Convolutional recurrent U‐net for cardiac cine MRI reconstruction via effective spatio‐temporal feature exploitation

The proposed CRUNet-MR model is well-suited for cine MRI reconstruction, effectively leveraging spatio-temporal features to reconstruct high-quality images, especially in dynamic cardiac regions. This capability highlights its potential to support higher acceleration factors, enabling faster and more patient-friendly cardiac imaging.

Authors

H. Lamb
Mariya Doneva (ORCID: https://orcid.org/0000-0003-0292-5069)
Nicola Pezzotti
Donghang Lyu (ORCID: https://orcid.org/0009-0008-1446-9930)
Matthias J. P. van Osch
Marius Staring

Institutions

Leiden University Medical Center (NL)
Philips (Germany) (DE)
Philips (France) (FR)
Eindhoven University of Technology (NL)

Publication Details

Journal: Medical Physics
Published: 2025-12-31
DOI: https://doi.org/10.1002/mp.70245
Primary Topic: Advanced MRI Techniques and Applications
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Ministerie van Economische Zaken en Klimaat

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article

Convolutional recurrent U‐net for cardiac cine MRI reconstruction via effective spatio‐temporal feature exploitation

H. Lamb, Mariya Doneva, Nicola Pezzotti, Donghang Lyu et al.

Dec 31, 2025

Medical Physics

Advanced MRI Techniques and Applications

article

Convolutional recurrent U‐net for cardiac cine MRI reconstruction via effective spatio‐temporal feature exploitation

H. Lamb, Mariya Doneva, Nicola Pezzotti, Donghang Lyu, Matthias J. P. van Osch, Marius Staring

December 31, 2025article en

Abstract

The proposed CRUNet-MR model is well-suited for cine MRI reconstruction, effectively leveraging spatio-temporal features to reconstruct high-quality images, especially in dynamic cardiac regions. This capability highlights its potential to support higher acceleration factors, enabling faster and more patient-friendly cardiac imaging.

Medical PhysicsVol. 53(1)

Leiden University Medical Center (NL), Philips (Germany) (DE), Philips (France) (FR), Eindhoven University of Technology (NL)

Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Ministerie van Economische Zaken en Klimaat

Openalex Percentile: Top 30%

Advanced MRI Techniques and Applications

DOI Open Access PDF OpenAlex

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