cp\u003ewere included in this study. The study population consisted of 16 females (53.3%) and 14 males (46.7%). The majority of CIEDs were implanted on the left side, with 90% and 10% on the right side.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCT Acquisition and Image Reconstruction\u003c/h2\u003e\u003cp\u003eThe CT examinations were performed using a single-source, 512-slice Multidetector Computed Tomography (MDCT) scanner (Revolution CT, GE Healthcare, USA) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Scan parameters: tube voltage, 120 kVp; assist mode; tube current, SmartmA mode (100\u0026ndash;500 mA); detector coverage, 40 mm; helical pitch, 0.992; rotation time, 0.80 s; slice thickness, 1.25 mm; slice interval, 1.25 mm; and scan FOV, 50 cm.\u003c/p\u003e\u003cp\u003eCT images were reconstructed by using ASIR and the prototype SMAR algorithm (chest parameters) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Iterative reconstruction techniques such as ASIR have been shown to improve image quality in chest CT imaging [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. SMAR was performed using a vendor-specified \"thorax\" setting, which entails predetermined SMAR reconstruction parameters tailored to chest anatomy and hardware [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eASIR and ASIR\u0026thinsp;+\u0026thinsp;MAR images were reviewed side-by-side using: soft-tissue window (WW 400 HU; WL 35 HU), bone window (WW 2500 HU; WL 480 HU), and lung window (WW 1500 HU; WL \u0026minus;\u0026thinsp;600 HU). Images were evaluated in the axial plane, without the use of multiplanar reformations. After reconstructions, images were loaded onto Advantage Windows Workstation 4.7 (GE Healthcare, Milwaukee, Wisconsin/USA) for viewing.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSubjective evaluation criteria and image analysis\u003c/h3\u003e\n\u003cp\u003eTwo independent readers evaluated subjective image quality and image artifacts, with 16 and 10 years of experience in reading chest CT scans, respectively. For overall image quality, a single score was given regarding metallic hardware, soft tissue, osseous structures, and lung parenchyma. The scale was rated as follows: 1) Severe artifact with invisibility of surrounding structures. 2) Obvious artifacts with significant distortion and insufficient identification of surrounding structures. 3) Moderate artifacts that allow identification of surrounding structures. 4) Mild artifacts with blurring of surrounding structures. 5) No artifacts.\u003c/p\u003e\u003cp\u003eFurthermore, MAR-related artifacts were rated separately for soft tissue, bone window, and lung parenchyma settings, respectively, in three categories: 3cm closer than the device, between 3 and 5 cm away from the device, and more than 5 cm away from the device (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A 3-point scale was used to evaluate the presence of MAR-related artifact from the CIED: 1\u0026thinsp;=\u0026thinsp;no artifact, 2\u0026thinsp;=\u0026thinsp;mild artifact that still allowed evaluation of anatomical structures, and 3\u0026thinsp;=\u0026thinsp;severe artifact that precluded evaluation. A MAR-related artifact was defined as an artifact visible in MAR-reconstructed images but not in ASIR-only images.\u003c/p\u003e\n\u003ch3\u003eObjective image analysis\u003c/h3\u003e\n\u003cp\u003eObjective assessment of image quality is essential for quantifying the effectiveness of artifact reduction techniques [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. An objective analysis of SMAR was conducted in accordance with previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Oval region of interest (diameter:1\u0026ndash;2 cm) measurements were performed in three different locations adjacent to CIED, and standard deviations (SD; in Hounsfield Units, HU) were recorded. Regions of interest were measured on slices with the strongest artifact level as judged visually. The mean SD across the three ROIs was used as an objective artifact metric; higher SD indicates higher artifact load.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS 22.0 (SPSS Inc., Chicago, IL). The measurement data are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Inter-reader agreement for subjective scores was assessed with Cohen's kappa (0\u0026ndash;0.20 poor, 0.21\u0026ndash;0.40 fair, 0.41\u0026ndash;0.60 moderate, 0.61\u0026ndash;0.80 substantial, 0.81\u0026ndash;1.00 almost perfect). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population and Baseline Characteristics\u003c/h2\u003e\u003cp\u003eThis retrospective study included a total of 30 patients with cardiac implantable electronic devices (CIEDs) who underwent chest CT with both ASIR and ASIR\u0026thinsp;+\u0026thinsp;SMAR reconstructions. The mean age of the study population was 63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5 years (range: 25\u0026ndash;89 years), with 53.3% (n\u0026thinsp;=\u0026thinsp;16) female and 46.7% (n\u0026thinsp;=\u0026thinsp;14) male patients. The majority of CIEDs were implanted in the left pectoral region (90%, n\u0026thinsp;=\u0026thinsp;27).