A unified high-dimensional k-space reconstruction framework is proposed to enhance diffusion-based solvers for noisy MRI inverse problems through representation lifting.

The paper proposes MindDiffuser, a two-stage framework that significantly improves image reconstruction from brain activity by combining semantic guidance from text-to-image models with structural ref…

The paper proposes a unified, contrast-agnostic framework that uses parameter-informed disentanglement and adaptive experts to robustly correct motion artifacts in MRI across various modalities and se…

This paper proposes STRMSR, a through-plane super-resolution framework for clinical cardiac MRI using HR reference views and intermediate SR results as memory.

The paper introduces a novel diffusion posterior sampling method that stabilizes and accelerates data-consistent sampling by replacing hand-tuned guidance weights with a per-noise-level, curvature-gui…

This paper systematically explores the convex polygon reconstruction problem with specified sets of features, contributing new testing algorithms and hardness results.

The paper proposes a measurement-geometry framework to quantify how well fixed measurement operators can distinguish between images generated by a prior, thereby guiding the design of more trustworthy…

This paper proposes an anatomically aware frequency-and-structure-guided vision transformer (AA-ViT) for accurate and non-invasive contrast enhanced MRI (CEMRI) synthesis using pre-contrast MRI modali…

This paper improves the foundations of neural likelihood approximation for Bayesian inverse problems by making the learning problem strictly convex and showing convergence to the true likelihood.

The paper introduces Robust Prior Update (RPU), a module that improves the faithfulness of diffusion-based inverse solvers by stabilizing the prior update step, thereby reducing measurement-conditione…

The paper introduces Dual-Spectral Flow Matching (DSFM), a novel generative framework that uses wavelet and cosine transforms to synthesize highly realistic, non-stationary fMRI time series for improv…

QuReC is a unified framework for all-in-one image restoration using a Degradation-Guided Query Reconstruction Module and a Local-Global Response Calibration Module.

The paper introduces a subgrid marching tetrahedra scheme that accurately recovers complex, intersection-free manifold meshes from tetrahedral grids, overcoming limitations of classic marching methods…

This paper investigates the use of contrastive objectives for brain decoding using functional MRI (fMRI) activity and shows that linear contrastive decoders outperform other methods.

The paper presents Conformalized Rate-Adaptive Sensing (CoRAS), a method for adaptively choosing image acquisition or compression rates while maintaining a target reconstruction error.

This paper proposes a plug-and-play gradient-step model that effectively reduces photon noise in dental cone-beam CT reconstruction by incorporating a data-driven denoiser prior.

This paper establishes recovery guarantees for low-rank Hermitian matrices from quadratic sampling matrices under the assumption of finite 4+δ moments of the entries.

This paper proposes using a Diffusion Transformer as a frozen reconstruction probe for audio deepfake detectors, achieving state-of-the-art performance on ASVspoof 5 Eval and ITW Full.

The paper introduces a novel third-order, rotation-invariant spherical bispectrum for watermarking panoramic images, enabling reliable watermark embedding and extraction under arbitrary 3D rotations.