Foto: UiT
Samuel Kuttner
Santiago Cepeda,
Olga Esteban-Sinovas,
Luigi Tommaso Luppino,
Samuel Kuttner,
Marek Wodzinski,
Ole Skeidsvoll Solheim
m.fl.:
Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma
Santiago Cepeda,
Roberto Romero,
Lidia Luque Fernandez,
Daniel García-Pérez,
Guillermo Blasco,
Luigi Tommaso Luppino
m.fl.:
Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: Development, external validation, and model comparison
Samuel Kuttner,
Luigi Tommaso Luppino,
Laurence Convert,
Otman Sarrhini,
Roger Lecomte,
Michael Christian Kampffmeyer
m.fl.:
Deep learning derived input function in dynamic [18F]FDG PET imaging of mice
Santiago Cepeda,
Luigi Tommaso Luppino,
Angel Pérez-Núñez,
Ole Skeidsvoll Solheim,
Sergio García-García,
María Velasco-Casares
m.fl.:
Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI
Seyed Esmaeil Dorraji,
Ana Oteiza,
Samuel Kuttner,
Armas Maria Montserrat Martin,
Premasany Kanapathippillai,
Sara Garbarino
m.fl.:
Positron emission tomography and single photon emission computed tomography imaging of tertiary lymphoid structures during the development of lupus nephritis
Samuel Kuttner,
Kristoffer Knutsen Wickstrøm,
Mark Lubberink,
Andreas Tolf,
Joachim Burman,
Rune Sundset
m.fl.:
Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function
Samuel Kuttner,
Kristoffer Knutsen Wickstrøm,
Gustav Kalda,
Seyed Esmaeil Dorraji,
Montserrat Martin-Armas,
Ana Oteiza
m.fl.:
Machine learning derived input-function in a dynamic 18F-FDG PET study of mice
Stine Hansen,
Samuel Kuttner,
Michael Kampffmeyer,
Tom-Vegard Markussen,
Rune Sundset,
Silje Kjærnes Øen
m.fl.:
Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI
Samuel Kuttner,
Martin Lyngby Lassen,
Silje Kjærnes Øen,
Rune Sundset,
Thomas Beyer,
Live Eikenes
:
Quantitative PET/MR imaging of lung cancer in the presence of artifacts in the MR-based attenuation correction maps
Richard Fjellaksel,
Ana Oteiza,
Montserrat Martin-Armas,
Patrick Riss,
Ole Kristian Hjelstuen,
Samuel Kuttner
m.fl.:
First in vivo evaluation of a potential SPECT brain radiotracer for the gonadotropin releasing hormone receptor
Pirjo Åström,
Krista Juurikka,
Elin Synnøve Hadler-Olsen,
Gunbjørg Svineng,
Nilva K. Cervigne,
Ricardo D. Coletta
m.fl.:
The interplay of matrix metalloproteinase-8, transforming growth factor-β1 and vascular endothelial growth factor-C cooperatively contributes to the aggressiveness of oral tongue squamous cell carcinoma
Arnoldus S Blix,
Samuel Kuttner,
Edvard Berger Messelt
:
Ascending aorta of hooded seals with particular emphasis on its vasa vasorum
Samuel Kuttner,
Robert Bujila,
Mika Kortesniemi,
Henrik Andersson,
Love Kull,
Bjørn Helge Østerås
m.fl.:
A proposed protocol for acceptance and constancy control of computed tomography systems: A Nordic Association for Clinical Physics (NACP) work group report
Christian Salomonsen,
Kristoffer Wickstrøm,
Samuel Kuttner,
Elisabeth Wetzer
:
Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [18F]FDG PET Images
16. oktober 2025 ARKIV
Christian Salomonsen,
Kristoffer Wickstrøm,
Samuel Kuttner,
Elisabeth Wetzer
:
Physics-informed deep learning for improved input function estimation in motion-blurred dynamic [18F]FDG PET images
24. september 2025 ARKIV
Christian Salomonsen,
Kristoffer Wickstrøm,
Samuel Kuttner,
Elisabeth Wetzer
:
Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [18F]FDG PET Images
27. september 2025 ARKIV
Christian Salomonsen,
Kristoffer Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Physics-Informed Machine Learning for dynamic PET modeling
2025 ARKIV
Christian Salomonsen,
Kristoffer Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Physics-informed deep learning for predicting the arterial input function in dynamic PET imaging
2025 ARKIV
Christian Salomonsen,
Kristoffer Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Physics-informed deep learning for improved input function estimation in motion-blurred dynamic [18F]FDG PET images
2025 ARKIV
Benjamin Ricaud,
Samuel Kuttner,
Erik Amandus Heggeli,
Petter Bjørklund,
Steffen Aagaard Sørensen
:
Kunstig intelligens-utstilling på Nordnorsk vitensenter
21. september 2024 ARKIV
Christian Salomonsen,
Samuel Kuttner,
Elin Kile
:
Kunstig intelligens: Hvordan kan datamaskiner se?
