Abstract
Magnetic resonance imaging (MRI) is essential for the detection and diagnosis of diseases. MRI scanners sequentially collect measurements in the frequency domain (or k-space), from which an image is reconstructed. A central challenge in MRI is its time-consuming sequential acquisition process as the scanner needs to densely sample the underlying k-space for accurate reconstruction. In order to improve patients’ comfort & safety and alleviate motion artifacts, reconstructing high-quality images from limited measurements is desirable. There are two core parts in the accelerated MRI pipeline: a sampling pattern deployed to collect the undersampled data in k-space and a corresponding reconstruction method (reconstructor) that also enables recovering any missing information. In this work, we use machine-learned models to predict the undersampling pattern and perform image reconstruction in a single pass and in an object-adaptive manner.
References
Z. Huang and S. Ravishankar, "Single-Pass Object-Adaptive Data Undersampling and Reconstruction for MRI," in IEEE Transactions on Computational Imaging, vol. 8, pp. 333-345, 2022, doi: 10.1109/TCI.2022.3167454. (Code)
S. Gautam, A. Li and S. Ravishankar, "Patient-Adaptive and Learned Mri Data Undersampling Using Neighborhood Clustering," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 2081-2085, doi: 10.1109/ICASSP48485.2024.10446528.