\u003c/p\u003e\u003cp\u003eIn qualitative assessment, ASIR\u0026thinsp;+\u0026thinsp;SMAR consistently reduced peri-device streak and beam-hardening artifacts on the soft-tissue window, improving delineation of adjacent pectoral soft tissues and vascular structures compared with ASIR (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the bone window, ASIR\u0026thinsp;+\u0026thinsp;SMAR decreased metallic artifacts and clarified cortical margins and thoracic osseous structures near the generator (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On the lung parenchymal window, ASIR\u0026thinsp;+\u0026thinsp;SMAR reduced near-field artifacts; however, distance-dependent far-field streaks were occasionally observed within the lung parenchyma (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eObjective Image Quality Assessment\u003c/h3\u003e\n\u003cp\u003eObjective artifact measurements demonstrated that the SMAR application significantly reduced artifact load. In ASIR\u0026thinsp;+\u0026thinsp;SMAR reconstructions, the standard deviation of Hounsfield Units (HU) in regions adjacent to the device was statistically significantly lower than in ASIR reconstructions (mean OAD: 43.60\u0026thinsp;\u0026plusmn;\u0026thinsp;17.33 vs. 90.40\u0026thinsp;\u0026plusmn;\u0026thinsp;42.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, measurements of hyperdense and hypodense artifacts were significantly reduced with SMAR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSubjective Image Quality Assessment\u003c/h3\u003e\n\u003cp\u003eASIR\u0026thinsp;+\u0026thinsp;SMAR reconstructions received higher overall subjective image quality scores from both readers compared with images obtained using ASIR alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The improvement in image quality for soft tissue (ST) adjacent to the device and the device itself (AD) was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). Although image quality scores for osseous structures (OS) were higher with ASIR\u0026thinsp;+\u0026thinsp;SMAR, this difference was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, for lung parenchyma (LP) evaluation, quality scores in ASIR\u0026thinsp;+\u0026thinsp;SMAR images were statistically significantly lower than those in ASIR images (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Inter-reader agreement was generally good.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSubjective image quality scores for ASIR versus ASIR\u0026thinsp;+\u0026thinsp;SMAR across soft-tissue, bone, and lung windows, averaged over two readers\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASIR Alone (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSMAR\u0026thinsp;+\u0026thinsp;ASIR (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall Image Quality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZone-Specific Assessment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNear-field (\u0026lt;\u0026thinsp;3 cm from device)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoft tissue window (AD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMid-field (3\u0026ndash;5 cm from device)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsseous structures (OS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoft tissue (ST)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFar-field (\u0026gt;\u0026thinsp;5 cm from device)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung parenchyma (LP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInter-reader Agreement (κ)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e\u003cp\u003eAbbreviations: ASIR, adaptive statistical iterative reconstruction; SMAR, smart metal artifact reduction; SD, standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSMAR-Related New Artifacts\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNew MAR-induced artifacts were observed in 13 of 30 patients (43.3%). All of these artifacts were located in far-field lung parenchyma (\u0026gt;5 cm from the device) and were severe (Score 3), precluding evaluation in 5 of these 13 patients. These artifacts were most frequently and severely detected in the lung parenchyma located more than 5 cm from the device (Fig. 6). In 5 patients, MAR-related artifacts limited assessment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we quantitatively and qualitatively assessed the impact of the Smart Metal Artifact Reduction Technique (SMAR), used in combination with the iterative reconstruction technique (ASIR), on the performance of chest CT for evaluating patients with CIED, compared with ASIR alone. SMAR, in combination with ASIR, led to a significant decrease in the degree of artifact compared to ASIR alone. Overall image quality was significantly increased with SMAR compared with ASIR. Detailed analysis revealed that SMAR led to more substantial artifact reduction in soft tissue (less than 3 cm away from the device). The image quality improvement of osseous structures (3-5cm away from the device) is less than that of soft tissue. Significant image improvement of lung parenchyma (more than 5cm away from the device) is not detected in this study. About one-third of the patients had newly induced artifacts that impaired image quality, which were detected in the lung parenchyma.