2021
DOI: 10.1186/s43055-021-00530-0
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Common computed tomography artifact: source and avoidance

Abstract: Background Artifacts have significantly degraded the quality of computed tomography (CT) images, to the extent of making them unusable for diagnosis. The types of artifact that could be used are as follows: (a) streaking, which is commonly due to a discrepancy in a single measurement, (b) shading, which is due to a group of channels deviating gradually from the true measurement, (c) rings, which are due to errors in individual detector calibration and (d) distortion, which is due to helical rec… Show more

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Cited by 9 publications
(2 citation statements)
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“…Maturen et al [6] reported a sensitivity of 94.1% and a negative predictive value of 97.6% for CECT in detecting active bleeding, although detecting bleeding can be challenging in the absence of active extravasation. Furthermore, the presence of metal implants, as seen in our patient, can create artifacts that hinder diagnosis [10]. In the cases presented in Table 1, most lumbar artery injuries were diagnosed using CECT.…”
Section: Discussionmentioning
confidence: 91%
“…Maturen et al [6] reported a sensitivity of 94.1% and a negative predictive value of 97.6% for CECT in detecting active bleeding, although detecting bleeding can be challenging in the absence of active extravasation. Furthermore, the presence of metal implants, as seen in our patient, can create artifacts that hinder diagnosis [10]. In the cases presented in Table 1, most lumbar artery injuries were diagnosed using CECT.…”
Section: Discussionmentioning
confidence: 91%
“…Modalities like MRIs, CT scans, and X-rays reveal detailed internal structures, where the diverse appearances of tissues and overlapping structures can complicate the segmentation process. Additionally, images are often subject to noise, artifacts, and distortions due to factors like patient movement, limitations of imaging devices, or specific scanning parameters [18,19]. These imperfections can lead to inaccurate segmentations.…”
Section: Semantic Segmentation With Aimentioning
confidence: 99%