# Automatic failure mode evaluation using non-linear phase contrast correction to improve flow measurement accuracy in cardiovascular magnetic resonance phase contrast imaging

## TL;DR

A new algorithm improves flow measurement accuracy in MRI scans by automatically detecting and correcting errors.

## Contribution

A non-linear phase contrast correction algorithm with automatic failure mode classification is validated for improved accuracy in cardiovascular MRI.

## Key findings

- nPCcor improved accuracy by 6% and 17% over uncorrected and linear-corrected methods.
- nPCcor correctly identified 70% of cases likely to produce inaccurate flow measurements.
- nPCcor showed a 22% improvement in accuracy for scanners with large phase offsets.

## Abstract

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.

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.

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.

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

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