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dc.contributor.authorOsadebey, Michael
dc.contributor.authorPedersen, Marius
dc.contributor.authorArnold, Douglas
dc.contributor.authorWendel-Mitoraj, Katrina
dc.date.accessioned2019-01-25T14:22:50Z
dc.date.available2019-01-25T14:22:50Z
dc.date.created2018-08-10T08:31:32Z
dc.date.issued2018
dc.identifier.issn2168-2372
dc.identifier.urihttp://hdl.handle.net/11250/2582418
dc.description.abstractMagnetic resonance imaging (MRI) system images are important components in the development of drugs because it can reveal the underlying pathology in diseases. Unfortunately, the processes of image acquisition, storage, transmission, processing, and analysis can influence image quality with the risk of compromising the reliability of MRI-based data. Therefore, it is necessary to monitor image quality throughout the different stages of the imaging workflow. This report describes a new approach to evaluate the quality of an MRI slice in multi-center clinical trials. The design philosophy assumes that an MRI slice, such as all natural images, possess statistical properties that can describe different levels of contrast degradation. A unique set of pixel configuration is assigned to each possible level of contrast-distorted MRI slice. Invocation of the central limit theorem results in two separate Gaussian distributions. The central limit theorem says that the mean and standard deviation of pixel configuration assigned to each possible level of contrast degradation will follow a normal distribution. The mean of each normal distribution corresponds to the mean and standard deviation of the underlying ideal image. Quality prediction processes for a test image can be summarized into four steps. The first step extracts local contrast feature image from the test image. The second step computes the mean and standard deviation of the feature image. The third step separately standardizes each normal distribution using the mean and standard deviation computed from the feature image. This gives two separate z-scores. The fourth step predicts the lightness contrast quality score and the texture contrast quality score from cumulative distribution function of the appropriate normal distribution. The proposed method was evaluated objectively on brain and cardiac MRI volume data using four different types and levels of degradation. The four types of degradation are Rician noise, circu...nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleImage Quality Evaluation in Clinical Research: A Case Study on Brain and Cardiac MRI Images in Multi-Center Clinical Trialsnb_NO
dc.title.alternativeImage Quality Evaluation in Clinical Research: A Case Study on Brain and Cardiac MRI Images in Multi-Center Clinical Trialsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.journalIEEE Journal of Translational Engineering in Health and Medicinenb_NO
dc.identifier.doi10.1109/JTEHM.2018.2855213
dc.identifier.cristin1600879
dc.relation.projectNorges forskningsråd: 247689nb_NO
dc.description.localcode© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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