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 Efficacy of metal artifact reduction algorithm of cone-beam computed tomography for detection of fenestration and dehiscence around dental implants (Preprint)

2020 ◽  
Author(s):  
Fatemeh Salemi ◽  
Mohamad Reza Jamalpour ◽  
Amir Eskandarloo ◽  
Leili Tapak ◽  
Narges Rahimi
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Roc Curve ◽  
Titanium Implants ◽  
Cone Beam ◽  
Kappa Statistics ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Sensitivity Specificity

UNSTRUCTURED This study aimed to assess the efficacy of metal artifact reduction (MAR) algorithm of two cone-beam computed tomography (CBCT) systems for detection of peri-implant fenestration and dehiscence. Thirty-six titanium implants were placed in bone blocks of bovine ribs. Fenestration and dehiscence were created in the buccal bone around implants using a round bur. The bone blocks were then mounted in a wax rim to simulate the mandible. CBCT images were obtained using Cranex 3D and ProMax 3D CBCT systems with and without MAR algorithm before and after creation of defects. Two experienced radiologists observed the images twice with a 2-week interval. Data were analyzed using SPSS software version 22.The Kappa coefficient of agreement, the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, accuracy of different imaging modalities were calculated and analyzed. According to the kappa statistics, the intra- and inter-observer agreements were higher for images without the MAR algorithm compared with those with the MAR algorithm. In both CBCT systems, use of MAR algorithm decreased the area under the ROC curve and subsequently the diagnostic accuracy for detection of fenestration and dehiscence. The sensitivity, specificity and accuracy of both CBCT systems were higher in absence of the MAR algorithm. The specificity of ProMax 3D for detection of fenestration was equal with/without the MAR algorithm. Although CBCT is suitable for detection of peri-implant defects, application of the MAR algorithm does not enhance the detection of peri-implant fenestration and dehiscence.

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