Aniket Pramanik1 and Mathews Jacob1
1Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States
1Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States
We propose a joint reconstruction-segmentation framework for calibration-free Parallel MRI. It consists of a novel image domain deep structured low-rank network for calibration-less PMRI cascaded with a segmentation network to reduce segmentation errors due to undersampling artifacts.
Proposed I-DSLR-SEG-E2E network architecture. A K-iteration I-DSLR network is cascaded with a CNN for segmentation. It is trained end-to-end.
Comparison of reconstruction and segmentation quality of various methods on 6-fold undersampled k-space measurements. Reconstruction SNR in dB along with dice coefficients for CSF, GM and WM are reported for the particular slice. The
methods in red box typically cascade separately trained tasks and the blue one is the proposed end-to-end training approach.