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Löcke M, Garay Labra JE, Franco P, Uribe S, Bertoglio C. A comparison of phase unwrapping methods in velocity-encoded MRI for aortic flows. Magn Reson Med 2023; 90:2102-2115. [PMID: 37345719 DOI: 10.1002/mrm.29767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 04/17/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The phase of a MRI signal is used to encode the velocity of blood flow. Phase unwrapping artifacts may appear when aiming to improve the velocity-to-noise ratio (VNR) of the measured velocity field. This study aims to compare various unwrapping algorithms on ground-truth synthetic data generated using computational fluid dynamics (CFD) simulations. METHODS We compare four different phase unwrapping algorithms on two different synthetic datasets of four-dimensional flow MRI and 26 datasets of 2D PC-MRI acquisitions including the ascending and descending aorta. The synthetic datasets are constructed using CFD simulations of an aorta with a coarctation, with different levels of spatiotemporal resolutions and noise. The error of the unwrapped images was assessed by comparison against the ground truth velocity field in the synthetic data and dual-VENC reconstructions in the in vivo data. RESULTS Using the unwrapping algorithms, we were able to remove aliased voxels in the data almost entirely, reducing the L2-error compared to the ground truth by 50%-80%. Results indicated that the best choice of algorithm depend on the spatiotemporal resolution and noise level of the dataset. Temporal unwrapping is most successful with a high temporal and low spatial resolution (δ t = 30 $$ \delta t=30 $$ ms,h = 2 . 5 $$ h=2.5 $$ mm), reducing the L2-error by 70%-85%, while Laplacian unwrapping performs better with a lower temporal or better spatial resolution (δ t = 60 $$ \delta t=60 $$ ms,h = 1 . 5 $$ h=1.5 $$ mm), especially for signal-to-noise ratio (SNR) 12 as opposed to SNR 15, with an error reduction of 55%-85% compared to the 50%-75% achieved by the Temporal method. The differences in performance between the methods are statistically significant. CONCLUSIONS The temporal method and spatiotemporal Laplacian method provide the best results, with the spatiotemporal Laplacian being more robust. However, single-V enc $$ {V}_{\mathrm{enc}} $$ methods only situationally and not generally reach the performance of dual-V enc $$ {V}_{\mathrm{enc}} $$ unwrapping methods.
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Affiliation(s)
- Miriam Löcke
- Bernoulli Institute, University of Groningen, Groningen, Groningen, The Netherlands
| | | | - Pamela Franco
- Biomedical Imaging Center, School of Engineering, Universidad Católica de Chile, Santiago, Región Metropolitana de Santiago, Chile
| | - Sergio Uribe
- Biomedical Imaging Center, School of Engineering, Universidad Católica de Chile, Santiago, Región Metropolitana de Santiago, Chile
| | - Cristóbal Bertoglio
- Bernoulli Institute, University of Groningen, Groningen, Groningen, The Netherlands
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Franco P, Ma L, Schnell S, Carrillo H, Montalba C, Markl M, Bertoglio C, Uribe S. Comparison of Improved Unidirectional Dual Velocity-Encoding MRI Methods. J Magn Reson Imaging 2023; 57:763-773. [PMID: 35716109 DOI: 10.1002/jmri.28305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND In phase-contrast (PC) MRI, several dual velocity encoding methods have been proposed to robustly increase velocity-to-noise ratio (VNR), including a standard dual-VENC (SDV), an optimal dual-VENC (ODV), and bi- and triconditional methods. PURPOSE To develop a correction method for the ODV approach and to perform a comparison between methods. STUDY TYPE Case-control study. POPULATION Twenty-six volunteers. FIELD STRENGTH/SEQUENCE 1.5 T phase-contrast MRI with VENCs of 50, 75, and 150 cm/second. ASSESSMENT Since we acquired single-VENC protocols, we used the background phase from high-VENC (VENCH ) to reconstruct the low-VENC (VENCL ) phase. We implemented and compared the unwrapping methods for different noise levels and also developed a correction of the ODV method. STATISTICAL TESTS Shapiro-Wilk's normality test, two-way analysis of variance with homogeneity of variances was performed using Levene's test, and the significance level was adjusted by Tukey's multiple post hoc analysis with Bonferroni (P < 0.05). RESULTS Statistical analysis revealed no extreme outliers, normally distributed residuals, and homogeneous variances. We found statistically significant interaction between noise levels and the unwrapping methods. This implies that the number of non-unwrapped pixels increased with the noise level. We found that for β = VENCL /VENCH = 1/2, unwrapping methods were more robust to noise. The post hoc test showed a significant difference between the ODV corrected and the other methods, offering the best results regarding the number of unwrapped pixels. DATA CONCLUSIONS All methods performed similarly without noise, but the ODV corrected method was more robust to noise at the price of a higher computational time. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Pamela Franco
