Hojin Ha1,2,3, John-Peder Kvitting2,3,4, Petter Dyverfeldt2,3, Tino Ebbers2,3. 1. Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, Republic of Korea. 2. Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. 3. Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. 4. Department of Cardiothoracic Surgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
Abstract
PURPOSE: To validate pressure drop measurements using 4D flow MRI-based turbulence production in various shapes of stenotic stenoses. METHODS: In vitro flow phantoms with seven different 3D-printed aortic valve geometries were constructed and scanned with 4D flow MRI with six-directional flow encoding (ICOSA6). The pressure drop through the valve was non-invasively predicted based on the simplified Bernoulli, the extended Bernoulli, the turbulence production, and the shear-scaling methods. Linear regression and agreement of the predictions with invasively measured pressure drop were analyzed. RESULTS: All pressure drop predictions using 4D Flow MRI were linearly correlated to the true pressure drop but resulted in different regression slopes. The regression slope and 95% limits of agreement for the simplified Bernoulli method were 1.35 and 11.99 ± 21.72 mm Hg. The regression slope and 95% limits of agreement for the extended Bernoulli method were 1.02 and 0.74 ± 8.48 mm Hg. The regression slope and 95% limits of agreement for the turbulence production method were 0.89 and 0.96 ± 8.01 mm Hg. The shear-scaling method presented good correlation with an invasively measured pressure drop, but the regression slope varied between 0.36 and 1.00 depending on the shear-scaling coefficient. CONCLUSION: The pressure drop assessment based on the turbulence production method agrees well with the extended Bernoulli method and invasively measured pressure drop in various shapes of the aortic valve. Turbulence-based pressure drop estimation can, as a complement to the conventional Bernoulli method, play a role in the assessment of valve diseases.
PURPOSE: To validate pressure drop measurements using 4D flow MRI-based turbulence production in various shapes of stenotic stenoses. METHODS: In vitro flow phantoms with seven different 3D-printed aortic valve geometries were constructed and scanned with 4D flow MRI with six-directional flow encoding (ICOSA6). The pressure drop through the valve was non-invasively predicted based on the simplified Bernoulli, the extended Bernoulli, the turbulence production, and the shear-scaling methods. Linear regression and agreement of the predictions with invasively measured pressure drop were analyzed. RESULTS: All pressure drop predictions using 4D Flow MRI were linearly correlated to the true pressure drop but resulted in different regression slopes. The regression slope and 95% limits of agreement for the simplified Bernoulli method were 1.35 and 11.99 ± 21.72 mm Hg. The regression slope and 95% limits of agreement for the extended Bernoulli method were 1.02 and 0.74 ± 8.48 mm Hg. The regression slope and 95% limits of agreement for the turbulence production method were 0.89 and 0.96 ± 8.01 mm Hg. The shear-scaling method presented good correlation with an invasively measured pressure drop, but the regression slope varied between 0.36 and 1.00 depending on the shear-scaling coefficient. CONCLUSION: The pressure drop assessment based on the turbulence production method agrees well with the extended Bernoulli method and invasively measured pressure drop in various shapes of the aortic valve. Turbulence-based pressure drop estimation can, as a complement to the conventional Bernoulli method, play a role in the assessment of valve diseases.
Authors: David Marlevi; Bram Ruijsink; Maximilian Balmus; Desmond Dillon-Murphy; Daniel Fovargue; Kuberan Pushparajah; Cristóbal Bertoglio; Massimiliano Colarieti-Tosti; Matilda Larsson; Pablo Lamata; C Alberto Figueroa; Reza Razavi; David A Nordsletten Journal: Sci Rep Date: 2019-02-04 Impact factor: 4.379
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