\u003c/p\u003e\n\u003cp\u003eThese effects were most pronounced in peri-device soft tissues and less marked in osseous structures; far-field lung parenchyma showed occasional MAR-induced streaks. From a clinical perspective, these findings suggest that MAR algorithms are particularly effective for evaluating the soft tissue and osseous structures surrounding cardiac implantable electronic devices. The suppression of peri-device artifacts improves visualization of adjacent vascular, muscular, and skeletal tissues, which may prevent missed diagnoses of soft-tissue or bony pathologies such as infection, hematoma, or cortical erosion. However, the occasional appearance of newly induced streaks in distant lung parenchyma emphasizes the need for cautious interpretation of MAR-reconstructed lung images. In clinical routine, radiologists should therefore use MAR images primarily for near- and mid-field assessment while reviewing ASIR-only images in parallel for accurate evaluation of distant parenchymal regions.\u003c/p\u003e\n\u003cp\u003eMetal-artifact severity depends on the hardware material, size, and geometry. Mitigation strategies include protocol optimization (kVp/mA, collimation, kernels), DECT with virtual monoenergetic images, and projection-completion MAR algorithms that interpolate corrupted sinogram data. The combination of virtual monoenergetic imaging and MAR algorithms has shown promising results in reducing artifacts from various metallic implants [9].\u003c/p\u003e\n\u003cp\u003eVendors provide MAR software under different commercial names, including SEMAR (single-energy MAR, Canon Medical Systems, Otawara, Japan), O-MAR (orthopedic MAR, Philips Healthcare, Best, Netherlands), SMAR and MARS (Smart MAR and MAR Sequence, respectively, GE Healthcare, Milwaukee, WI), and MARIS and iMAR (MAR in Image Space and iterative MAR, respectively, Siemens Healthineers, Erlangen, Germany) [7]. Standardized frameworks for objective performance assessment of MAR algorithms are needed to facilitate comparison across different techniques and implementations [20]. MAR algorithms have found applications beyond diagnostic imaging, including treatment planning in radiation therapy [21].\u003c/p\u003e\n\u003cp\u003eThere are only a few reports that spotlight metal artifacts in chest CT in patients with CIEDs [16,18]. Metal artifacts arising from metal elements of CIEDs are a result of two processes: a beam hardening phenomenon due to the dense metallic component and the exponential edge-gradient effect due to disparity between the high-density metal and the low-density surrounding tissue [15]. Schalla et al. [16] demonstrated that advanced CT techniques can effectively reduce metal artifacts from CIED leads, thereby improving the visualization of cardiac structures in patients with cardiac devices. Emerging technologies such as photon-counting detector CT show potential for further reducing beam hardening artifacts from metallic implants [17]. Pennig et al. [5] evaluated the reduction of CT artifacts from CIED using a combination of virtual monoenergetic images (VMI) and the MAR algorithm. They stated that the combination of VMI and MAR, as well as MAR as a standalone approach, provides an effective reduction of artifacts from CIEDs. In their study, they evaluated the diagnostic assessment of the pectoral soft tissue surrounding the device, as well as the heart and major vessels adjacent to the leads. They did not assess the lung parenchymal images and MAR-induced artifacts. Previous phantom studies have shown that MAR techniques can improve pulmonary nodule detection in the presence of pacemaker artifacts [1].\u003c/p\u003e\n\u003cp\u003eMAR algorithms can improve image quality. However, they may blur peri-metal data and introduce new artifacts that obscure parts of the hardware. In our study, new artifacts were detected in the lung parenchyma (more than 5cm away from the device), so further evaluation is necessary. Huang et al. [8] evaluated three different MAR techniques (O-MAR and monochromatic gemstone spectral imaging with or without MAR post-processing) and found that both MAR post-processing algorithms (O-MAR and MAR post-processing for gemstone spectral imaging) induced new artifacts in chest CT. In patients with shoulder arthroplasties, Shim et al. [19] showed that O-MAR tends to degrade the depiction of bone trabeculae and bone cortex, generating new artifacts, including a pseudo-cemented appearance and scapular pseudo-notching. They suggest using O-MAR in conjunction with non-O-MAR images, rather than as a replacement.