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

TL;DR

This paper introduces an automated framework for rapid cine cardiac MRI reconstruction and analysis that reduces scan times significantly while maintaining high image quality and accurate functional biomarker estimation.

Contribution

It presents a novel integrated deep learning-based system enabling quality-controlled reconstruction, segmentation, and analysis of undersampled cine CMR data with active acquisition capabilities.

Findings

Reduced mean scan time from 12 to 4 seconds per slice.

Achieved image quality sufficient for clinical parameter estimation within 5% accuracy.

Demonstrated effectiveness on UK Biobank and in-vivo data.

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 from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to within 5% mean absolute difference.