333 citations · 1.9k across the 64 of their papers we have counts for
12 papers · 1 filter
A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern
Yunjie Chen, Rianne A. Weber, Olaf M. Neve +6
Objectives: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. Materials an…
Knowledge Distillation for Continual Learning of Biomedical Neural Fields
Wouter Visser, Jelmer M. Wolterink
Neural fields are increasingly used as a light-weight, continuous, and differentiable signal representation in (bio)medical imaging. However, unlike discrete signal representations…
Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Puru Vaish, Felix Meister, Tobias Heimann +2
Many recent approaches in representation learning implicitly assume that uncorrelated views of a data point are sufficient to learn meaningful representations for various downstrea…
GReAT: leveraging geometric artery data to improve wall shear stress assessment
Julian Suk, Jolanda J. Wentzel, Patryk Rygiel +3
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed…
Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations
Caner Özer, Patryk Rygiel, Bram de Wilde +2
Artifacts pose a significant challenge in medical imaging, impacting diagnostic accuracy and downstream analysis. While image-based approaches for detecting artifacts can be effect…
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models
Patryk Rygiel, Julian Suk, Christoph Brune +2
Abdominal aortic aneurysms (AAAs) are pathologic dilatations of the abdominal aorta posing a high fatality risk upon rupture. Studying AAA progression and rupture risk often involv…
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Dieuwertje Alblas, Patryk Rygiel, Julian Suk +5
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Current clinical guidelines recomme…
Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation
Puru Vaish, Felix Meister, Tobias Heimann +2
Medical image segmentation models are often trained on curated datasets, leading to performance degradation when deployed in real-world clinical settings due to mismatches between…
Steerable Anatomical Shape Synthesis with Implicit Neural Representations
Bram de Wilde, Max T. Rietberg, Guillaume Lajoinie +1
Generative modeling of anatomical structures plays a crucial role in virtual imaging trials, which allow researchers to perform studies without the costs and constraints inherent t…
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
Patryk Rygiel, Julian Suk, Kak Khee Yeung +2
Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters ca…
Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging
Rienk Zorgdrager, Nathan Blanken, Jelmer M. Wolterink +2
Resolving arterial flows is essential for understanding cardiovascular pathologies, improving diagnosis, and monitoring patient condition. Ultrasound contrast imaging uses microbub…
Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models
Guido Nannini, Julian Suk, Patryk Rygiel +7
Coronary artery disease, caused by the narrowing of coronary vessels due to atherosclerosis, is the leading cause of death worldwide. The diagnostic gold standard, fractional flow…