LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior

Diffusion model, as a powerful generative model, has found a wide range of applications including MRI reconstruction. However, most existing diffusion model-based MRI reconstruction methods operate directly in pixel space, which makes their optimization and inference computationally expensive. Latent diffusion models were introduced to address this problem in natural image processing, but directly applying them to MRI reconstruction still faces many challenges, including the lack of control over the generated results, the adaptability of Variational AutoEncoder (VAE) to MRI, and the exploration of applicable data consistency in latent space. To address these challenges, a Latent Diffusion Prior based undersampled MRI reconstruction (LDPM) method is proposed. A sketcher module is utilized to provide appropriate control and balance the quality and fidelity of the reconstructed MR images. A VAE adapted for MRI tasks (MR-VAE) is explored, which can serve as the backbone for future MR-related tasks. Furthermore, a variation of the DDIM sampler, called the Dual-Stage Sampler, is proposed to achieve high-fidelity reconstruction in the latent space. The proposed method achieves competitive results on fastMRI datasets, and the effectiveness of each module is demonstrated in ablation experiments.

Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative Priors

Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to the complex signal interactions between and within the excited slices. This study presents a robust SMS MRI reconstruction method using deep generative priors. Starting from Gaussian noise, we leverage denoising diffusion probabilistic models (DDPM) to gradually recover the individual slices through reverse diffusion iterations while imposing data consistency from the measured k-space under readout concatenation framework. The posterior sampling procedure is designed such that the DDPM training can be performed on single-slice images without special adjustments for SMS tasks. Additionally, our method integrates a low-frequency enhancement (LFE) module to address a practical issue that SMS-accelerated fast spin echo (FSE) and echo-planar imaging (EPI) sequences cannot easily embed autocalibration signals. Extensive experiments demonstrate that our approach consistently outperforms existing methods and generalizes well to unseen datasets. The code is available at https://github.com/Solor-pikachu/ROGER after the review process.

Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI

In general, diffusion model-based MRI reconstruction methods incrementally remove artificially added noise while imposing data consistency to reconstruct the underlying images. However, real-world MRI acquisitions already contain inherent noise due to thermal fluctuations. This phenomenon is particularly notable when using ultra-fast, high-resolution imaging sequences for advanced research, or using low-field systems favored by low- and middle-income countries. These common scenarios can lead to sub-optimal performance or complete failure of existing diffusion model-based reconstruction techniques. Specifically, as the artificially added noise is gradually removed, the inherent MRI noise becomes increasingly pronounced, making the actual noise level inconsistent with the predefined denoising schedule and consequently inaccurate image reconstruction. To tackle this problem, we propose a posterior sampling strategy with a novel NoIse Level Adaptive Data Consistency (Nila-DC) operation. Extensive experiments are conducted on two public datasets and an in-house clinical dataset with field strength ranging from 0.3T to 3T, showing that our method surpasses the state-of-the-art MRI reconstruction methods, and is highly robust against various noise levels. The code will be released after review.