Two-dimensional (2D) cine imaging is essential in routine clinical cardiac MR (CMR) exams for assessing cardiac structure and function. Traditional cine imaging requires patients to hold their breath for extended periods and maintain consistent heartbeats for optimal image quality, which can be challenging for those with impaired breath-holding capacity or irregular heart rhythms. This study aims to systematically assess the performance of a deep learning-based reconstruction (Sonic DL Cine, GE HealthCare, Waukesha, WI, USA) for accelerated cardiac cine acquisition. Multiple retrospective experiments were designed and conducted to comprehensively evaluate the technique using data from an MR-dedicated extended cardiac torso anatomical phantom (digital phantom) and healthy volunteers on different cardiac planes. Image quality, spatiotemporal sharpness, and biventricular cardiac function were qualitatively and quantitatively compared between Sonic DL Cine-reconstructed images with various accelerations (4-fold to 12-fold) and fully sampled reference images. Both digital phantom and in vivo experiments demonstrate that Sonic DL Cine can accelerate cine acquisitions by up to 12-fold while preserving comparable SNR, contrast, and spatiotemporal sharpness to fully sampled reference images. Measurements of cardiac function metrics indicate that function measurements from Sonic DL Cine-reconstructed images align well with those from fully sampled reference images. In conclusion, this study demonstrates that Sonic DL Cine is able to reconstruct highly under-sampled (up to 12-fold acceleration) cine datasets while preserving SNR, contrast, spatiotemporal sharpness, and quantification accuracy for cardiac function measurements. It also provides a feasible approach for thoroughly evaluating the deep learning-based method.

Aims: Cardiovascular magnetic resonance (CMR) 4-dimensional (4D) intraventricular flow analysis quantifies volume and kinetic energy (KE) of direct flow (DF), and residual volume (ReV) components, illustrating heart failure (HF) haemodynamic changes. Study aims were (1) compare volume and KE indexed (KEi) of DF and ReV between groups. (2) Assess relationship between 4D-flow parameters with CMR parameters. Methods and results: 187 subjects (63.0 ± 17.1 years; 101 males) comprising 78 controls, 47 HF with preserved ejection fraction (HFpEF), 25 HF with mildly reduced ejection fraction (HFmrEF), 37 HF with reduced ejection fraction (HFrEF) were included. Volume and KEi of DF, and ReV were obtained from 4D flow CMR images. Controls had highest DF volume and systolic KEi (control 35.0% and 54.7 µJ/mL), followed by HFpEF (22.7% and 61.4 µJ/mL), HFmrEF (13.1% and 43.3 µJ/mL), HFrEF (5.2% and 33.1 µJ/mL) (P < 0.001). ReV and diastolic KEi were lowest in controls (26.0% and 7.9 µJ/mL), and higher across HFpEF (31.8% and 7.8 µJ/mL), HFmrEF (41.6% and 10.8 µJ/mL), HFrEF (49.5% and 11.5 µJ/mL) (P < 0.001). DF volume correlated positively with left ventricular ejection fraction (LVEF) (r = 0.794), but negatively with LV-end-diastolic volume indexed (EDVi) (r = −0.563) (all P < 0.001). ReV correlated negatively with LVEF (r = −0.737) but positively with LV-EDVi (r = 0.602) (all P < 0.001). Loss of two diastolic peaks in KE time curves for HF patients were shown. Conclusion: CMR 4D DF and ReV with their KEi showed haemodynamic changes and KEi time curve pattern distortions in HF.

Aims: Quantitative stress perfusion (QP) cardiac magnetic resonance (CMR) can be performed using the dual sequence (DS) or dual bolus (DB) technique. DS does not require additional contrast and image acquisition but needs a research sequence. DB can be performed on all magnetic resonance imaging (MRI) scanners with standard perfusion sequences but requires additional contrast injection and image acquisition. Our aim was to compare the prognostic significance of DB and DS. Methods and Results: DB and DS were performed on the same patient and the same examination. Analysts were blinded to clinical outcomes. Stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) were quantified. The primary outcome was a composite of major adverse cardiovascular events (MACE) comprising acute coronary syndrome, stroke, heart failure (HF) hospitalization, late revascularization, and all-cause death. 570 patients (mean age: 63.2 ± 12.3 years; 61.2% male) were recruited. Median follow-up was 743 days; 54 events were documented. All QP CMR variables demonstrated significance in univariate Cox regression [DB stress MBF [HR = 0.53 (95%CI:0.35–0.78)], DB MPR [HR = 0.38 (95%CI:0.22–0.66)], DS stress MBF [HR = 0.27 (95%CI:0.18–0.40)] and DS MPR [HR = 0.19 (95%CI:0.13–0.29)]]. On multivariable Cox regression models, only DB MPR, DS MBF, and DS MPR remained significant for MACE (HR = 0.50 (95%CI:0.28–0.89), HR = 0.35 (95%CI:0.23–0.53); HR = 0.23 (95%CI 0.15–0.36), respectively). Harrell’s C-index of DS MPR and DS stress MBF showed significantly better prognostication than their DB counterparts (P < 0.001 and P = 0.012, respectively). Conclusion: In this blinded comparison, DS stress MBF and MPR demonstrated better prognostication than DB stress MBF and MPR. Our findings support DS as the preferred approach where available.

Phase contrast (PC) cardiovascular magnetic resonance (CMR) is clinically used to quantify flow. The quantification accuracy is diminished by background phase errors. Image-based background phase correction algorithms are commercially available, but their accuracy is still under evaluation. Here, we validate a recently developed non-linear phase contrast correction (nPCcor) algorithm that includes automatic failure mode classification in a large single-vendor multi-scanner retrospective study. 346 through-plane PC images at the aortic valve (AAo) and pulmonary artery (PA) were acquired on three different GE HealthCare 1.5T clinical MRI scanners. Each PC scan was repeated on a static phantom, and the static phantom corrected PC series was considered as the reference standard. Two image-based static tissue background phase corrections were applied on each PC series: a linear and the nPCcor. Accuracy of nPCcor was studied by comparing the net flow in the vessel of interest for the uncorrected, linear-corrected, and nPCcor images with respect to the static phantom-corrected series. Accuracy was defined as a difference in net flow ≤10% with respect to the static phantom corrected net flow. Flow measurements using the nPCcor images after nPCcor automatic classification were found to be accurate for 87% of PC datasets, 6% and 17% better than using uncorrected and linear-corrected (p<0.05), respectively. Most importantly, nPCcor was able to correctly identify 16 out of 23 (70%) PC cases likely to provide inaccurate flow measurements. Flow measurements after nPCcor in the scanner with the largest phase offsets were found to be accurate for 74% of PC datasets, 22% better than using the uncorrected images (p<0.05). nPCcor correction was statistically significant more accurate than linear correction for all scanners (p<0.05). The percentage of regurgitation reclassification of ≥1 category decreased to 8% after nPCcor correction, 4% better than for uncorrected images. nPCcor with automatic failure mode evaluation improved accuracy with respect to no correction and linear correction and successfully identified PC scans that are likely to result in unreliable flow measurements. nPCcor performance and phase offset errors varied greatly among scanners using the same CMR protocol. nPCcor has higher impact in scanners exhibiting the largest background phase offsets. observational study.