\u003c/p\u003e\n\u003cp\u003eIn the literature, MAR algorithm-related artifacts have been explained by the fact that although the MAR algorithm can effectively eliminate blooming of the metal, it can also introduce far-field artifacts of a dark star type on axial images, likely representing photon starvation. We also observed a thick band streak in the lung parenchyma, which was far from the CIED, in 13 of 30 patients in our study. In 5 patients, MAR-related artifacts preclude the evaluation. However, not all studies have reported newly induced artifacts, likely reflecting differences among MAR implementations and study populations.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study is important as it is the first clinical study to evaluate the image quality of patients with CIED using the MAR algorithm in lung parenchyma, soft tissue, and bone tissue, although it has limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, our study followed a retrospective study design. Only a small number of patients were included in our study. Secondly, we only evaluated the metal artifact reduction of the CIED generator, not the leads. Thirdly, the performance of various metal artifact reduction algorithms from different vendors may differ. Comparison studies analyzing these various algorithms might be needed.\u003c/p\u003e\n\u003cp\u003eMoreover, we did not compare our results to those of non-iterative MAR algorithms or dual-energy CT with monoenergetic reconstructions. Lastly, we only evaluated the effect of SMAR in subjects without chest pathology. Further study on the ability of SMAR to evaluate pathologic conditions is warranted.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, MAR reduces CIED-related artifacts and improves delineation of the device and near-field regions, especially in soft-tissue and bone windows, thereby increasing diagnostic confidence. Radiologists should, however, interpret far-field lung parenchyma with caution, as occasional MAR-induced streaks may limit assessment; in practice, MAR is best used complementarily to ASIR, prioritizing near- and mid-field evaluation while reviewing ASIR-only images for distant parenchyma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of Interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHuman Ethics and Consent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Istanbul Yeniyuzyil University (Approval No.: 2021/04-653). The requirement for informed consent was waived due to the retrospective design and use of de-identified data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This article does not contain any individual participant data in any form (including images, videos, or other personal information).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eÖzgür Genç: Conceptualization, methodology, data curation, formal analysis, investigation, visualization, writing—original draft preparation.\u003c/p\u003e\n\u003cp\u003eSerap Baş: Supervision, validation, resources, writing—review and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSelles M, van Osch JAC, Maas M, Boomsma MF, Wellenberg RHH (2024) Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques. Eur J Radiol. ;170:111276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrad.2023.111276\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrad.2023.111276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 38142571\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKalisz K, Buethe J, Saboo SS, Abbara S, Halliburton S, Rajiah P Artifacts at Cardiac CT: Physics and Solutions. Radiographics. 2016 Nov-Dec;36(7):2064\u0026ndash;2083. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1148/rg.2016160079\u003c/span\u003e\u003cspan address=\"10.1148/rg.2016160079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 27768543\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang J, Zhang D, Wilcox C, Heidinger B, Raptopoulos V, Brook A, Brook OR (2017) Metal implants on CT: comparison of iterative reconstruction algorithms for reduction of metal artifacts with single energy and spectral CT scanning in a phantom model. Abdom Radiol (NY). ;42(3):742\u0026ndash;748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00261-016-1023-1\u003c/span\u003e\u003cspan address=\"10.1007/s00261-016-1023-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 28078382\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwartz FR, Tailor T, Gaca JG, Kiefer T, Harrison K, Hughes GC, Ramirez-Giraldo JC, Marin D, Hurwitz LM (2020) Impact of dual energy cardiac CT for metal artefact reduction post aortic valve replacement. Eur J Radiol. ;129:109135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrad.2020.109135\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrad.2020.109135\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32590257\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePennig L, Zopfs D, Gertz R, Bremm J, Zaeske C, Gro\u0026szlig;e Hokamp N, Celik E, Goertz L, Langenbach M, Persigehl T, Gupta A, Borggrefe J, Lennartz S, Laukamp KR (2021) Reduction of CT artifacts from cardiac implantable electronic devices using a combination of virtual monoenergetic images and post-processing algorithms. Eur Radiol 