arXiv:2604.06280v1 Announce Type: cross
Abstract: Purpose: To develop and evaluate DosimeTron, an agentic AI system for automated patient-specific MC internal radiation dosimetry in PET/CT examinations.
Materials and Methods: In this retrospective s…
arXiv:2604.06482v1 Announce Type: cross
Abstract: Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiotherapy. Representing patient anatomy and associated…
arXiv:2604.06671v1 Announce Type: cross
Abstract: Introduction: Mechanical thrombectomy can cause vessel deformation and procedure-related injury. Benchtop models are widely used for device testing, but time-resolved, full-field 3D vessel-motion measu…
arXiv:2503.14094v2 Announce Type: replace-cross
Abstract: Accurate speed-of-sound (SoS) estimation is crucial for ultrasound image formation, yet conventional systems often rely on an assumed value for imaging. We propose to leverage conventional imag…
arXiv:2604.03321v1 Announce Type: cross
Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The succ…
arXiv:2507.16962v3 Announce Type: replace-cross
Abstract: Magnetic resonance imaging (MRI) has greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different scanners, acquisition protocols, or imagin…
arXiv:2603.04955v2 Announce Type: replace
Abstract: In this work, we investigate uncertainty-aware neural network models for blood glucose prediction and adverse glycemic event identification in Type 1 diabetes. We consider three families of sequence …
arXiv:2509.01752v2 Announce Type: replace
Abstract: Limited-angle computed tomography (LACT) offers improved temporal resolution and reduced radiation dose for cardiac imaging, but suffers from severe artifacts due to truncated projections. To address…