arXiv:2608.24025v1 Announce Type: new Abstract: Endpoint-only unsupervised 4D medical image interpolation synthesizes intermediate volumes from sparsely sampled sequences with only the start and end...
arXiv:2610.09397v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame a...
arXiv:2608.28712v1 Announce Type: cross Abstract: Objective: Clinical cardiac CT multiphase reconstructions generally provide acceptable image quality in end-diastole (ED) or end-systole (ES) phases,...
arXiv:2606. 26764v1 Announce Type: cross Abstract: Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions.
Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation.
arXiv:2610.09185v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electroc...
arXiv:2607. 03299v1 Announce Type: cross Abstract: Real-time cardiac cine MRI enables visualization of the beating heart during free breathing, but severe undersampling and motion make reconstruction highly challenging.
arXiv:2609.08627v1 Announce Type: cross Abstract: In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, c...
The paper introduces a new framework for learning continuous-time diffeomorphic image registration by modeling a non-autonomous ODE as a two-parameter flow map. By enforcing cocycle consistency, the method learns flow maps without time discretization or velocity integration during training, enabling efficient inference with few compositions. Experiments on nine datasets show consistent alignment improvements, including a 2.1% Dice gain on brain MRI, 12% TRE reduction on lung CT, and 2.6% Dice improvement on cardiac MRI and ultrasound.
The paper presents an anatomy‑aligned surface learning framework for reconstructing patient‑specific 4D myocardial surfaces from sparsely sampled short‑axis cine MRI. By parameterizing epicardial and endocardial surfaces on a shared circumferential‑longitudinal UV domain, the method transforms irregular 3D reconstruction into structured coordinate‑field completion, enabling explicit correspondence across subjects and cardiac phases. Experiments on three public datasets show the approach outperforms mesh‑based and implicit methods, achieving Chamfer distances around 2.6–2.9 mm and preserving ventricular function with small errors in volume and ejection fraction.
arXiv:2606. 18496v1 Announce Type: cross Abstract: Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either.
Patient-specific 4D myocardial reconstruction from cine MRI supports quantitative functional assessment, regional motion analysis, and simulation-based modeling. However, routinely acquired short-axis...