Acar, MertÇukur, TolgaÖksüz, İlkay2022-01-272022-01-272021-09-25978-3-030-88551-9978-3-030-88551-90302-9743http://hdl.handle.net/11693/76819Conference Name: International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021Date of Conference: 1 October 2021Deep learning techniques have recently been adopted for accelerating dynamic MRI acquisitions. Yet, common frameworks for model training rely on availability of large sets of fully-sampled MRI data to construct a ground-truth for the network output. This heavy reliance is undesirable as it is challenging to collect such large datasets in many applications, and even impossible for high spatiotemporal-resolution protocols. In this paper, we introduce self-supervised training to deep neural architectures for dynamic reconstruction of cardiac MRI. We hypothesize that, in the absence of ground-truth data, elevating complexity in self-supervised models can instead constrain model performance due to the deficiencies in training data. To test this working hypothesis, we adopt self-supervised learning on recent state-of-the-art deep models for dynamic MRI, with varying degrees of model complexity. Comparison of supervised and self-supervised variants of deep reconstruction models reveals that compact models have a remarkable advantage in reliability against performance loss in self-supervised settings.EnglishCardiac MRIDynamic reconstructionSelf-supervised learningConvolutional neural networksConference Paper10.1007/978-3-030-88552-6_4978-3-030-88552-61611-3349