Speaker
Description
Magnetic resonance imaging (MRI) has become a standard diagnostic tool in medical assessments. However, long acquisition times remain one of its main drawbacks. This makes dynamic imaging, as needed in cineMRI, particularly challenging: motion artefacts and limited data acquisition per time point lead to an inherently ill-posed reconstruction problem. Typical solution strategies include breath-holding by the patient to limit induced motion or manual binning of frames according to an external signal. We propose a method that incorporates motion signal information directly in the image discretisation. Inspired by the deep-image prior, invertible residual networks, Lipschitz-constrained networks via Householder reflections, and previous work on motion disentanglement, we design a multi-component neural network-based architecture for image reconstruction. The motion signals, in this case representative of the breathing and cardiac cycle, are used as conditioning information for the network. This framework not only allows for retrospective modification and suppression of motion but also enables the reconstruction of incomplete motion information, such as when no breathing belt is used. In our presentation, we provide a mathematically sound formulation of the reconstruction problem within a statistical learning framework, together with general features of the proposed network architecture. These are complemented by numerical test cases highlighting capabilities such as cine reconstruction, retrospective motion suppression, feature tracking, displacement extraction, and resolution independence.