Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation

TL;DR

This study introduces a deep learning pipeline for cardiac biomarker estimation from CMR data that emphasizes improving scan-rescan reproducibility, demonstrating that interpolation methods enhance biomarker consistency across repeated scans.

Contribution

The paper proposes and evaluates interpolation-based approaches to improve scan-rescan precision of cardiac biomarkers, addressing a gap in reproducibility for deep learning methods in CMR analysis.

Findings

Both interpolation methods reduced scan-rescan variability.

Interpolation improved the confidence intervals of biomarker estimates.

Focus on reproducibility enhances longitudinal cardiac analysis.

Abstract

Quantification of cardiac biomarkers from cine cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages, such as increased accuracy and faster analysis. However, only a few studies have focused on the scan-rescan precision of the biomarker estimates, which is important for reproducibility and longitudinal analysis. Here, we propose a cardiac biomarker estimation pipeline that not only focuses on achieving high segmentation accuracy but also on improving the scan-rescan precision of the computed biomarkers, namely left and right ventricular ejection fraction, and left ventricular myocardial mass. We evaluate two approaches to improve the apical-basal resolution of the segmentations used for estimating the biomarkers: one based on image interpolation and one based on segmentation interpolation. Using a database comprising scan-rescan cine CMR data acquired from 92 subjects, we compare the performance of these two methods against ground truth (GT) segmentations and DL segmentations obtained before interpolation (baseline). The results demonstrate that both the image-based and segmentation-based interpolation methods were able to narrow Bland-Altman scan-rescan confidence intervals for all biomarkers compared to the GT and baseline performances. Our findings highlight the importance of focusing not only on segmentation accuracy but also on the consistency of biomarkers across repeated scans, which is crucial for longitudinal analysis of cardiac function.