4.6k citations · 5k across the 26 of their papers we have counts for
26 papers
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction
Bo Zhou, Jo Schlemper, Neel Dey +5
While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are…
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration
Neel Dey, Jo Schlemper, Seyed Sadegh Mohseni Salehi +3
Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task. Current multi-modality registration techniq…
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction
Bo Zhou, Neel Dey, Jo Schlemper +4
Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities to aid in radiological decision-making. Given the need for lowering the time cost of multiple acquisi…
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
Chen Qin, Jinming Duan, Kerstin Hammernik +7
Purpose: To introduce a novel deep learning based approach for fast and high-quality dynamic multi-coil MR reconstruction by learning a complementary time-frequency domain network…
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
Chen Qin, Jo Schlemper, Kerstin Hammernik +3
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction. In particular, we examine the network interpolation in parameter space between a…
-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Kerstin Hammernik, Jo Schlemper, Chen Qin +3
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a -net, for accelerated…