A spatial masked-set framework for sparse multi-shell diffusion MRI signal synthesis that achieves lower signal NMSE than both analytical q-space models and a state-of-the-art continuous dMRI signal synthesis model designed for arbitrary input and output q-space sampling.
Abstract
Dense multi-shell diffusion MRI provides rich q-space information but requires long acquisition times. We propose a spatial masked-set framework for sparse multi-shell diffusion MRI signal synthesis. The model treats observed measurements as an unordered set, uses a local $3 \times 3 \times 3$ neighborhood for spatial context, and predicts radial-order-6 SHORE coefficients for the center voxel. The coefficients can then be decoded analytically to synthesize signals at arbitrary q-space locations. Training combines shell-wise gradient dropping, dense signal supervision, and rotation-consistent SHORE targets so that sparse input signals remain aligned with their coefficient supervision under augmentation. We evaluate on held-out HCP100 white-matter voxels by retaining limited subsets of measured diffusion-weighted signals from the reference acquisition. The proposed method achieves lower signal NMSE than both analytical q-space models and a state-of-the-art continuous dMRI signal synthesis model designed for arbitrary input and output q-space sampling. In the $b=1000$ setting with 10 input gradients, it achieves $2.70\%$ NMSE, a $22.4\%$ relative reduction over this continuous model. Fractional anisotropy on reconstructed $b=1000$ signals provides a complementary tensor-derived endpoint, with analytical models remaining competitive for FA despite higher dense-signal NMSE across the evaluated q-space. The implementation is available on \href{https://github.com/xmindflow/SHOREPred}{https://github.com/xmindflow/SHOREPred}.
Diffusion magnetic resonance imaging (dMRI) enables noninvasive mapping of tissue microstructure by probing water molecule diffusivity. While advanced multi-shell diffusion models offer improved sensitivity to cellular properties, their requirement for densely sampled q-space data leads to prohibitively long acquisition times. Current deep learning approaches for parameter estimation face three key limitations: (1) dependency on fixed acquisition protocols, (2) model-specific assumptions that constrain applicability, and (3) reliance on supervised learning paradigms that demand large labeled datasets and exhibit poor generalization to out-of-distribution cases. To address these challenges, we propose MINeR, a novel unsupervised subject-specific framework for reconstructing dense q-space data from highly undersampled acquisitions. Our method leverages direction-modulated implicit neural representation to flexibly sample diffusion signals across q-space, supporting the estimation of parameters for diverse diffusion models. Comprehensive evaluations demonstrate that MINeR maintains high fidelity in microstructural parameter estimation, particularly for advanced multi-shell diffusion models. The framework shows remarkable generalization capability, as evidenced by its robust performance on tumor data. Notably, MINeR effectively reconstructs high-quality diffusion signals by interpolating from 6 directions, significantly reducing acquisition time, while maintaining robust parameter estimation. This work presents a practical approach for enabling microstructural modeling from sparsely sampled q-space data, thereby improving the clinical applicability of diffusion MRI. The code is available at: https://github.com/AMRI-Lab/MINeR.
Tianping Zeng, Jie Feng, Tong Sun et al.· Medical Image Analysis· 0 citations
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.
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Full-image objectives in brain magnetic resonance imaging (MRI) super-resolution (SR) can underweight tissue-transition regions affected by the partial-volume effect (PVE), as these regions occupy only a small fraction of the image. Binary boundaries also do not capture the continuous mixture of cerebrospinal fluid, gray matter, and white matter within a voxel. We propose Anatomy-Guided Gaussian-Parameter Warping with PVE-Balanced Reconstruction (AGW-PBR), which combines a low-resolution (LR)-only reconstruction backbone with a training-time objective that emphasizes tissue transitions. The backbone integrates LR-derived Sobel guidance, soft latent-basis assignment, and bounded grid-anchored residual warping. Fixed, quality-controlled tissue fractions derived from registered T1/T2/PD IXI images are converted into tissue-mixture entropy, which defines mean-normalized reconstruction weights within validated PVE support. These sidecars are used only during training, and inference requires only the LR image. AGW-PBR is evaluated on T2-weighted IXI images at 2x, 4x, and 6x using three seeds and subject-level paired analyses. At 4x, test-only SynthSeg masks independently assess reconstruction in tissue-interface and non-interface regions. Targeted ablations examine valid-support supervision, spatially aligned entropy weighting, and soft latent assignment. The AGW-backbone is also trained from scratch on fastMRI at 4x without PVE supervision. AGW-PBR improves full-image reconstruction across the tested IXI scales and regional fidelity at 4x, while the PVE-free backbone retains strong performance on fastMRI. These findings support tissue-mixture entropy weighting for partial-volume-aware brain MRI SR.
Xiaoli Tong, Wen-Yun Yang, Zi-Heng Zhang et al.· 0 citations
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are fundamental imaging modalities that provide complementary information for clinical assessment. However, CT acquisition may not always be preferred or available due to additional cost, workflow burden, and exposure to ionizing radiation. Therefore, reliably synthesizing a corresponding CT image from an available MRI scan is an important medical imaging problem. In this work, we present a conditional latent diffusion framework for MRI-to-CT synthesis. To improve the reliability of stochastic generation, the proposed framework incorporates a modular output steering mechanism that favors candidates better matched to target-domain characteristics. In this way, the diversity of diffusion-based synthesis is preserved while output consistency and realism are improved. Experimental results indicate that the proposed approach yields improved quantitative and qualitative performance over a baseline latent diffusion model. The proposed method achieved 26.49±2.19 dB PSNR and 87.89±2.59% SSIM, outperforming the baseline latent diffusion model.
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The proposed K-space Gaussian Representation (KGR), the first explicit continuous representation formulated directly in the native k-space domain, suggests that explicit continuous parameterization of native k-space provides a principled framework for integrating continuous signal modeling with structured low-rank reconstruction.
Diffusion Magnetic Resonance Imaging (dMRI) is a powerful tool for probing brain microstructure, but clinical acquisitions are often limited by low out-of-plane resolution, resulting in degraded structural information and reduced utility for advanced analysis. We introduce SIINR (Structurally Informed Implicit Neural Representations), a general framework for super-resoltion of clinical dMRI datasets while quantifying uncertainty in the reconstructed outputs. SIINR utilizes a supervised 3D U-net as a prior and combines it with a self-supervised implicit neural representation (INR) that fuses the high-resolution prior and the original low-resolution data. The INR enables joint modeling across spatial and angular domains, enforces data consistency, and provides analytic approximate posterior distributions for downstream uncertainty quantification. We validate the framework on a diverse set of open-access dMRI datasets, demonstrating that SIINR outperforms standard interpolation methods in both quantitative error metrics and qualitative anatomical fidelity. Experiments on clinical cases, including subjects with multiple sclerosis and brain lesions, illustrate the framework its ability to propagate intensity changes and flag uncertain regions in challenging scenarios. SIINR is flexible, modular, and can be adapted to different upsampling ratios and downstream tasks, providing a principled approach for enhancing clinical dMRI and supporting robust interpretation of derived neuroimaging metrics.
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