Jul 2026· Journal of Mathematical Imaging and Vision· Vol 68· 0 citations· 41 references
Computer Science
TL;DR
A model-driven bilevel optimization framework that couples SENSE-based image reconstruction with SPIRiT-based k-space calibration through shared CSMs, and introduces a deep-prior-guided regularization strategy that preserves the structure of classical linear regularizers while adaptively learning spatially varying regularization weights from denoised intermediate reconstructions.
I2SB-Inversion is proposed, a multi-contrast guided reconstruction framework based on the Schrödinger Bridge that achieves a a high acceleration factor of R=11.38 and consistently outperforms existing methods in both quantitative and qualitative evaluations.
Yue Wang, Yuanbiao Yang, Zhuo-xu Cui et al.· IEEE Transactions on Medical...· 0 citations
This work proposes UMPIRE-Net (Unrolled Magnitude-Phase In REgularization Network), a PD-DL method that introduces separate learned regularizers for magnitude and phase components, together with a novel data-fidelity formulation that enforces measurements consistency.
Mahdi Saberi, Toygan Kilic, Mehmet Akçakaya· 2 citations
This work proposes an image-domain dual-branch INR framework, termed I-FP-INR, which extends the original INR design by introducing an additional feature-processing branch, which aims to extract complementary feature embeddings to enhance the overall representation, thereby benefiting reconstruction.
Donghang Lyu, Marius Staring, Yiming Dong et al.· 0 citations
This work designs an implicit pixel-wise learnable step size to adapt to the spatial gradient heterogeneity of CT images and develops a cross-prompt guiding mechanism to enable inter-domain prompt interaction, which facilitates efficient prompt generation and enhances the convergence stability of the model.
Wenchao Du, Qiao Mu, Huanhuan Cui et al.· IEEE Transactions on Medical...· 0 citations
While multishot interleaved echo‐planar imaging (iEPI) enables higher resolution diffusion kurtosis imaging (DKI) compared to single‐shot EPI, its clinical application is hindered by the lengthy acquisition time. This study proposes a novel model‐based reconstruction approach to accelerate iEPI DKI acquisition.
Jian Lyu, Li Guo, Wen Zhong et al.· Magnetic Resonance in Medici...· 0 citations
Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensive iterative refinement, limiting their practical deployment. Consistency models (CMs) provide a compelling alternative, aiming to map out the diffusion trajectory in a single pass, enabling faster generation. In this work, we propose CM-RED, a novel MRI reconstruction method that integrates a pretrained CM into the regularization by denoising (RED) scheme. Our method builds on accelerated proximal gradient RED (RED-APG), and further incorporates controlled noise injection during the update steps to enhance generative diversity and accelerate convergence. Extensive experiments on the fastMRI knee and brain datasets demonstrate that CM-RED achieves high-quality reconstructions across multiple anatomies, contrast weights, acceleration factors, and undersampling patterns, using only 4 network function evaluations (NFEs). The proposed method consistently outperforms existing DM- and CM-based approaches in both quantitative metrics and visual fidelity, and exhibits strong robustness to hyperparameter variations, highlighting CM-RED as an efficient and effective generative framework for accelerated MRI reconstruction. The source code and pretrained models are publicly available at https://github.com/MerveGulle/CM-RED.
Merve Gülle, Junno Yun, Y. Alçalar et al.· 0 citations