Background: 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. Methods: Three hundred forty-six 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. Results: Flow measurements using the nPCcor images after nPCcor automatic classification were found to be accurate for 87% (281/323) 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 70% (16/23) 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% (62/84) 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% (8/323) after nPCcor correction, 3% better than for uncorrected images. Conclusion: 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.

Early structural changes to the heart, including the chambers and the coronary arteries, provide important information on pre-clinical heart disease like cardiac failure. Currently, contrast-enhanced cardiac computed tomography angiography (CCTA) is the preferred modality for the visualization of the cardiac chambers and the coronaries. In clinical practice not every patient undergoes a CCTA scan; many patients receive only a non-contrast-enhanced calcium scoring CT scan (CTCS), which has less radiation dose and does not require the administration of contrast agent. Quantifying cardiac structures in such images is challenging, as they lack the contrast present in CCTA scans. Such quantification would however be relevant, as it enables population based studies with only a CTCS scan. The purpose of this work is therefore to investigate the feasibility of automatic segmentation and quantification of cardiac structures viz whole heart, left atrium, left ventricle, right atrium, right ventricle and aortic root from CTCS scans. A fully automatic multi- atlas-based segmentation approach is used to segment the cardiac structures. Results show that the segmentation overlap between the automatic method and that of the reference standard have a Dice similarity coefficient of 0.91 on average for the cardiac chambers. The mean surface-to-surface distance error over all the cardiac structures is 1.4±1.7 mm. The automatically obtained cardiac chamber volumes using the CTCS scans have an excellent correlation when compared to the volumes in corresponding CCTA scans, a Pearson correlation coefficient (R) of 0.95 is obtained. Our fully automatic method enables large-scale assessment of cardiac structures on non-contrastenhanced CT scans.