Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sampling patterns for these k-space coefficients has received significant attention, with many CS MRI methods exploiting variable-density probability distributions. Realizing that an optimal sampling pattern may depend on the downstream task (e.g. image reconstruction, segmentation, or classification), we here propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The former is enabled through a probabilistic generative model that leverages the Gumbel-softmax relaxation to sample across trainable beliefs while maintaining differentiability. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality compared to other sampling baselines.
|Status||Gepubliceerd - mei 2020|
|Evenement||2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Virtual, Barcelona, Spanje|
Duur: 4 mei 2020 → 8 mei 2020
|Congres||2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Periode||4/05/20 → 8/05/20|