Blar i Fakultet for medisin og helsevitenskap (MH) på tidsskrift "BMC Medical Imaging"
Viser treff 1-6 av 6
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Animal study assessing safety of an acoustic coupling fluid that holds the potential to avoid surgically induced artifacts in 3D ultrasound guided operations
(Journal article; Peer reviewed, 2014)Background Use of ultrasound in brain tumor surgery is common. The difference in attenuation between brain and isotonic saline may cause artifacts that degrade the ultrasound images, potentially affecting resection grades ... -
Automatic quantification of left ventricular function by medical students using ultrasound
(Peer reviewed; Journal article, 2020)Background Automatic analyses of echocardiograms may support inexperienced users in quantifying left ventricular (LV) function. We have developed an algorithm for fully automatic measurements of mitral annular plane ... -
Characterizing and quantifying low-value diagnostic imaging internationally: a scoping review
(Peer reviewed; Journal article, 2022)Background Inappropriate and wasteful use of health care resources is a common problem, constituting 10–34% of health services spending in the western world. Even though diagnostic imaging is vital for identifying correct ... -
Comparison of contrast in brightness mode and strain ultrasonography of glial brain tumours
(Journal article; Peer reviewed, 2012)Background Image contrast between normal tissue and brain tumours may sometimes appear to be low in intraoperative ultrasound. Ultrasound imaging of strain is an image modality that has been recently explored for ... -
Does health differ between participants and non-participants in the MRI-HUNT study, a population based neuroimaging study? The Nord-Trøndelag health studies 1984-2009
(Journal article; Peer reviewed, 2012)Background Bias with regard to participation in epidemiological studies can have a large impact on the generalizability of results. Our aim was to investigate the direction and magnitude of potential bias by comparing ... -
Three-stage segmentation of lung region from CT images using deep neural networks
(Journal article; Peer reviewed, 2021)