7 papers
Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation
Sina Wendrich, Lukas Förner, Zoe Reinke +5
Longitudinal multiparametric MRI is central to follow-up imaging in oncology, yet real-world clinical data are characterised by missing sequences, heterogeneous acquisition protoco…
Inverse Bayesian Inference for Extracting Lesion Dynamics from Longitudinal Spectral CT
Lukas Förner, Melina Wördehoff, Julian Steffens +6
Longitudinal medical imaging captures temporal evolution of lesions, yet extracting the underlying dynamical parameters governing this evolution remains challenging. We propose an…
Bridging MRI and PET physiology: Untangling complementarity through orthogonal representations
Sonja Adomeit, Kartikay Tehlan, Lukas Förner +7
Multimodal imaging analysis often relies on joint latent representations, yet these approaches rarely define what information is shared versus modality-specific. Clarifying this di…
Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis
Kartikay Tehlan, Lukas Förner, Sina Wendrich +5
We propose a geometric framework for longitudinal multi-parametric MRI analysis based on patient-specific energy modelling in sequence space. Rather than operating on images with s…
Physiological neural representation for personalised tracer kinetic parameter estimation from dynamic PET
Kartikay Tehlan, Thomas Wendler
Dynamic positron emission tomography (PET) with [F]FDG enables non-invasive quantification of glucose metabolism through kinetic analysis, often modelled by the two-tissue c…
Anatomy-constrained modelling of image-derived input functions in dynamic PET using multi-organ segmentation
Valentin Langer, Kartikay Tehlan, Thomas Wendler
Accurate kinetic analysis of [F]FDG distribution in dynamic positron emission tomography (PET) requires anatomically constrained modelling of image-derived input functions (…
Fine-Tuning TransMorph with Gradient Correlation for Anatomical Alignment
Lukas Förner, Kartikay Tehlan, Thomas Wendler
Unsupervised deep learning is a promising method in brain MRI registration to reduce the reliance on anatomical labels, while still achieving anatomically accurate transformations.…