31(9):7151\u0026ndash;7161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-021-07746-8\u003c/span\u003e\u003cspan address=\"10.1007/s00330-021-07746-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 33630164; PMCID: PMC8379133\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrook OR, Gourtsoyianni S, Brook A, Mahadevan A, Wilcox C, Raptopoulos V (2012) Spectral CT with metal artifacts reduction software for improvement of tumor visibility in the vicinity of gold fiducial markers. Radiology. ;263(3):696\u0026ndash;705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1148/radiol.12111170\u003c/span\u003e\u003cspan address=\"10.1148/radiol.12111170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 22416251\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChou R, Chi HY, Lin YH, Ying LK, Chao YJ, Lin CH (2020) Comparison of quantitative measurements of four manufacturer's metal artifact reduction techniques for CT imaging with a self-made acrylic phantom. Technol Health Care 28(S1):273\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/THC-209028\u003c/span\u003e\u003cspan address=\"10.3233/THC-209028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 32364160; PMCID: PMC7369061\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWellenberg RH, Hakvoort ET, Slump CH, Boomsma MF, Maas M, Streekstra GJ (2018) Metal artifact reduction techniques in musculoskeletal CT-imaging. Eur J Radiol. ;107:60\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrad.2018.08.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrad.2018.08.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 30292289\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang JY, Kerns JR, Nute JL, Liu X, Balter PA, Stingo FC, Followill DS, Mirkovic D, Howell RM, Kry SF (2015) An evaluation of three commercially available metal artifact reduction methods for CT imaging. Phys Med Biol. ;60(3):1047-67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/0031-9155/60/3/1047\u003c/span\u003e\u003cspan address=\"10.1088/0031-9155/60/3/1047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 25585685\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKidoh M, Nakaura T, Nakamura S, Tokuyasu S, Osakabe H, Harada K, Yamashita Y (2014) Reduction of dental metallic artefacts in CT: value of a newly developed algorithm for metal artefact reduction (O-MAR). Clin Radiol. ;69(11):e11-6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crad.2014.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.crad.2014.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 25239794\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSubhas N, Primak AN, Obuchowski NA, Gupta A, Polster JM, Krauss A, Iannotti JP, McCollough CH (2014) Iterative metal artifact reduction: evaluation and optimization of technique. Skeletal Radiol. ;43(12):1729-35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00256-014-1987-2\u003c/span\u003e\u003cspan address=\"10.1007/s00256-014-1987-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 25120200\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGjesteby L, De Man B, Jin Y, Paganetti H, Verburg J, Giantsoudi D, Wang G (2016) Metal Artifact Reduction in CT: Where Are We After Four Decades? IEEE Access 4:5826\u0026ndash;5849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/ACCESS.2016.2608621\u003c/span\u003e\u003cspan address=\"10.1109/ACCESS.2016.2608621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoos J, Lanzman RS, Meineke A, Heusch P, Aissa J, Schleich C, Kr\u0026ouml;pil P, Antoch G, Kr\u0026ouml;ger JR (2015) Dose monitoring using the DICOM structured report: assessment of the relationship between cumulative radiation exposure and BMI in abdominal CT. Clin Radiol. ;70(2):176\u0026thinsp;\u0026ndash;\u0026thinsp;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crad.2014.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.crad.2014.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 25468640\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatsura M, Sato J, Akahane M, Kunimatsu A, Abe O (2018) Mar-Apr;38(2):450\u0026ndash;461 Current and Novel Techniques for Metal Artifact Reduction at CT: Practical Guide for Radiologists. Radiographics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1148/rg.2018170102\u003c/span\u003e\u003cspan address=\"10.1148/rg.2018170102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 29528826\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBongers MN, Schabel C, Thomas C, Raupach R, Notohamiprodjo M, Nikolaou K, Bamberg F (2015) Comparison and combination of dual-energy- and iterative-based metal artefact reduction on hip prosthesis and dental implants. PLoS ONE 10(11):e0143584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0143584\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0143584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 26571123; PMCID: PMC4646649\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLell MM, Kachelrie\u0026szlig; M Recent and Upcoming Technological Developments in Computed Tomography: High Speed, Low Dose, Learning