scholarly journals Comparison of quantitative measurements of four manufacturer’s metal artifact reduction techniques for CT imaging with a self-made acrylic phantom

2020 ◽  
Vol 28 ◽  
pp. 273-287 ◽  
Author(s):  
Ryan Chou ◽  
Hung-Yi Chi ◽  
Yi-Hung Lin ◽  
Liu-Kuo Ying ◽  
Yu-Ju Chao ◽  
...  
Keyword(s):  
Ct Imaging ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Reduction Techniques ◽  
Quantitative Measurements

Metal artifact reduction techniques in musculoskeletal CT-imaging

2018 ◽  
Vol 107 ◽  
pp. 60-69 ◽  
Author(s):  
R.H.H. Wellenberg ◽  
E.T. Hakvoort ◽  
C.H. Slump ◽  
M.F. Boomsma ◽  
M. Maas ◽  
...  
Keyword(s):  
Ct Imaging ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Reduction Techniques

scholarly journals Metal artifact reduction strategies for improved attenuation correction in hybrid PET/CT imaging

2012 ◽  
Vol 39 (6Part1) ◽  
pp. 3343-3360 ◽  
Author(s):  
Mehrsima Abdoli ◽  
Rudi A. J. O. Dierckx ◽  
Habib Zaidi
Keyword(s):  
Attenuation Correction ◽  
Ct Imaging ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Pet Ct ◽  
Reduction Strategies

scholarly journals A metal artifact reduction method for small field of view CT imaging

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0227656
Author(s):  
Seungwon Choi ◽  
Seunghyuk Moon ◽  
Jongduk Baek
Keyword(s):  
General Assumption ◽  
Ct Imaging ◽  
Previous Method ◽  
Field Of View ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Missing Information ◽  
Data Truncation ◽  
Small Field Of View

Several sinogram inpainting based metal artifact reduction (MAR) methods have been proposed to reduce metal artifact in CT imaging. The sinogram inpainting method treats metal trace regions as missing data and estimates the missing information. However, a general assumption with these methods is that data truncation does not occur and that all metal objects still reside within the field-of-view (FOV). These assumptions are usually violated when the FOV is smaller than the object. Thus, existing inpainting based MAR methods are not effective. In this paper, we propose a new MAR method to effectively reduce metal artifact in the presence of data truncation. The main principle of the proposed method involves using a newly synthesized sinogram instead of the originally measured sinogram. The initial reconstruction step involves obtaining a small FOV image with the truncation artifact removed. The final step is to conduct sinogram inpainting based MAR methods, i.e., linear and normalized MAR methods, on the synthesized sinogram from the previous step. The proposed method was verified for extended cardiac-torso simulations, clinical data, and experimental data, and its performance was quantitatively compared with those of previous methods (i.e., linear and normalized MAR methods directly applied to the originally measured sinogram data). The effectiveness of the proposed method was further demonstrated by reducing the residual metal artifact that were present in the reconstructed images obtained using the previous method.

scholarly journals Metal About the Hip and Artifact Reduction Techniques: From Basic Concepts to Advanced Imaging

2019 ◽  
Vol 23 (03) ◽  
pp. e68-e81 ◽  
Author(s):  
Iman Khodarahmi ◽  
Amanda Isaac ◽  
Elliot K. Fishman ◽  
Danoob Dalili ◽  
Jan Fritz
Keyword(s):  
Image Quality ◽  
Pulse Sequences ◽  
Dual Energy Ct ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Reconstruction Algorithms ◽  
Radiographic Findings ◽  
Reduction Techniques ◽  
Ct Data

AbstractPromising outcomes of hip replacement interventions in this era of aging populations have led to higher demands for hip arthroplasty procedures. These require effective methods and techniques for the detection of postoperative outcomes and complications. Based on the presence or absence of radiographic findings, magnetic resonance imaging (MRI) and computed tomography (CT) may be required to detect and further characterize different causes of failing implants. Yet metal-related artifacts degrade image quality and pose significant challenges for adequate image quality. To mitigate such artifacts in MRI, a set of techniques, collectively known as metal artifact reduction sequence (MARS) MRI, were developed that optimize the framework of the conventional pulse sequences and exploit novel multispectral and multispatial imaging methods such as Slice Encoding for Metal Artifact Correction (SEMAC) and Multi-Acquisition Variable-Resonance Image Combination (MAVRIC). Metal-induced artifacts on CT can be effectively reduced with virtual monochromatic reconstruction of dual-energy CT data sets, metal artifact reduction reconstruction algorithms, and postprocessing image visualization techniques.

Impact of iterative metal artifact reduction on diagnostic image quality in patients with dental hardware

2016 ◽  
Vol 58 (3) ◽  
pp. 279-285 ◽  
Author(s):  
Jakob Weiß ◽  
Christoph Schabel ◽  
Malte Bongers ◽  
Rainer Raupach ◽  
Stephan Clasen ◽  
...  
Keyword(s):  
Image Quality ◽  
Ct Imaging ◽  
Diagnostic Image Quality ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Diagnostic Image ◽  
Anatomical Structures ◽  
Dental Fillings

Background Metal artifacts often impair diagnostic accuracy in computed tomography (CT) imaging. Therefore, effective and workflow implemented metal artifact reduction algorithms are crucial to gain higher diagnostic image quality in patients with metallic hardware. Purpose To assess the clinical performance of a novel iterative metal artifact reduction (iMAR) algorithm for CT in patients with dental fillings. Material and Methods Thirty consecutive patients scheduled for CT imaging and dental fillings were included in the analysis. All patients underwent CT imaging using a second generation dual-source CT scanner (120 kV single-energy; 100/Sn140 kV in dual-energy, 219 mAs, gantry rotation time 0.28–1/s, collimation 0.6 mm) as part of their clinical work-up. Post-processing included standard kernel (B49) and an iterative MAR algorithm. Image quality and diagnostic value were assessed qualitatively (Likert scale) and quantitatively (HU ± SD) by two reviewers independently. Results All 30 patients were included in the analysis, with equal reconstruction times for iMAR and standard reconstruction (17 s ± 0.5 vs. 19 s ± 0.5; P > 0.05). Visual image quality was significantly higher for iMAR as compared with standard reconstruction (3.8 ± 0.5 vs. 2.6 ± 0.5; P < 0.0001, respectively) and showed improved evaluation of adjacent anatomical structures. Similarly, HU-based measurements of degree of artifacts were significantly lower in the iMAR reconstructions as compared with the standard reconstruction (0.9 ± 1.6 vs. –20 ± 47; P < 0.05, respectively). Conclusion The tested iterative, raw-data based reconstruction MAR algorithm allows for a significant reduction of metal artifacts and improved evaluation of adjacent anatomical structures in the head and neck area in patients with dental hardware.

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