Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)
arXiv:2604.06482v2 Announce Type: replace-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 motion fields as 3D Gaussians, we developed a spatiotemporal Gaussian representation-based framework (DREME-GSMR), which enables time-resolved dynamic MRI reconstruction from a pre-treatment 3D MR scan without any prior anatomical/motion model. DREME-GSMR represents a reference MRI volume and a corresponding low-rank motion model (as moti
The continuous advancements in AI, particularly in generative models and spatiotemporal representations, enable the development of more sophisticated medical imaging reconstruction techniques.
This development represents a significant step towards real-time, high-fidelity medical imaging, crucial for precise, adaptive cancer treatments like radiotherapy.
The ability to reconstruct 3D MRI volumes rapidly and accurately without prior anatomical models will enhance diagnostic precision and treatment efficacy, reducing patient exposure and time.
- · Medical imaging companies
- · Oncology and radiology clinics
- · AI healthcare developers
- · Patients undergoing radiotherapy
- · Legacy medical imaging software
Improved radiotherapy outcomes due to enhanced motion tracking and adaptive planning.
Reduced healthcare costs through more efficient and accurate diagnostic and treatment procedures.
Acceleration of personalized medicine by providing real-time, patient-specific anatomical and physiological data.
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Read at arXiv cs.LG