Gated Recurrent Neural Networks for Accelerated Ventilation MRI

Abstract

Thanks to recent advancements of specific acquisition methods and post-processing, proton Magnetic Resonance Imaging became an alternative imaging modality for detecting and monitoring chronic pulmonary disorders. Currently, ventilation maps of the lung are calculated from time-resolved image series which are acquired under free breathing. Each series consists of 140 coronal 2D images containing several breathing cycles. To cover the majority of the lung, such a series is acquired at several coronal slice-positions. A reduction of the number of images per slice enable an increase in the number of slice-positions per patient and therefore a more detailed analysis of the lung function without adding more stress to the patient. In this paper, we present a new method in order to reduce the number of images for one coronal slice while preserving the quality of the ventilation maps. As the input is a time-dependent signal, we designed our model based on Gated Recurrent Units. The results show that our method is able to compute ventilation maps with a high quality using only 40 images. Furthermore, our method shows strong robustness regarding changes in the breathing cycles during the acquisition.