- Biomedical Imaging Center, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Electrical Engineering Department, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile.,Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile
| | - Liliana Ma
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, USA
| | - Susanne Schnell
- Institut für Physik, Universität Greifswald, Greifswald, Germany
| | - Hugo Carrillo
- Center for Mathematical Modeling, Universidad de Chile, Santiago, Chile.,Inria Chile Research Center, Santiago, Chile
| | - Cristian Montalba
- Biomedical Imaging Center, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile.,Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Michael Markl
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, USA
| | | | - Sergio Uribe
- Biomedical Imaging Center, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile.,Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile.,Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
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Herthum H, Hetzer S, Kreft B, Tzschätzsch H, Shahryari M, Meyer T, Görner S, Neubauer H, Guo J, Braun J, Sack I. Cerebral tomoelastography based on multifrequency MR elastography in two and three dimensions. Front Bioeng Biotechnol 2022; 10:1056131. [PMID: 36532573 PMCID: PMC9755504 DOI: 10.3389/fbioe.2022.1056131] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/21/2022] [Indexed: 09/01/2023] Open
Abstract
Purpose: Magnetic resonance elastography (MRE) generates quantitative maps of the mechanical properties of biological soft tissues. However, published values obtained by brain MRE vary largely and lack detail resolution, due to either true biological effects or technical challenges. We here introduce cerebral tomoelastography in two and three dimensions for improved data consistency and detail resolution while considering aging, brain parenchymal fraction (BPF), systolic blood pressure, and body mass index (BMI). Methods: Multifrequency MRE with 2D- and 3D-tomoelastography postprocessing was applied to the brains of 31 volunteers (age range: 22-61 years) for analyzing the coefficient of variation (CV) and effects of biological factors. Eleven volunteers were rescanned after 1 day and 1 year to determine intraclass correlation coefficient (ICC) and identify possible long-term changes. Results: White matter shear wave speed (SWS) was slightly higher in 2D-MRE (1.28 ± 0.02 m/s) than 3D-MRE (1.22 ± 0.05 m/s, p < 0.0001), with less variation after 1 day in 2D (0.33 ± 0.32%) than in 3D (0.96 ± 0.66%, p = 0.004), which was also reflected in a slightly lower CV and higher ICC in 2D (1.84%, 0.97 [0.88-0.99]) than in 3D (3.89%, 0.95 [0.76-0.99]). Remarkably, 3D-MRE was sensitive to a decrease in white matter SWS within only 1 year, whereas no change in white matter volume was observed during this follow-up period. Across volunteers, stiffness correlated with age and BPF, but not with blood pressure and BMI. Conclusion: Cerebral tomoelastography provides high-resolution viscoelasticity maps with excellent consistency. Brain MRE in 2D shows less variation across volunteers in shorter scan times than 3D-MRE, while 3D-MRE appears to be more sensitive to subtle biological effects such as aging.
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Affiliation(s)
- Helge Herthum
- Berlin Center for Advanced Neuroimaging, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
- Institute of Medical Informatics, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bernhard Kreft
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Heiko Tzschätzsch
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Mehrgan Shahryari
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tom Meyer
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Steffen Görner
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Hennes Neubauer
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jing Guo
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jürgen Braun
- Institute of Medical Informatics, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ingolf Sack
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
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