2024 ARKIV
Akos Samuel Kuttner,
Benjamin Ricaud,
Petter Bjørklund,
Mona Holmø
:
Vil vekke barns interesse for kunstig intelligens med ny utstilling
uit.no 2024 ARKIV
Magnus Aamo Holte,
Benjamin Ricaud,
Samuel Kuttner,
tove e Marienborg,
Erik Amandus Heggeli
:
Klar med KI-utstilling
18. september 2024 ARKIV
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024 ARKIV
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024 ARKIV
Christian Salomonsen,
Kristoffer Knutsen Wickstrøm,
Elisabeth Wetzer,
Samuel Kuttner
:
Kinetic modeling based compound loss for deep learning derived input function prediction in dynamic [18F]FDG PET images of mice
2024 ARKIV
Benjamin Ricaud,
Magnus Aamo Holte,
Samuel Kuttner
:
Nordnorsk vitensenter: Klar med KI-utstilling
18. september 2024 ARKIV
Joel Burman,
Elin Kile,
Karl Øyvind Mikalsen,
Samuel Kuttner
:
PET-MRI-based prediction models for classifying prostate cancer
2024 ARKIV
Elin Kile,
Samuel Kuttner
:
Tracer kinetic modeling from short dynamic PSMA PET scans of prostate cancer patients
2024 ARKIV
Santiago Cepeda,
Luigi Tommaso Luppino,
Marek Wodsinski,
Ole Skeidsvoll Solheim,
Angel Pérez-Núñez,
Sergio García-García
m.fl.:
External evaluation of a machine learning model employing radiomics to identify regions of local recurrence in glioblastoma from postoperative MRI
Akos Samuel Kuttner,
Per-Christian Johansen,
Rune Sundset,
Katharina Lindberg,
Kent Alexander Johansen,
Anneli Johansen
m.fl.:
Starter avansert forskning på kreftdiagnostikk
Pingvinavisa 2023 ARKIV
Akos Samuel Kuttner,
Luigi Tommaso Luppino
:
Deep learning based input-function in positron emission tomography imaging: The story of an innovation project
2023 ARKIV
Santiago Cepeda,
Luigi Tommaso Luppino,
Ole Skeidsvoll Solheim,
Angel Pérez-Núñez,
Sergio García-García,
Anna Maria Karlberg
m.fl.:
Machine Learning-based Identification of Local Recurrence Regions in Glioblastoma using Postoperative MRI: Implications for Survival Prognostication
Fredrik Emil Aspheim,
Luigi Tommaso Luppino,
Samuel Kuttner
:
Deep learning-derived input function in dynamic PET imaging
2023 ARKIV
Luigi Tommaso Luppino,
Samuel Kuttner,
Fredrik Emil Aspheim
:
Explainability of deep-learning-derived input function in dynamic PET imaging
UiT Norges arktiske universitet 01. juni 2023 ARKIV
Fredrik Emil Aspheim,
Samuel Kuttner,
Luigi Tommaso Luppino,
Rune Sundset,
Michael Christian Kampffmeyer,
Robert Jenssen
:
Deep learning derived input-function in dynamic PET-imaging
2023 ARKIV
Samuel Kuttner,
