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Manini C, Hüllebrand M, Walczak L, Nordmeyer S, Jarmatz L, Kuehne T, Stern H, Meierhofer C, Harloff A, Erley J, Kelle S, Bannas P, Trauzeddel RF, Schulz-Menger J, Hennemuth A. Impact of training data composition on the generalizability of CNN aortic cross section segmentation in 4D Flow MRI. J Cardiovasc Magn Reson 2024:101081. [PMID: 39127260 DOI: 10.1016/j.jocmr.2024.101081] [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: 11/27/2023] [Revised: 07/08/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Time-resolved, three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow MRI segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies. METHODS The study population consists of 260 4D flow MRI datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced 3D U-net convolutional neural network (CNN) architecture with residual units was trained for 2D+t aortic cross-sectional segmentation. The model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation. RESULTS The representation of technical factors such as scanner vendor and field strength in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients' datasets performed well on datasets of healthy subjects but not vice versa. CONCLUSION This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow MRI, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.
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Affiliation(s)
- Chiara Manini
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.
| | - Markus Hüllebrand
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Fraunhofer MEVIS, Berlin, Germany
| | - Lars Walczak
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Fraunhofer MEVIS, Berlin, Germany
| | - Sarah Nordmeyer
- Department of Diagnostic and Interventional Radiology, Tübingen University Hospital, Tübingen, Germany
| | - Lina Jarmatz
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
| | - Titus Kuehne
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Germany
| | - Heiko Stern
- Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Germany
| | - Christian Meierhofer
- Congenital Heart Disease and Pediatric Cardiology, German Heart Center Munich, Germany
| | - Andreas Harloff
- Department of Neurology and Neurophysiology, University Medical Center Freiburg - Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jennifer Erley
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Germany; Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Corporate Member of Freie Universität Berlin and Humboldt - Universität zu Berlin, Berlin, Germany
| | - Sebastian Kelle
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Germany; Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Corporate Member of Freie Universität Berlin and Humboldt - Universität zu Berlin, Berlin, Germany
| | - Peter Bannas
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany
| | - Ralf Felix Trauzeddel
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany; Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité - Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine, Charité Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Jeanette Schulz-Menger
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany; Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité - Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany; Department of Cardiology and Nephrology, Helios Hospital Berlin-Buch, Berlin, Germany
| | - Anja Hennemuth
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany; Fraunhofer MEVIS, Berlin, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Germany; Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany
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Perinajová R, van de Ven T, Roelse E, Xu F, Juffermans J, Westenberg J, Lamb H, Kenjereš S. A comprehensive MRI-based computational model of blood flow in compliant aorta using radial basis function interpolation. Biomed Eng Online 2024; 23:69. [PMID: 39039565 PMCID: PMC11265469 DOI: 10.1186/s12938-024-01251-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 06/03/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Properly understanding the origin and progression of the thoracic aortic aneurysm (TAA) can help prevent its growth and rupture. For a better understanding of this pathogenesis, the aortic blood flow has to be studied and interpreted in great detail. We can obtain detailed aortic blood flow information using magnetic resonance imaging (MRI) based computational fluid dynamics (CFD) with a prescribed motion of the aortic wall. METHODS We performed two different types of simulations-static (rigid wall) and dynamic (moving wall) for healthy control and a patient with a TAA. For the latter, we have developed a novel morphing approach based on the radial basis function (RBF) interpolation of the segmented 4D-flow MRI geometries at different time instants. Additionally, we have applied reconstructed 4D-flow MRI velocity profiles at the inlet with an automatic registration protocol. RESULTS The simulated RBF-based movement of the aorta matched well with the original 4D-flow MRI geometries. The wall movement was most dominant in the ascending aorta, accompanied by the highest variation of the blood flow patterns. The resulting data indicated significant differences between the dynamic and static simulations, with a relative difference for the patient of 7.47±14.18% in time-averaged wall shear stress and 15.97±43.32% in the oscillatory shear index (for the whole domain). CONCLUSIONS In conclusion, the RBF-based morphing approach proved to be numerically accurate and computationally efficient in capturing complex kinematics of the aorta, as validated by 4D-flow MRI. We recommend this approach for future use in MRI-based CFD simulations in broad population studies. Performing these would bring a better understanding of the onset and growth of TAA.
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Affiliation(s)
- Romana Perinajová
- Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.
- J.M. Burgerscentrum Research School for Fluid Mechanics, Delft, The Netherlands.
| | - Thijn van de Ven
- Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Elise Roelse
- Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Fei Xu
- Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
- J.M. Burgerscentrum Research School for Fluid Mechanics, Delft, The Netherlands
| | - Joe Juffermans
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jos Westenberg
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hildo Lamb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Saša Kenjereš
- Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.
