TY - GEN
T1 - Fill the K-Space and Refine the Image
T2 - 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023 held in Conjunction with MICCAI 2023
AU - Xin, Bingyu
AU - Ye, Meng
AU - Axel, Leon
AU - Metaxas, Dimitris N.
N1 - Publisher Copyright: © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information. Currently, the unrolled model, an approach combining iterative MRI reconstruction steps with learnable neural network layers, stands as the best-performing method for MRI reconstruction. However, there are two main limitations to overcome: firstly, the unrolled model structure and GPU memory constraints restrict the capacity of each denoising block in the network, impeding the effective extraction of detailed features for reconstruction; secondly, the existing model lacks the flexibility to adapt to variations in the input, such as different contrasts, resolutions or views, necessitating the training of separate models for each input type, which is inefficient and may lead to insufficient reconstruction. In this paper, we propose a two-stage MRI reconstruction pipeline to address these limitations. The first stage involves filling the missing k-space data, which we approach as a physics-based reconstruction problem. We first propose a simple yet efficient baseline model, which utilizes adjacent frames/contrasts and channel attention to capture the inherent inter-frame/-contrast correlation. Then, we extend the baseline model to a prompt-based learning approach, PromptMR, for all-in-one MRI reconstruction from different views, contrasts, adjacent types, and acceleration factors. The second stage is to refine the reconstruction from the first stage, which we treat as a general video restoration problem to further fuse features from neighboring frames/contrasts in the image domain. Extensive experiments show that our proposed method significantly outperforms previous state-of-the-art accelerated MRI reconstruction methods.
AB - The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information. Currently, the unrolled model, an approach combining iterative MRI reconstruction steps with learnable neural network layers, stands as the best-performing method for MRI reconstruction. However, there are two main limitations to overcome: firstly, the unrolled model structure and GPU memory constraints restrict the capacity of each denoising block in the network, impeding the effective extraction of detailed features for reconstruction; secondly, the existing model lacks the flexibility to adapt to variations in the input, such as different contrasts, resolutions or views, necessitating the training of separate models for each input type, which is inefficient and may lead to insufficient reconstruction. In this paper, we propose a two-stage MRI reconstruction pipeline to address these limitations. The first stage involves filling the missing k-space data, which we approach as a physics-based reconstruction problem. We first propose a simple yet efficient baseline model, which utilizes adjacent frames/contrasts and channel attention to capture the inherent inter-frame/-contrast correlation. Then, we extend the baseline model to a prompt-based learning approach, PromptMR, for all-in-one MRI reconstruction from different views, contrasts, adjacent types, and acceleration factors. The second stage is to refine the reconstruction from the first stage, which we treat as a general video restoration problem to further fuse features from neighboring frames/contrasts in the image domain. Extensive experiments show that our proposed method significantly outperforms previous state-of-the-art accelerated MRI reconstruction methods.
KW - Dynamic
KW - MRI reconstruction
KW - Multi-contrast
KW - Prompt-based learning
KW - Two-stage approach
UR - https://www.scopus.com/pages/publications/85186723922
UR - https://www.scopus.com/pages/publications/85186723922#tab=citedBy
U2 - 10.1007/978-3-031-52448-6_25
DO - 10.1007/978-3-031-52448-6_25
M3 - Conference contribution
SN - 9783031524479
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 261
EP - 273
BT - Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers - 14th International Workshop, STACOM 2023, Held in Conjunction with MICCAI 2023, Revised Selected Papers
A2 - Camara, Oscar
A2 - Puyol-Antón, Esther
A2 - Suinesiaputra, Avan
A2 - Young, Alistair
A2 - Sermesant, Maxime
A2 - Tao, Qian
A2 - Wang, Chengyan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 12 October 2023 through 12 October 2023
ER -