Rodrigo Berzaghi
:
Reproducibility of the arterial input function in 18F FDG mouse PET imaging
2023 ARKIV
Fredrik Emil Aspheim,
Luigi Tommaso Luppino,
Michael Christian Kampffmeyer,
Robert Jenssen,
Rune Sundset,
Akos Samuel Kuttner
:
Interpretable deep learning model for input function estimation in small-animal 18F-FDG PET imaging
2023 ARKIV
Klara Leffler,
Luigi Tommaso Luppino,
Samuel Kuttner,
Jan Axelsson
:
Deep learning-based filling of incomplete sinograms from low-cost, long axial field-of-view PET scanners with inter-detector gaps
2023 ARKIV
Cheyma Azzouz,
Luigi Tommaso Luppino,
Samuel Kuttner,
Santiago Cepeda
:
Predicting areas of local recurrence in glioblastoma MRI scans using deep learning
2023 ARKIV
Luigi Tommaso Luppino,
Klara Leffler,
Samuel Kuttner,
Jan Erik Axelsson
:
Deep learning-based filling of incomplete sinograms from low-cost, long axial field-of-view PET scanners with inter-detector gaps
2023 ARKIV
Cheyma Azzouz,
Luigi Tommaso Luppino,
Samuel Kuttner
:
Kunstig intelligens: Hvordan kan datamaskiner se?
2023 ARKIV
Samuel Kuttner,
Luigi Tommaso Luppino,
Kristoffer Wickstrøm,
Nils Thomas Doherty Midtbø,
Seyed Esmaeil Dorraji,
Ana Oteiza
m.fl.:
Deep learning derived input function in dynamic 18F-FDG PET imaging of mice
2022 ARKIV
Ellen Kathrine Bludd,
Akos Samuel Kuttner
:
Kunstig intelligens kan spare kreftpasienter for ubehagelige undersøkelser
Forskning.no 2021 ARKIV
Samuel Kuttner,
Martin Lyngby Lassen,
Silje Kjærnes Øen,
Rune Sundset,
Thomas Beyer,
Live Eikenes
:
The impact of artefacts in MR-based attenuation correction maps on PET/MR images of lung cancer
2018 ARKIV
Aurora Rosvoll Grøndahl,
Samuel Kuttner,
Mattijs Elschot,
Tone Frost Bathen,
Rune Sundset,
Ingerid Skjei Knudtsen
m.fl.:
Maskinlæring og medisinske bilder for bedre diagnostikk og persontilpasset kreftbehandling
HMT - Helsetjenesten Medisinsk Teknikk : helse, medisin, teknikk 2018 ARKIV
Angel Moldes-Anaya,
Lorenzo Ragazzi,
Samuel Kuttner,
Daniel Vincenz,
Jürgen Goldschmidt,
Vegard Brun
m.fl.:
Targetting mnemonic pathways of stress by cerebral perfusion using 99mTc-HMPAO SPECT/CT
2017 ARKIV
Angel Moldes-Anaya,
Samuel Kuttner,
Daniel Vincenz,
Jürgen Goldschmidt,
Vegard Brun,
Kirsten Brun Kjelstrup
:
Targeting mnemonic pathways of stress by cerebral perfusion using 99mTc-HMPAO SPECT/CT
2015 ARKIV
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Forskningsinteresser
Positronemisjonstomografi (PET), Maskinlæring, dyp læring, medisinsk fysikk, medisinsk avbildning, bildeanalyse