Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement
2026-06-02 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method called PDMR to quickly and accurately reconstruct 3D MRI images while tracking motion, which is important for applications like MRI-guided radiotherapy. Their approach learns a simplified way to represent motion from past data, allowing fast updates during image capture using less data. They tested their method on simulated and real MRI scans and found it performs better than current techniques in maintaining image quality and consistency. This work could help make real-time MRI imaging faster and more reliable in clinical settings.
Prospective reconstructionMRI-guided radiotherapyDeformation vector fields (DVFs)Latent manifoldTri-plane representationMotion estimation3D MRIUltra-sparse samplingDigital phantomTemporal consistency
Authors
Lixuan Chen, Zhongnan Liu, Jesse Hamilton, James M. Balter, Jeong Joon Park, Liyue Shen
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.