- J.M. Burgerscentrum Research School for Fluid Mechanics, Delft, The Netherlands.
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Winter P, Berhane H, Moore JE, Aristova M, Reichl T, Wollenberg J, Richter A, Jarvis KB, Patel A, Caprio FZ, Abdalla RN, Ansari SA, Markl M, Schnell S. Automated intracranial vessel segmentation of 4D flow MRI data in patients with atherosclerotic stenosis using a convolutional neural network. FRONTIERS IN RADIOLOGY 2024; 4:1385424. [PMID: 38895589 PMCID: PMC11183785 DOI: 10.3389/fradi.2024.1385424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 05/13/2024] [Indexed: 06/21/2024]
Abstract
Introduction Intracranial 4D flow MRI enables quantitative assessment of hemodynamics in patients with intracranial atherosclerotic disease (ICAD). However, quantitative assessments are still challenging due to the time-consuming vessel segmentation, especially in the presence of stenoses, which can often result in user variability. To improve the reproducibility and robustness as well as to accelerate data analysis, we developed an accurate, fully automated segmentation for stenosed intracranial vessels using deep learning. Methods 154 dual-VENC 4D flow MRI scans (68 ICAD patients with stenosis, 86 healthy controls) were retrospectively selected. Manual segmentations were used as ground truth for training. For automated segmentation, deep learning was performed using a 3D U-Net. 20 randomly selected cases (10 controls, 10 patients) were separated and solely used for testing. Cross-sectional areas and flow parameters were determined in the Circle of Willis (CoW) and the sinuses. Furthermore, the flow conservation error was calculated. For statistical comparisons, Dice scores (DS), Hausdorff distance (HD), average symmetrical surface distance (ASSD), Bland-Altman analyses, and interclass correlations were computed using the manual segmentations from two independent observers as reference. Finally, three stenosis cases were analyzed in more detail by comparing the 4D flow-based segmentations with segmentations from black blood vessel wall imaging (VWI). Results Training of the network took approximately 10 h and the average automated segmentation time was 2.2 ± 1.0 s. No significant differences in segmentation performance relative to two independent observers were observed. For the controls, mean DS was 0.85 ± 0.03 for the CoW and 0.86 ± 0.06 for the sinuses. Mean HD was 7.2 ± 1.5 mm (CoW) and 6.6 ± 3.7 mm (sinuses). Mean ASSD was 0.15 ± 0.04 mm (CoW) and 0.22 ± 0.17 mm (sinuses). For the patients, the mean DS was 0.85 ± 0.04 (CoW) and 0.82 ± 0.07 (sinuses), the HD was 8.4 ± 3.1 mm (CoW) and 5.7 ± 1.9 mm (sinuses) and the mean ASSD was 0.22 ± 0.10 mm (CoW) and 0.22 ± 0.11 mm (sinuses). Small bias and limits of agreement were observed in both cohorts for the flow parameters. The assessment of the cross-sectional lumen areas in stenosed vessels revealed very good agreement (ICC: 0.93) with the VWI segmentation but a consistent overestimation (bias ± LOA: 28.1 ± 13.9%). Discussion Deep learning was successfully applied for fully automated segmentation of stenosed intracranial vasculatures using 4D flow MRI data. The statistical analysis of segmentation and flow metrics demonstrated very good agreement between the CNN and manual segmentation and good performance in stenosed vessels. To further improve the performance and generalization, more ICAD segmentations as well as other intracranial vascular pathologies will be considered in the future.
