Caval to pulmonary 3D flow distribution in patients with Fontan circulation and impact of potential 4D flow MRI error sources. Issue 2 (15th September 2018)

• Record Type: Journal Article
• Authors: Jarvis, Kelly; Schnell, Susanne; Barker, Alex J.; Rose, Michael; Robinson, Joshua D.; Rigsby, Cynthia K.; Markl, Michael
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 2(2019)
• Page Start: 1205
• Page End: 1218
• Publication Date: 2018-09-15
• Subjects: 4D flow MRI -- background phase errors -- congenital heart disease -- flow distribution -- Fontan circulation -- probabilistic tracking -- segmentation -- uncertainty -- velocity noise; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27455
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Uneven flow distribution in patients with Fontan circulation is suspected to lead to complications. 4D flow MRI offers evaluation using time‐resolved pathlines; however, the potential error is not well understood. The aim of this study was to systematically assess variability in flow distribution caused by well‐known sources of error. Methods: 4D flow MRI was acquired in 14 patients with Fontan circulation. Flow distribution was quantified by the % of caval venous flow pathlines reaching the left and right pulmonary arteries. Impact of data acquisition and data processing uncertainties were investigated by (1) probabilistic 4D blood flow tracking at varying noise levels, (2) down‐sampling to mimic acquisition at different spatial resolutions, (3) pathline calculation with and without eddy current correction, and (4) varied segmentation of the Fontan geometry to mimic analysis errors. Results: Averaged among the cohort, uncertainties accounted for flow distribution errors from noise ≤3.2%, low spatial resolution ≤2.3% to 3.8%, eddy currents ≤6.4%, and inaccurate segmentation ≤3.9% to 9.1% (dilation and erosion, respectively). In a worst‐case scenario (maximum additive errors for all 4 sources), flow distribution errors were as high as 22.5%. Conclusion: Inaccuracies related to postprocessing (segmentation, eddy currents) resulted in the largest potential error (≤15.5% combined) whereas errors related to data acquisition (noise, low spatial resolution) had a lower impact (≤5.5%‐7.0% combined). Whereas it is unlikely that these errors will be additive or affect the identification of severe asymmetry, these results illustrate the importance of eddy current correction and accurate segmentation to minimize Fontan flow distribution errors.