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.
Abstract
Cardiac cine Magnetic Resonance Imaging (MRI) is a critical diagnostic tool that provides dynamic insights for radiologists. To accelerate acquisition, under-sampled k-space data is often used, requiring reconstruction methods that combine coil sensitivity encoding with prior information to recover missing data. Deep learning approaches have gained more attention for leveraging data-adaptive priors. While supervised learning approaches are a common choice, they depend on fully sampled reference data, which is not always available. Unsupervised methods eliminate the need for fully sampled reference data, which can be advantageous in cardiac cine MRI reconstruction. Among them, implicit neural representations (INRs) have shown great potential due to their simple architecture and good quality reconstructions. In this work, we propose an image-domain dual-branch INR framework, termed I-FP-INR, which extends the original INR design by introducing an additional feature-processing branch. This design aims to extract complementary feature embeddings to enhance the overall representation, thereby benefiting reconstruction. Extensive evaluations on both public datasets and in-house data show consistent improvements over baseline methods in reconstruction quality, with strong robustness across varied scenarios.
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.
Weipeng Chen, Yan-Ran Li, Raymond H. Chan et al.· Journal of Mathematical Imag...· 0 citations
This work proposes a multi-contrast self-supervised multi-contrast learned partitioning method that jointly trains on multiple under-sampled contrasts without requiring fully sampled k-space data as a reference, and improves reconstruction fidelity over single-contrast self-supervised MRI reconstructions.
Brenden T. Kadota, Charles Millard, Mark Chiew· IEEE Transactions on Biomedi...· 0 citations
Pretrained segmentation models for cardiac magnetic resonance imaging (MRI) often fail to generalize across imaging sequences due to substantial contrast variations. These variations arise from different imaging protocols, yet fundamentally, all contrasts are governed by the same underlying tissue properties, primarily captured by three components: the magnetization strength (M0), T1, and T2. Building on this insight, we introduce Reverse Imaging, a physics-driven framework for data augmentation and domain generalization in cardiac MRI. Our method infers tissue properties from observed MR images with annotation by solving an ill-posed nonlinear inverse problem, regularized by a generative prior. The prior is learned from the multiparametric saturation-recovery single shot acquisition (mSASHA) dataset for joint cardiac T1 and T2 mapping. In inference, we characterize imaging sequences as weak, moderate, or strong observations according to the physical information they provide and the degree of ill-posedness. This motivates an iterative prior-learning strategy that uses moderate T1-mapping observations to alleviate mSASHA data scarcity via pseudo tissue-property estimates. We further integrate MRI physics into posterior inference by expressing the sequence model as a likelihood term guiding the reverse diffusion process. For widely used but weak cine observations, we develop a sequence-specific ControlNet to improve efficiency and spatial consistency. Extensive experiments on eight unseen cardiac MRI sequences with markedly different contrast mechanisms show that Reverse Imaging yields plausible tissue-property estimates, supports synthesis of diverse yet physically consistent contrasts, and improves segmentation robustness under severe cross-sequence shifts.
Yidong Zhao, Yi Zhang, Tong Yang et al.· IEEE Transactions on Medical...· 0 citations
Results indicate that DMFT provides a promising framework for accurate and efficient cardiac MRIreconstruction, and structure aware prior guidance offers additional interpretability support in Healthcare 4.0 scenarios.
Jun Lyu, Bo Zhao, Selwa A. F. Al-Hazzaa et al.· IEEE journal of biomedical a...· 0 citations
COJEPA is presented, a self-supervised framework for volumetric brain MRI that combines a joint-embedding predictive architecture (JEPA) with a contrastive loss (CO), targeting two complementary properties: local predictivity and global discriminability.
