scholarly journals Evaluation of New Commercially Available Metal Artifact Reduction (MAR) Algorithm on Both Image Quality and Relative Dosimetry for Patients with Hip Prosthesis or Dental Fillings

2017 ◽  
Vol 06 (02) ◽  
pp. 124-138 ◽  
Author(s):  
Vicky W. Huang ◽  
Kirpal Kohli
Keyword(s):  
Image Quality ◽  
Hip Prosthesis ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Dental Fillings

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.

scholarly journals Effect of metal artifact reduction software on image quality of C-arm cone-beam computed tomography during intracranial aneurysm treatment

2018 ◽  
Vol 24 (3) ◽  
pp. 303-308 ◽  
Author(s):  
Yukiko Enomoto ◽  
Keita Yamauchi ◽  
Takahiko Asano ◽  
Katharina Otani ◽  
Toru Iwama
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Intracranial Aneurysms ◽  
Clinical Findings ◽  
Cone Beam ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Cbct Image

Background and purpose C-arm cone-beam computed tomography (CBCT) has the drawback that image quality is degraded by artifacts caused by implanted metal objects. We evaluated whether metal artifact reduction (MAR) prototype software can improve the subjective image quality of CBCT images of patients with intracranial aneurysms treated with coils or clips. Materials and methods Forty-four patients with intracranial aneurysms implanted with coils (40 patients) or clips (four patients) underwent one CBCT scan from which uncorrected and MAR-corrected CBCT image datasets were reconstructed. Three blinded readers evaluated the image quality of the image sets using a four-point scale (1: Excellent, 2: Good, 3: Poor, 4: Bad). The median scores of the three readers of uncorrected and MAR-corrected images were compared with the paired Wilcoxon signed-rank and inter-reader agreement of change scores was assessed by weighted kappa statistics. The readers also recorded new clinical findings, such as intracranial hemorrhage, air, or surrounding anatomical structures on MAR-corrected images. Results The image quality of MAR-corrected CBCT images was significantly improved compared with the uncorrected CBCT image ( p < 0.001). Additional clinical findings were seen on CBCT images of 70.4% of patients after MAR correction. Conclusion MAR software improved image quality of CBCT images degraded by metal artifacts.

scholarly journals Evaluation of the quality of CT images acquired with smart metal artifact reduction software

2018 ◽  
Vol 13 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Peng Zhou ◽  
Chunling Zhang ◽  
Zhen Gao ◽  
Wangshu Cai ◽  
Deyue Yan ◽  
...  
Keyword(s):  
Image Quality ◽  
Quantitative Assessment ◽  
Objective Assessment ◽  
Ct Images ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Metal Artifacts ◽  
Embolization Coil

AbstractObjectiveTo evaluate the practical effectiveness of smart metal artifact reduction (SMAR) in reducing artifacts caused by metallic implants.MethodsPatients with metal implants underwent computed tomography (CT) examinations on high definition CT scanner, and the data were reconstructed with adaptive statistical iterative reconstruction (ASiR) with value weighted to 40% and smart metal artifact reduction (SMAR) technology. The comparison was assessed by both subjective and objective assessment between the two groups of images. In terms of subjective assessment, three radiologists evaluated image quality and assigned a score for visualization of anatomic structures in the critical areas of interest. Objectively, the absolute CT value of the difference (ΔCT) and artifacts index (AI) were adopted in this study for the quantitative assessment of metal artifacts.ResultsIn subjective image quality assessment, three radiologists scored SMAR images higher than 40% ASiR images (P<0.01) and the result suggested that visualization of critical anatomic structures around the region of the metal object was significantly improved by using SMAR compared with 40% ASiR. The ΔCT and AI for quantitative assessment of metal artifacts showed that SMAR appeared to be superior for reducing metal artifacts (P<0.05) and indicated that this technical approach was more effective in improving the quality of CT images.ConclusionA variety of hardware (dental filling, embolization coil, instrumented spine, hip implant, knee implant) are processed with the SMAR algorithm to demonstrate good recovery of soft tissue around the metal. This artifact reduction allows for the clearer visualization of structures hidden underneath.

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.

Metal artifact reduction (MAR) based on two-compartment physical modeling: evaluation in patients with hip implants

2016 ◽  
Vol 58 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Johannes Boos ◽  
Lino Morris Sawicki ◽  
Rotem Shlomo Lanzman ◽  
Christoph Thomas ◽  
Joel Aissa ◽  
...  
Keyword(s):  
Image Quality ◽  
Near Field ◽  
Image Interpretation ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Hip Implants ◽  
Subjective Image Quality ◽  
Hip Prostheses ◽  
The Impact

Background Artifacts from metallic implants can hinder image interpretation in computed tomography (CT). Image quality can be improved using metal artifact reduction (MAR) techniques. Purpose To evaluate the impact of a MAR algorithm on image quality of CT examinations in comparison to filtered back projection (FBP) in patients with hip prostheses. Material and Methods Twenty-two patients with 25 hip prostheses who underwent clinical abdominopelvic CT on a 64-row CT were included in this retrospective study. Axial images were reconstructed with FBP and five increasing MAR levels (M30–34). Objective artifact strength (OAS) (SIart-SInorm) was assessed by region of interest (ROI) measurements in position of the strongest artifact (SIart) and in an osseous structure without artifact (SInorm) (in Hounsfield units [HU]). Two independent readers evaluated subjective image quality regarding metallic hardware, delineation of bone, adjacent muscle, and pelvic organs on a 5-point scale (1, non-diagnostic; 5, excellent image quality). Artifacts in the near field, far field, and newly induced artifacts due to the MAR technique were analyzed. Results OAS values were: M34: 243.8 ± 155.4 HU; M33: 294.3 ± 197.8 HU; M32: 340.5 ± 210.1 HU; M31: 393.6 ± 225.2 HU; M30: 446.8 ± 224.2 HU and FBP: 528.9 ± 227.7 HU. OAS values were significantly lower for M32–34 compared to FBP ( P < 0.01). For overall subjective image quality, results were: FBP, 2.0 ± 0.2; M30, 2.3 ± 0.8; M31, 2.6 ± 0.5; M32, 3.0 ± 0.6; M33, 3.5 ± 0.6; and M34, 3.8 ± 0.4 ( P < 0.001 for M30–M34 vs. FBP, respectively). Increasing MAR levels resulted in new artifacts in 17% of reconstructions. Conclusion The investigated MAR algorithm led to a significant reduction of artifacts from metallic hip implants. The highest MAR level provided the least severe artifacts and the best overall image quality.

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