- Conference Paper
Undersampling the k-space in MRI allows saving precious acquisition time, yet results in an ill-posed inversion problem. Recently, many deep learning techniques have been developed, addressing this issue of recovering the fully sampled MR image from the undersampled data. However, these learning based schemes are susceptible to differences between the training data and the image to be reconstructed at test time. One such difference can be attributed to the bias field present in MR images, caused by field inhomogeneities and coil sensitivities. In this work, we address the sensitivity of the reconstruction problem to the bias field and propose to model it explicitly in the reconstruction, in order to decrease this sensitivity. To this end, we use an unsupervised learning based reconstruction algorithm as our basis and combine it with a N4-based bias field estimation method, in a joint optimization scheme. We use the HCP dataset as well as in-house measured images for the evaluations. We show that the proposed method improves the reconstruction quality, both visually and in terms of RMSE. Show more
Book titleMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II
Journal / seriesLecture Notes in Computer Science
Pages / Article No.
SubjectMR imaging; Machine learning
Organisational unit09579 - Konukoglu, Ender / Konukoglu, Ender
173016 - Improving Priors towards Automated Prescreening of Brain Magnetic Resonance Images (SNF)
NotesShared first authorship by the first two authors. Conference lecture held on October 5, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.
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