D (2020) Multienergy. Invest Radiol. ;55(1):8\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/RLI.0000000000000601\u003c/span\u003e\u003cspan address=\"10.1097/RLI.0000000000000601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31688657\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWillemink MJ, No\u0026euml;l PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29(5):2185\u0026ndash;2195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-018-5810-7\u003c/span\u003e\u003cspan address=\"10.1007/s00330-018-5810-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 30377791; PMCID: PMC6434931\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeyer LL, Schoepf UJ, Meinel FG, Nance JW Jr, Bastarrika G, Leipsic JA, Paul NS, Rengo M, Laghi A, De Cecco CN (2015) State of the Art: Iterative CT Reconstruction Techniques. Radiology. ;276(2):339\u0026thinsp;\u0026ndash;\u0026thinsp;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1148/radiol.2015132766\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2015132766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 26203706\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorsbach F, Bickelhaupt S, Wanner GA, Krauss A, Schmidt B, Alkadhi H (2013) Reduction of metal artifacts from hip prostheses on CT images of the pelvis: value of iterative reconstructions. Radiology. ;268(1):237\u0026thinsp;\u0026ndash;\u0026thinsp;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1148/radiol.13122089\u003c/span\u003e\u003cspan address=\"10.1148/radiol.13122089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 23513242\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee MJ, Kim S, Lee SA, Song HT, Huh YM, Kim DH, Han SH, Suh JS Overcoming artifacts from metallic orthopedic implants at high-field-strength MR imaging and multi-detector CT. Radiographics. 2007 May-Jun;27(3):791\u0026ndash;803. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1148/rg.273065087\u003c/span\u003e\u003cspan address=\"10.1148/rg.273065087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 17495292\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBamberg F, Dierks A, Nikolaou K, Reiser MF, Becker CR, Johnson TR (2011) Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. Eur Radiol. ;21(7):1424-9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-011-2062-1\u003c/span\u003e\u003cspan address=\"10.1007/s00330-011-2062-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 21249371\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"artifact, pacemaker, CT, thorax, reconstruction, image processing, image enhancement","lastPublishedDoi":"10.21203/rs.3.rs-7842222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7842222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo evaluate whether Smart Metal Artifact Reduction (SMAR) used in combination with adaptive statistical iterative reconstruction (ASIR) improves chest CT image quality in patients with cardiac implantable electronic devices (CIEDs), compared with ASIR alone.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective study, 30 CIED patients who underwent chest CT with both ASIR and ASIR\u0026thinsp;+\u0026thinsp;SMAR reconstructions were included. Two readers (16 and 10 years of chest CT experience) assessed subjective image quality and artifacts on soft-tissue, bone, and lung windows; objective artifact load was quantified using the standard deviation of Hounsfield Units in regions of interest adjacent to the generator. Scans were acquired on a single-source 512-slice CT system with standard thoracic parameters; SMAR reconstructions used vendor \u0026ldquo;thorax\u0026rdquo; settings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eASIR\u0026thinsp;+\u0026thinsp;SMAR reduced peri-device artifact magnitude relative to ASIR alone on objective measurements and improved overall diagnostic image quality across evaluated windows (average of two readers). However, far-field lung streaks were observed in 13 out of 30 patients, and in 5 cases, MAR-related artifacts limited assessment, indicating distance-dependent effects.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSMAR complements ASIR by enhancing depiction of the device and near-to mid-field regions, increasing diagnostic confidence. Radiologists should review ASIR-only images for distant lung parenchyma, where occasional MAR-induced streaks may limit evaluation.\u003c/p\u003e","manuscriptTitle":"The Effectiveness of a Smart Metal Artifact Reduction Technique in Chest CT of Patients with Cardiac Implantable Electronic Devices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 05:23:20","doi":"10.21203/rs.3.rs-7842222/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8f32f8c-1dc2-4607-afab-ace2414a9f47","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-09T22:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 05:23:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7842222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7842222","identity":"rs-7842222","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}