Browsing NTNU Open by Author "Løvstakken, Lasse"
Now showing items 1-20 of 80
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3D Myocardial Mechanical Wave Measurements: Toward In Vivo 3D Myocardial Elasticity Mapping
Salles, Sebastien; Espeland, Torvald; Rodriguez-Molares, Alfonso; Aase, Svein Arne; Hammer, Tommy; Støylen, Asbjørn; Aakhus, Svend; Løvstakken, Lasse; Torp, Hans (Peer reviewed; Journal article, 2020)Objectives This study aimed to investigate the potential of a novel 3-dimensional (3D) mechanical wave velocity mapping technique, based on the natural mechanical waves produced by the heart itself, to approach a noninvasive ... -
3D Segmentation of the Left Ventricle in Echocardiography Based on Deep Learning
Steinsland, Erik Nikolai (Master thesis, 2020)3D ekkokardiografi har blitt et nyttig verktøy for nøyaktig segmentering og volummåling av venstre ventrikkel, da ultralyd anses som trygt og tilgjengelig sammenlignet med andre medisinske avbildningsmetoder. Manuell sporing ... -
4D ultrasound vector flow imaging for intraventricular flow assessment
Wigen, Morten Smedsrud (Doctoral theses at NTNU;2019:38, Doctoral thesis, 2019)4D ultralyd vector flow avbildning for evaluering av blodstrøm i hjertet Ultralyd er den mest utbredte modaliteten for evaluering av hjertefunksjon grunnet tilgjengelighet, lav kostnad og sanntidsvisning. Ekkokardiografi ... -
A fast 4D B-spline framework for model-based reconstruction and regularization in vector flow imaging
Grønli, Thomas; Wigen, Morten Smedsrud; Segers, Patrick; Løvstakken, Lasse (Journal article; Peer reviewed, 2018)A generic framework for model-based regularization and reconstruction is described, with applications in a wide range of noisy measurement scenarios. The framework employs automatic differentiation and stochastic gradient ... -
A framework for the simulation and validation of acoustic fields from medical ultrasound transducers
Bakstad, Ole (Master thesis, 2012)The unified simulation framework for medical ultrasound, FieldSim, cur- rently supports linear and non-linear simulations by using Field II and Aber- sim, respectively. In this thesis the quasi-linear simulation tool, ... -
A Scientific Tool for Real-time Acquisition and Analysis of Cardiovascular Measurements: Including ultrasound and other physiological data sources
Wigen, Morten Smedsrud (Master thesis, 2013)En programvare for studier av karsystemet, for både eksperimentelle og kliniske forsøk, skal utvikles i denne oppgaven. Data fra både ultralyd og andre fysiologiske kilder, som trykk, blodstrøm og volum, skal kunne mottas ... -
A System for the Acquisition and Analysis of Invasive and Non-invasive Measurements used to quantify Cardiovascular Performance
Omejer, Ole Øvergaard (Master thesis, 2011)Invasive measurements of cardiac functioning allows for more accurate measures of cardiac functioning than non-invasive measurements. However, invasive measurements is often not available in clinical settings. By comparing ... -
Adaptive Clutter Filtering for Improved Measurement of Cardiac Blood Velocities
Vågsholm, Beate Haram (Master thesis, 2017)In ultrasound color flow images of the heart, irregular blood flow patterns might be an early indication of heart disease. The blood flow is possible to image using multi-dimensional blood velocity estimation like speckle ... -
Adaptive speckle tracking algorithms for improved ultrasound blood flow imaging
Drange, Linn Siri (Master thesis, 2013)This thesis will present several ways to further improve the robustness of the speckle tracking method. The speckle tracking algorithm was made more adaptive to the velocity changes over time, utilizing velocity estimates ... -
Age Estimation from B-mode Echocardiography with 3D Convolutional Neural Networks
Fiorito, Adrian Meidell (Master thesis, 2019)Ekkokardiografi er en ikke-invasiv og sikker metode som bruker ultralyd for avbildning av hjertet. Som på mange andre felt utfører metoder for dyp læring, spesifikt \textit{Convolutional neural networks} (CNNs), oppgaver ... -
Annotation Web - An open-source web-based annotation tool for ultrasound images
Smistad, Erik; Østvik, Andreas; Løvstakken, Lasse (Peer reviewed; Journal article, 2021)The use of deep learning and other machine learning techniques requires large amounts of annotated image data. There exist several tools to annotate images, however to our knowledge there are no tools made specifically for ... -
Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography
Salte, Ivar M.; Østvik, Andreas; Smistad, Erik; Melichova, Daniela; Nguyen, Thuy Mi; Karlsen, Sigve; Brunvand, Harald; Haugaa, Kristina H.; Edvardsen, Thor; Løvstakken, Lasse; Grenne, Bjørnar (Journal article; Peer reviewed, 2021)Objectives This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible ... -
Assessment of Early Diastolic Intraventricular Pressure Difference in Children by Blood Speckle-Tracking Echocardiography
Sørensen, Kristian; Fadnes, Solveig; Mertens, Luc; Henry, Matthew; Segers, Patrick; Løvstakken, Lasse; Nyrnes, Siri Ann (Peer reviewed; Journal article, 2023)Background: The lack of reliable echocardiographic techniques to assess diastolic function in children is a major clinical limitation. Our aim was to develop and validate the intraventricular pressure difference (IVPD) ... -
Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning
Hu, Jieyu; Olaisen, Sindre Hellum; Smistad, Erik; Dalen, Håvard; Løvstakken, Lasse (Journal article; Peer reviewed, 2023)Objective: Echocardiography, a critical tool for assessing left atrial (LA) volume, often relies on manual or semi-automated measurements. This study introduces a fully automated, real-time method for measuring LA volume ... -
Automatic annotation of structures in echocardiography using deep learning
Jansen, Amanda Kathrine (Master thesis, 2021)I dag utføres ultralydundersøkelse av hjertet vanligvis av en lege som har tilegnet seg spesialisering i tolkning av ultralydbilder. Som et resultat kan det være utfordrende for ikke-eksperter å bruke ekkokardiografi. Å ... -
Automatic Detection of Mitral Annular Plane Systolic Excursion from Transesophageal Echocardiography Using Deep Learning
Nordal, Trym (Master thesis, 2019)Perioperativ monitorering av hjertet til pasienter som gjennomgår operasjoner er nødvendig for å forsikre at hjertets funksjon gjennoprettes. I dag gjennomføres periopertiv monitorering av hjertet manuelt, og baserer seg ... -
Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks
Viggen, Erlend Magnus; Merciu, Ioan Alexandru; Løvstakken, Lasse; Måsøy, Svein-Erik (Peer reviewed; Journal article, 2020)The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on ... -
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 measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases
Olaisen, Sindre Hellum; Smistad, Erik; Espeland, Torvald; Hu, Jieyu; Pasdeloup, David Francis Pierre; Østvik, Andreas; Aakhus, Svend; Rösner, Assami; Malm, Siri; Stylidis, Michael; Holte, Espen; Grenne, Bjørnar Leangen; Løvstakken, Lasse; Dalen, Håvard (Peer reviewed; Journal article, 2023)Aims Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully ... -
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 ...