1.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Utrecht UniversityNL73 papers
- Eindhoven University of TechnologyNL20 papers
- Heidelberg UniversityDE20 papers
- University Hospital HeidelbergDE18 papers
- Radboud University NijmegenNL17 papers
- University of AmsterdamNL16 papers
- Leiden University Medical CenterNL13 papers
- Radboud University Medical CenterNL11 papers
- University College LondonGB9 papers
- Amsterdam University Medical CentersNL8 papers
- Delft University of TechnologyNL7 papers
- King's College LondonGB7 papers
36 papers · 1 filter
An in vivo validation dataset for dynamic volumetric MRI
Max H. C. van Riel, David G. J. Heesterbeek, Martijn Froeling +2
Dynamic volumetric MRI provides valuable information on in vivo motion and biomechanics, with applications spanning cardiac, musculoskeletal, or pulmonary imaging, amongst others.…
Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset
Jelmer van Lune, Stefano Mandija, Oscar van der Heide +5
Magnetic resonance imaging (MRI) is a cornerstone of clinical neuroimaging, yet conventional MRIs provide qualitative information heavily dependent on scanner hardware and acquisit…
Millennium Pathways for Tractography: 40 grand challenges to shape the future of tractography
Maxime Descoteaux, Kurt G. Schilling, Dogu Baran Aydogan +44
In the spirit of the historic Millennium Prize Problems that heralded a new era for mathematics, the newly formed International Society for Tractography (IST) has launched the Mill…
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Yiling Wang, Elia Lombardo, Adrian Thummerer +25
Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accel…
SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy
Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon +16
Medical imaging is essential in modern radiotherapy, supporting diagnosis, treatment planning, and monitoring. Synthetic imaging, particularly synthetic computed tomography (sCT),…
Data-driven discovery of mechanical models directly from MRI spectral data
D. G. J. Heesterbeek, M. H. C. van Riel, T. van Leeuwen +2
Finding interpretable biomechanical models can provide insight into the functionality of organs with regard to physiology and disease. However, identifying broadly applicable dynam…
A diffusion MRI model for random walks confined on cylindrical surfaces: Towards non-invasive quantification of myelin sheath radius
Erick J Canales-Rodríguez, Chantal M. W. Tax, Elda Fischi-Gomez +3
Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in…
Time-Resolved Reconstruction of Motion, Force, and Stiffness using Spectro-Dynamic MRI
Max H. C. van Riel, Tristan van Leeuwen, Cornelis A. T. van den Berg +1
Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI dat…
Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes
Niek R. F. Huttinga, Osman Akdag, Martin F. Fast +5
The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracki…
A three-dimensional MR-STAT protocol for high-resolution multi-parametric quantitative MRI
Hongyan Liu, Oscar van der Heide, Edwin Versteeg +5
Magnetic Resonance Spin Tomography in Time-Domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters…
Generalizable synthetic MRI with physics-informed convolutional networks
Luuk Jacobs, Stefano Mandija, Hongyan Liu +3
In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisitio…
Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology
Muhamed Barakovic, Marco Pizzolato, Chantal M. W. Tax +7
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows no…
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
Adrian Thummerer, Erik van der Bijl, Arthur Jr Galapon +6
Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomo…
Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis
Lotte Nijskens, Cornelis, AT van den Berg +2
Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (…
Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks
Maarten Terpstra, Matteo Maspero, Joost Verhoeff +1
Background: Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. Howe…
Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC within 3 minutes
Hongyan Liu, Oscar van der Heide, Stefano Mandija +2
MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the re…
Real-time non-rigid 3D respiratory motion estimation for MR-guided radiotherapy using MR-MOTUS
Niek R. F. Huttinga, Tom Bruijnen, Cornelis A. T. van den Berg +1
The MR-Linac is a combination of an MR-scanner and radiotherapy linear accelerator (Linac) which holds the promise to increase the precision of radiotherapy treatments with MR-guid…
Deep learning-based synthetic-CT generation in radiotherapy and PET: a review
Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino +1
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical one…
MRI-guided HIFU Methods for the Ablation of Liver and Renal Cancers
Baudouin Denis de Senneville, Chrit Moonen, Mario Ries
MRI-guided High Intensity Focused Ultrasound (MRI-HIFU) is a promising method for the non-invasive ablation of pathological tissue in many organs, including mobile organs such as l…
MRI-Guided High Intensity Focused Ultrasound of Liver and Kidney
Baudouin Denis de Senneville, Mario Ries, Wilbert Bartels +1
High Intensity Focused Ultrasound (HIFU) can be used to achieve a local temperature increase deep inside the human body in a non-invasive way. MRI guidance of the procedure allows…
Technical feasibility of Magnetic Resonance Fingerprinting on a 1.5T MRI-Linac
T. Bruijnen, O. van der Heide, M. P. W. Intven +4
Hybrid MRI-linac (MRL) systems enable daily multiparametric quantitative MRI to assess tumor response to radiotherapy. Magnetic Resonance Fingerprinting (MRF) may provide time effi…
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy
M. Virgolin, Z. Wang, B. V. Balgobind +11
To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent…
CBCT-to-CT synthesis with a single neural network for head-and-neck, lung and breast cancer adaptive radiotherapy
Matteo Maspero, Mark HF Savenije, Tristan CF van Heijst +4
Purpose: CBCT-based adaptive radiotherapy requires daily images for accurate dose calculations. This study investigates the feasibility of applying a single convolutional network t…
Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data
Fenghua Guo, Alexander Leemans, Max A. Viergever +2
Spherical deconvolution is a widely used approach to quantify fiber orientation distribution from diffusion MRI data. The damped Richardson-Lucy (dRL) is developed to perform robus…