2 citations · 2 across the 13 of their papers we have counts for
13 papers
Implicit representations are dead. Long live explicit primitives!
Nil Stolt-Ansó, Maik Dannecker, Wenqi Huang +2
Continuous parameterization of medical data has emerged as a powerful paradigm for resolution-independent image representation. While Implicit Neural Representations offer high fid…
NISF++: Geometrically-grounded implicit representations of 3D+time cardiac function from 2D short- and long-axis MR views
Nil Stolt-Ansó, Maik Dannecker, Steven Jia +2
Clinical acquisition in cardiac magnetic resonance (CMR) imaging involves obtaining cross-sectional planes of the heart along the radial and longitudinal directions. Despite these…
PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +14
Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple…
Fast and Explicit: Slice-to-Volume Reconstruction via 3D Gaussian Primitives with Analytic Point Spread Function Modeling
Maik Dannecker, Steven Jia, Nil Stolt-Ansó +4
Recovering high-fidelity 3D images from sparse or degraded 2D images is a fundamental challenge in medical imaging, with broad applications ranging from 3D ultrasound reconstructio…
Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping
Busra Bulut, Maik Dannecker, Thomas Sanchez +9
T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is cha…
CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain
Maik Dannecker, Vasiliki Sideri-Lampretsa, Sophie Starck +5
Magnetic resonance imaging of fetal and neonatal brains reveals rapid neurodevelopment marked by substantial anatomical changes unfolding within days. Studying this critical stage…
Evaluation of Deformable Image Registration under Alignment-Regularity Trade-off
Vasiliki Sideri-Lampretsa, Daniel Rueckert, Huaqi Qiu
Evaluating deformable image registration (DIR) is challenging due to the inherent trade-off between achieving high alignment accuracy and maintaining deformation regularity. Howeve…
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Fabian Drexel, Vasiliki Sideri-Lampretsa, Henriette Bast +6
Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. H…
Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data?
Simon Langer, Jessica Ritter, Rickmer Braren +2
Modern deep learning-based clinical imaging workflows rely on accurate labels of the examined anatomical region. Knowing the anatomical region is required to select applicable down…
From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review
Anna Reithmeir, Veronika Spieker, Vasiliki Sideri-Lampretsa +3
Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration i…
Interpretable deformable image registration: A geometric deep learning perspective
Vasiliki Sideri-Lampretsa, Nil Stolt-Ansó, Huaqi Qiu +4
Deformable image registration poses a challenging problem where, unlike most deep learning tasks, a complex relationship between multiple coordinate systems has to be considered. A…
Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide
Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe +1
Fine-tuning large language models (LLMs) with limited data poses a practical challenge in low-resource languages, specialized domains, and constrained deployment settings. While pr…
Intensity-based 3D motion correction for cardiac MR images
Nil Stolt-Ansó, Vasiliki Sideri-Lampretsa, Maik Dannecker +1
Cardiac magnetic resonance (CMR) image acquisition requires subjects to hold their breath while 2D cine images are acquired. This process assumes that the heart remains in the same…