Deep learning for retrospective motion correction in MRI: a comprehensive review
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since
the MR signal is acquired in frequency space, any motion of the imaged object leads to …
the MR signal is acquired in frequency space, any motion of the imaged object leads to …
Deep learning for accelerated and robust MRI reconstruction
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
Enhancing Dynamic CT Image Reconstruction with Neural Fields Through Explicit Motion Regularizers
P Arratia, M Ehrhardt, L Kreusser - arXiv preprint arXiv:2406.01299, 2024 - arxiv.org
Image reconstruction for dynamic inverse problems with highly undersampled data poses a
major challenge: not accounting for the dynamics of the process leads to a non-realistic …
major challenge: not accounting for the dynamics of the process leads to a non-realistic …
Machine Learning for Quantitative MR Image Reconstruction
In the last years, the design of image reconstruction methods in the field of quantitative
Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when …
Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when …