Yan Dai

Open access Aug 2026

S2V-DREME: a time-resolved slice-to-volume MR image reconstruction framework with dynamic reconstruction and motion estimation

Objective. Existing volumetric magnetic resonance imaging (MRI) techniques are constrained by the trade-off between acquisition time and image quality, limiting accuracy in motion-impacted sites such as the liver. To enable fast, better-quality volumetric imaging with sufficient spatiotemporal resolution, we developed a time-resolved volumetric MRI technique that recovers 3D volumes from acquired 2D MR slices for real-time 3D anatomy and motion tracking. Approach. 2D MR slices dynamically acquired in time and space were mapped to time-resolved 3D MRIs using a one-shot slice-to-volume framework, S2V-DREME. The model jointly estimates a reference 3D MRI and time-resolved deformation vector fields (DVFs) that warp the reference volume into dynamic 3D MRIs. The reference volume is represented by a spatial implicit neural representation (INR), while the DVFs are derived via low-rank motion modeling. Motion basis components (MBCs) are generated by a spline-enhanced INR (SINR)-based motion generator, with coefficients inferred by a feature-wise linear modulation-based motion encoder. A progressive optimization strategy sequentially initializes the spatial INR and MBCs before joint optimization. The loss function integrates slice data fidelity, total variation regularization, MBC normalization, and DVF smoothness constraints. Main results. S2V-DREME generates time-resolved volumetric MRIs from 2D MR slice inputs. It was evaluated on digital phantom extended cardiac torso (XCAT), physical phantom, and human studies. In XCAT, it accurately captured regular and irregular motion during dynamic reconstruction (training stage, Dice similarity coefficient (DSC)/COME: 0.92 ± 0.03/0.98 ± 0.43 mm) and real-time motion estimation (testing stage, DSC/COME: 0.91 ± 0.02/0.99 ± 0.73 mm). Physical phantom experiments achieved a mean COME of 1.16 mm, and human studies demonstrated the feasibility of time-resolved 3D reconstruction from orthogonal-view and single-view slice acquisitions. Significance. By combining a novel step-and-shoot acquisition protocol with motion-compensated one-shot learning, S2V-DREME enables accurate time-resolved volumetric MRI reconstruction and motion tracking from cineslices, with strong potential for rapid volumetric imaging and real-time MR-guided adaptive radiotherapy.

Xiaoxue Qian, H. Shao, Jie Deng et al. · Physics in Medicine and Biol... · 0 citations

Aug 2026

Physics-Assisted Deep Learning Denoising for Stabilized IMPULSED dMRI Microenvironment Parameter Fitting

Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting. A dataset of paired noise-free and Rician-noise-corrupted dMRI signals was generated using the IMPULSED-dMRI signal model. Three denoising architectures were evaluated: Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Denoised signals were then fitted to estimate cell diameter $d$, intracellular volume fraction $V_{\mathrm{in}}$, and extracellular apparent diffusion coefficient $D_\mathrm{ex}$. DL-based processing substantially improved dMRI signal denoising. The MLP and LSTM achieved similar performance, with the LSTM slightly better overall, and both outperformed the CNN. In the subsequent model fitting step, the LSTM produced modest reductions in parameter MAE. The dominant benefit was fitting stabilization, with the overall fitting failure rate reduced from 57.6\% to 17.7\%. The proposed framework improves dMRI signal quality and stabilizes subsequent IMPULSED-based microenvironmental parameter fitting.

Wen Li, Yan Dai, A. P. Rodriguez et al. · arXiv.org · 0 citations