# Intra- and inter-field strength reproducibility of deep-learning based real-time cardiac MRI cine sequences with breath hold and in free breathing

## TL;DR

This study evaluates the consistency of deep-learning-based cardiac MRI measurements across different magnetic field strengths and breathing conditions in healthy adults.

## Contribution

The study introduces a novel assessment of intra- and inter-field strength reproducibility using deep-learning-based real-time cardiac MRI.

## Key findings

- Breath-hold sequences showed significant differences in stroke volume and ejection fraction at 1.5T.
- Free-breathing sequences had minor, clinically irrelevant stroke volume variation at 1.5T.
- Most volumetric parameters showed excellent reproducibility across field strengths and breathing conditions.

## Abstract

To assess intra- and inter-field strength reproducibility of volumetric parameters using deep-learning-based real-time cardiac cine MRI during breath-hold (BH) and free-breathing (FB). In this prospective single-center study, 56 healthy adults underwent cardiac MRI at 1.5 T. Of these, 33 had a follow-up scan after 2–7 weeks, and 23 received an additional same-day scan at 3 T with the same protocol. Real-time cine sequences (1RR), including short-axis and 2-, 3-, and 4-chamber views, were acquired in BH and FB. Left ventricular volumes were analyzed using automated segmentation. Intra-class correlation coefficients (ICC) and subjective image quality (sIQ) were used to assess reproducibility. At 1.5 T, BH sequences showed significant differences in stroke volume (SV) and ejection fraction (EF), while FB sequences revealed only minor, clinically irrelevant SV variation. End-diastolic volume (EDV) and left ventricular (LV) mass showed excellent reproducibility (ICC > 0.93); end-systolic volume (ESV) and SV had good reproducibility (ICC 0.79–0.88). Inter-field comparisons revealed significant differences for EDV (BH), and for SV and EF (FB), though most parameters remained consistent. EDV, ESV, and LV mass showed excellent reproducibility (ICC > 0.90), and SV showed good to excellent agreement. Deep-learning-based real-time cine MRI provides good to excellent reproducibility of cardiac volumetric parameters across field strengths and breathing conditions.