bibtext: @InProceedings{ Hua_Noise_MICCAI2024,
                   author = { Huang, Shoujin and Luo, Guanxiong and Wang, Xi and Chen, Ziran and Wang, Yuwan and Yang, Huaishui and Heng, Pheng-Ann and Zhang, Lingyan and Lyu, Mengye },
                   title = { { Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 for Nila is available at \url{https://github.com/Solor-pikachu/Nila}.
title: Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI
authors: Huang, Shoujin Luo, Guanxiong Wang, Xi Chen, Ziran Wang, Yuwan Yang, Huaishui Heng, Pheng-Ann Zhang, Lingyan Lyu, Mengye