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.

Attention to Head Locations for Crowd Counting

Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications. In this paper, we propose a novel method using an attention model to exploit head locations which are the most important cue for crowd counting. The attention model estimates a probability map in which high probabilities indicate locations where heads are likely to be present. The estimated probability map is used to suppress non-head regions in feature maps from several multi-scale feature extraction branches of a convolution neural network for crowd density estimation, which makes our method robust to complex backgrounds, scale variations and non-uniform distributions. In addition, we introduce a relative deviation loss to compensate a commonly used training loss, Euclidean distance, to improve the accuracy of sparse crowd density estimation. Experiments on Shanghai-Tech, UCF_CC_50 and World-Expo'10 data sets demonstrate the effectiveness of our method.