Caval to Pulmonary 3D Flow Distribution in Patients with Fontan Circulation and Impact of Potential 4D Flow MRI Error Sources

Overview

Journal Magn Reson Med

Publisher Wiley

Specialty Radiology

Date 2018 Oct 3

PMID 30277276

Citations 7

Authors

Kelly Jarvis

Susanne Schnell

Alex J Barker

Michael Rose

Joshua D Robinson

Cynthia K Rigsby

Michael Markl

Affiliations

Soon will be listed here.

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.

Citing Articles

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications.

Bracamonte J, Saunders S, Wilson J, Truong U, Soares J Appl Sci (Basel). 2023; 12(8).

PMID: 36911244 PMC: 10004130. DOI: 10.3390/app12083954.

Impact of pulmonary artery flow distribution on Fontan hemodynamics and flow energetics.

Weiss E, Robinson J, Sodhi A, Markl M, Rigsby C Pediatr Radiol. 2023; 53(5):900-909.

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Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data.

Rutkowski D, Roldan-Alzate A, Johnson K Sci Rep. 2021; 11(1):10240.

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Respiration Dependency of Caval Blood Flow in Patients with Fontan Circulation: Quantification Using 5D Flow MRI.

Bastkowski R, Bindermann R, Brockmeier K, Weiss K, Maintz D, Giese D Radiol Cardiothorac Imaging. 2021; 1(4):e190005.

PMID: 33778515 PMC: 7977808. DOI: 10.1148/ryct.2019190005.

Reduced scan time and superior image quality with 3D flow MRI compared to 4D flow MRI for hemodynamic evaluation of the Fontan pathway.

Rijnberg F, van Assen H, Juffermans J, Kroft L, van den Boogaard P, de Koning P Sci Rep. 2021; 11(1):6507.

PMID: 33753790 PMC: 7985309. DOI: 10.1038/s41598-021-85936-6.

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