A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Abstract

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as acquired data are sufficient to produce high quality reconstructions and segmentations. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform realistic simulations of radial k-space acquisitions on a dataset of subjects from the UK Biobank and present results on in-vivo cine CMR k-space data collected from healthy subjects. The results demonstrate that our method can produce quality-controlled images in a mean scan time reduced

Real-time MRI-guided radiotherapy with deep learning

“…Interestingly, spatial and temporal information from MRI is relevant in other applications, such as cardiac imaging 133,225 or dynamic contrast-enhanced MRI 244,245 . Future work could investigate the application of MODEST, retraining the currently used model for these applications.…”

“…With CNNs, the time-consuming model training can be performed offline before treatment. Then, the trained model can be used for fast, online inference, achieving reconstruction quality on par or better than compressed sensing within tens of milliseconds for 2D imaging 225 .…”