Information till studenter och medarbetare med anledning av covid-19 (Uppdaterad: 26 januari 2021)

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Umeå universitet
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Tufve Nyholm

PhD in Radiation Physics in 2009 and professor in 2018. The PhD thesis was focused on algorithms for dose calculations and quality assurance aspects on external radiotherapy. This work is partly the basis for the EqualDose software.

Currently, my main area of research is MRI in cancer care, including both integration of MR in the radiotherapy workflow and use of MR techniques for early assessment of treatment response.

Forskningsledare

Tufve Nyholm Professor, sjukhusfysiker
E-post
E-post
Telefon
090-786 72 92
Bildgivande metoder för förbättrad strålbehandling
Bildgivande metoder för förbättrad strålbehandling

Tufve Nyholms forskning handlar om metod- och teknikutveckling inom strålterapi.

52 VR-miljoner till forskning inom naturvetenskap och teknik
52 VR-miljoner till forskning inom naturvetenskap och teknik

Vetenskapsrådet delar ut nära 52 miljoner till projekt inom naturvetenskap och teknikvetenskap.

Publikationer

2020
A Question-Centric Model for Visual Question Answering in Medical Imaging
IEEE Transactions on Medical Imaging, IEEE 2020, Vol. 39, (9) : 2856-2868
Vu, Minh Hoang; Löfstedt, Tommy; Nyholm, Tufve; et al.
2020
Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation
Physics in Medicine and Biology, Institute of Physics (IOP) 2020, Vol. 65, (22)
Löfstedt, Tommy; Hellström, Max; Bylund, Mikael; et al.
2020
Dosimetric effects of adaptive prostate cancer radiotherapy in an MR-linac workflow
Radiation Oncology, BioMed Central 2020, Vol. 15, (1)
Mannerberg, Annika; Persson, Emilia; Jonsson, Joakim; et al.
2020
Evaluation of multislice inputs to convolutional neural networks for medical image segmentation
Medical physics (Lancaster), John Wiley & Sons 2020, Vol. 47, (12) : 6216-6231
Vu, Minh H.; Grimbergen, Guus; Nyholm, Tufve; et al.
2020
Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images
EJNMMI Physics, Springer 2020, Vol. 7, (1)
Wallstén, Elin; Axelsson, Jan; Jonsson, Joakim; et al.
2019
A constrained singular value decomposition method that integrates sparsity and orthogonality
PLoS ONE, Vol. 14, (3)
Guillemot, Vincent; Beaton, Derek; Gloaguen, Arnaud; et al.
2019
Attenuation correction of a flat table top for radiation therapy in hybrid PET/MR using CT- and Ge-68/Ga-68 transmission scan-based mu-maps
Physica medica (Testo stampato), ELSEVIER SCI LTD 2019, Vol. 65 : 76-83
Witoszynskyj, Stephan; Andrzejewski, Piotr; Georg, Dietmar; et al.
2019
Bayesian sparsity estimation in compressive sensing with application to MR images
Communications in Statistics: Case Studies, Data Analysis and Applications, Taylor & Francis Group 2019, Vol. 5, (4) : 415-431
Wang, Jianfeng; Zhou, Zhiyong; Garpebring, Anders; et al.
2019
Dosimetric Impact of MRI Distortions: A Study on Head and Neck Cancers
International Journal of Radiation Oncology, Biology, Physics, Elsevier 2019, Vol. 103, (4) : 994-1003
Adjeiwaah, Mary; Bylund, Mikael; Lundman, Josef A.; et al.
2019
Early changes in multiparametric imaging parameters during radiotherapy of squamous carcinoma
Radiotherapy and Oncology, Elsevier 2019, Vol. 132 : 63-63
Zborayova, Katarina; Antti, Henrik; Blomqvist, L.; et al.