Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement
Quick Take
The PDMR framework enables ultra-fast, motion-aware prospective 3D MRI reconstruction by leveraging latent-space motion tracking, achieving high fidelity and temporal consistency in clinical scenarios. It outperforms existing retrospective and online methods, demonstrating significant advancements in MRI-guided radiotherapy applications.
Key Points
- PDMR employs latent-space motion tracking for efficient MRI reconstruction.
- Achieves high-fidelity results in immediate and delayed scenarios.
- Outperforms state-of-the-art retrospective and online reconstruction methods.
- Utilizes a tri-plane representation for geometry-aware motion encoding.
- Demonstrates potential for ultra-fast MRI applications in clinical settings.
Article Content
From source RSS / original summaryarXiv:2606. 04249v1 Announce Type: new Abstract: Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fast motion estimation from currently acquired measurements. However, prospective reconstruction remains challenging due to ultra-sparse sampling and stringent latency requirements. In this work, we propose PDMR, a Prospective Dynamic 3D MRI Reconstruction framework with latent-space motion tracking.
Our core idea is to learn an efficient and generalizable latent manifold of motion fields offline, enabling rapid online adaptation for prospective reconstruction. Specifically, we parameterize the deformation vector fields (DVFs) on a low-dimensional manifold, effectively reducing the search space for fast online adaptation, and employ a tri-plane representation to achieve geometry-aware and memory-efficient encoding of 3D motion.
Experiments on both XCAT digital phantoms and in-house abdominal MRI datasets demonstrate that PDMR achieves high-fidelity and temporally consistent reconstruction across multiple prospective scenarios (Immediate and After-2min), outperforming state-of-the-art retrospective and online methods. Our results suggest a promising pathway toward ultra-fast, motion-aware prospective MRI reconstruction in clinical practice.
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