Blar i SINTEF Open på forfatter "Løvstakken, Lasse"

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    • Automatic intraoperative estimation of blood flow direction during neurosurgical interventions 

      Iversen, Daniel Høyer; Løvstakken, Lasse; Unsgård, Geirmund; Reinertsen, Ingerid (Journal article; Peer reviewed, 2018)
      Purpose In neurosurgery, reliable information about blood vessel anatomy and flow direction is important to identify, characterize, and avoid damage to the vasculature. Due to ultrasound Doppler angle dependencies and the ...

    • Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning 

      Østvik, Andreas; Smistad, Erik; Espeland, Torvald; Berg, Erik Andreas Rye; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)
      Recent studies in the field of deep learning suggest that motion estimation can be treated as a learnable problem. In this paper we propose a pipeline for functional imaging in echocardiography consisting of four central ...

    • Comparison of ultrasound vector flow imaging and CFD simulations with PIV measurements of flow in a left ventricular outflow trackt phantom - Implications for clinical use and in silico studies 

      Leinan, Paul Roger; Grønli, Thomas; Skjetne, Paal; Wigen, Morten Smedsrud; Urheim, Stig; Løvstakken, Lasse; Dahl, Sigrid Kaarstad (Peer reviewed; Journal article, 2022)
      In this study we have compared two modalities for flow quantification from measurement data; ultrasound (US) and shadow particle image velocimetry (PIV), and a flow simulation model using computational fluid dynamics (CFD). ...

    • Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study 

      Salte, Ivar Mjåland; Østvik, Andreas; Olaisen, Sindre Hellum; Karlsen, Sigve; Dahlslett, Thomas; Smistad, Erik; Eriksen-Volnes, Torfinn Kirknes; Brunvand, Harald; Haugaa, Kristina Ingrid Helena Hermann; Edvardsen, Thor; Dalen, Håvard; Løvstakken, Lasse; Grenne, Bjørnar Leangen (Peer reviewed; Journal article, 2023)
      Aims: Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements ...

    • Fully automatic real-time ejection fraction and MAPSE measurements in 2D echocardiography using deep neural networks 

      Smistad, Erik; Østvik, Andreas; Salte, Ivar Mjåland; Leclerc, Sarah; Bernard, Olivier; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)
      Cardiac ultrasound measurements such as left ventricular volume, ejection fraction (EF) and mitral annular plane systolic excursion (MAPSE) are time consuming and highly observer dependent. In this work, we investigate if ...

    • Myocardial Function Imaging in Echocardiography Using Deep Learning 

      Østvik, Andreas; Salte, Ivar Mjåland; Smistad, Erik; Nguyen, Thuy Mi; Melichova, Daniela; Brunvand, Harald; Haugaa, Kristina; Edvardsen, Thor; Grenne, Bjørnar; Løvstakken, Lasse (Peer reviewed; Journal article, 2021)
      Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated ...

    • Real-Time Automatic Ejection Fraction and Foreshortening Detection Using Deep Learning 

      Smistad, Erik; Salte, Ivar Mjåland; Østvik, Andreas; Melichova, Daniela; Nguyen, Thuy Mi; Haugaa, Kristina; Brunvand, Harald; Edvardsen, Thor; Leclerc, Sarah; Bernard, Olivier; Grenne, Bjørnar; Løvstakken, Lasse (Peer reviewed; Journal article, 2020)
      Volume and ejection fraction (EF) measurements of the left ventricle (LV) in 2-D echocardiography are associated with a high uncertainty not only due to interobserver variability of the manual measurement, but also due to ...

    • Real-Time Echocardiography Guidance for Optimized Apical Standard Views 

      Pasdeloup, David Francis Pierre; Olaisen, Sindre Hellum; Østvik, Andreas; Sæbø, Sigbjørn; Pettersen, Håkon Neergaard; Holte, Espen; Grenne, Bjørnar; Stølen, Stian Bergseng; Smistad, Erik; Aase, Svein Arne; Dalen, Håvard; Løvstakken, Lasse (Peer reviewed; Journal article, 2022)
      Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2-D ultrasound imaging. The reliability of these measurements depends on the correct pose of the ...

    • Real-time Standard View Classification in Transthoracic Echocardiography using Convolutional Neural Networks 

      Østvik, Andreas; Smistad, Erik; Aase, Svein Arne; Haugen, Bjørn Olav; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)
      Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, ...

    • Ultrasound speckle reduction using generative adversial networks 

      Dietrichson, Fabian Sødal; Smistad, Erik; Østvik, Andreas; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)
      Generative adversial networks (GANs) have shown its ability to create realistic and accurate image-to-image transformation. The goal of this work was to investigate whether deep convolutional GANs can learn to perform ...