6 citations · 9 across the 8 of their papers we have counts for
8 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…
Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction
Wenqi Huang, Veronika Spieker, Nil Stolt-Ansó +6
Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific recons…
Reconstruction-free segmentation from undersampled k-space using transformers
Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan +3
Motivation: High acceleration factors place a limit on MRI image reconstruction. This limit is extended to segmentation models when treating these as subsequent independent process…
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…
Direct Cardiac Segmentation from Undersampled K-space Using Transformers
Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan +3
The prevailing deep learning-based methods of predicting cardiac segmentation involve reconstructed magnetic resonance (MR) images. The heavy dependency of segmentation approaches…
NISF: Neural Implicit Segmentation Functions
Nil Stolt-Ansó, Julian McGinnis, Jiazhen Pan +2
Segmentation of anatomical shapes from medical images has taken an important role in the automation of clinical measurements. While typical deep-learning segmentation approaches ar…
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
Jiazhen Pan, Suprosanna Shit, Özgün Turgut +6
In dynamic Magnetic Resonance Imaging (MRI), k-space is typically undersampled due to limited scan time, resulting in aliasing artifacts in the image domain. Hence, dynamic MR reco…