Blar i NTNU Open på forfatter "Andersen, Hilde Kjernlie"
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Image quality with iterative reconstruction techniques in CT of the lungs?A phantom study
Andersen, Hilde Kjernlie; Volgyes, David; Martinsen, Anne Catrine Trægde (Journal article; Peer reviewed, 2018)Background Iterative reconstruction techniques for reducing radiation dose and improving image quality in CT have proved to work differently for different patient sizes, dose levels, and anatomical areas. Purpose This ... -
Image texture and radiation dose properties in CT
Możejko, Dawid; Andersen, Hilde Kjernlie; Pedersen, Marius; Waaler, Dag; Martinsen, Anne Catrine Trægde (Journal article; Peer reviewed, 2016)The aim of this study was to compare image noise properties of GE Discovery HD 750 and Toshiba Aquilion ONE. The uniformity section of a Catphan 600 image quality assurance phantom was scanned with both scanners, at different ... -
Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction-A 20-reader study on a semi-anthropomorphic liver phantom
Njølstad, Tormund; Dybwad, Anniken; Salvesen, Øyvind Olav; Andersen, Hilde Kjernlie; Schulz, Anselm (Peer reviewed; Journal article, 2022)Background A novel deep learning image reconstruction (DLIR) algorithm for CT has recently been clinically approved. Purpose To assess low-contrast detectability and dose reduction potential for CT images reconstructed ... -
Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction—A 20-reader study on a semi-anthropomorphic liver phantom
Njølstad, Tormund Salvesen; Jensen, Kristin; Dybwad, Anniken; Salvesen, Øyvind; Andersen, Hilde Kjernlie; Schulz, Anselm (Peer reviewed; Journal article, 2022)Background A novel deep learning image reconstruction (DLIR) algorithm for CT has recently been clinically approved. Purpose To assess low-contrast detectability and dose reduction potential for CT images reconstructed ... -
Three-stage segmentation of lung region from CT images using deep neural networks
Osadebey, Michael; Andersen, Hilde Kjernlie; Waaler, Dag; Fosså, Kristian; Martinsen, Anne Catrine Trægde; Pedersen, Marius (Journal article; Peer reviewed, 2021)