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Affiliation(s)
- Patrick Winter
- Department of Medical Physics, Faculty of Mathematics and Natural Sciences, University of Greifswald, Greifswald, Germany
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Haben Berhane
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Jackson E. Moore
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Maria Aristova
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicacgo, IL, United States
| | - Teresa Reichl
- Department of Experimental Physics V, University of Wuerzburg, Wuerzburg, Germany
| | - Julian Wollenberg
- Department of Medical Physics, Faculty of Mathematics and Natural Sciences, University of Greifswald, Greifswald, Germany
- Department of Diagnostic Radiology, University Hospital of Greifswald, Greifswald, Germany
| | - Adam Richter
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Kelly B. Jarvis
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Abhinav Patel
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Fan Z. Caprio
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicacgo, IL, United States
| | - Ramez N. Abdalla
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Sameer A. Ansari
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
- Department of Neurology, Northwestern University, Feinberg School of Medicine, Chicacgo, IL, United States
| | - Michael Markl
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
| | - Susanne Schnell
- Department of Medical Physics, Faculty of Mathematics and Natural Sciences, University of Greifswald, Greifswald, Germany
- Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States
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Roda GF, Awad ME, Melton DH, Christiansen CL, Stoneback JW, Gaffney BMM. The Amputated Limb Gluteus Medius is Biomechanically Disadvantaged in Patients with Unilateral Transfemoral Amputation. Ann Biomed Eng 2024; 52:565-574. [PMID: 37946055 PMCID: PMC10922424 DOI: 10.1007/s10439-023-03400-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Abstract
Patients with transfemoral amputation (TFA) are at an increased risk of secondary musculoskeleteal comorbidities, primarily due to asymmetric joint loading. Amputated limb muscle weakness is also prevalent in the TFA population, yet all factors that contribute to muscle strength and thus joint loading are not well understood. Our objective was to bilaterally compare gluteus medius (GMED) muscle factors (volume, fatty infiltration, moment arm) that all contribute to joint loading in patients with TFA. Quantitative magnetic resonance (MR) images of the hip were collected from eight participants with unilateral TFA (2M/6F; age: 47.3 ± 14.7 y/o; BMI: 25.4 ± 5.3 kg/m2; time since amputation: 20.6 ± 15.0 years) and used to calculate normalized GMED muscle volume and fatty infiltration. Six participants participated in an instrumented gait analysis session that collected whole-body kinematics during overground walking. Subject-specific musculoskeletal models were used to calculate bilateral GMED (anterior, middle, posterior) moment arms and frontal plane hip joint angles across three gait cycles. Differences in volume, fatty infiltration, hip adduction-abduction angle, and peak moment arms were compared between limbs using paired Cohen's d effect sizes. Volume was smaller by 36.3 ± 18.8% (d = 1.7) and fatty infiltration was greater by 6.4 ± 7.8% (d = 0.8) in the amputated limb GMED compared to the intact limb. The amputated limb GMED abduction moment arms were smaller compared to the intact limb for both overground walking (anterior: d = 0.9; middle: d = 0.1.2) and during normal range of motion (anterior: d = 0.8; middle: d = 0.8) while bilateral hip adduction-abduction angles were similar during overground walking (d = 0.5). These results indicate that in patients with TFA, the amputated limb GMED is biomechanically disadvantaged compared to the intact limb, which may contribute to the etiology of secondary comorbidities. This population might benefit from movement retraining to lengthen the amputated limb GMED abduction moment arm during gait.
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Affiliation(s)
- Galen F Roda
- Department of Mechanical Engineering, University of Colorado Denver, Denver, CO, USA
| | - Mohamed E Awad
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- University of Colorado Osseointegration Research Consortium, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Danielle H Melton
- University of Colorado Osseointegration Research Consortium, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Cory L Christiansen
- University of Colorado Osseointegration Research Consortium, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- VA Eastern Colorado Health Care System, Aurora, CO, USA
| | - Jason W Stoneback
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- University of Colorado Osseointegration Research Consortium, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Brecca M M Gaffney
- Department of Mechanical Engineering, University of Colorado Denver, Denver, CO, USA.
- University of Colorado Osseointegration Research Consortium, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Center for Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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Ramaekers MJFG, Westenberg JJM, Venner MFGHM, Juffermans JF, van Assen HC, Te Kiefte BJC, Adriaans BP, Lamb HJ, Wildberger JE, Schalla S. Evaluating a Phase-Specific Approach to Aortic Flow: A 4D Flow MRI Study. J Magn Reson Imaging 2024; 59:1056-1067. [PMID: 37309838 DOI: 10.1002/jmri.28852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/25/2023] [Accepted: 05/27/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Aortic flow parameters can be quantified using 4D flow MRI. However, data are sparse on how different methods of analysis influence these parameters and how these parameters evolve during systole. PURPOSE To assess multiphase segmentations and multiphase quantification of flow-related parameters in aortic 4D flow MRI. STUDY TYPE Prospective. POPULATION 40 healthy volunteers (50% male, 28.9 ± 5.0 years) and 10 patients with thoracic aortic aneurysm (80% male, 54 ± 8 years). FIELD STRENGTH/SEQUENCE 4D flow MRI with a velocity encoded turbo field echo sequence at 3 T. ASSESSMENT Phase-specific segmentations were obtained for the aortic root and the ascending aorta. The whole aorta was segmented in peak systole. In all aortic segments, time to peak (TTP; for flow velocity, vorticity, helicity, kinetic energy, and viscous energy loss) and peak and time-averaged values (for velocity and vorticity) were calculated. STATISTICAL TESTS Static vs. phase-specific models were assessed using Bland-Altman plots. Other analyses were performed using phase-specific segmentations for aortic root and ascending aorta. TTP for all parameters was compared to TTP of flow rate using paired t-tests. Time-averaged and peak values were assessed using Pearson correlation coefficient. P < 0.05 was considered statistically significant. RESULTS In the combined group, velocity in static vs. phase-specific segmentations differed by 0.8 cm/sec for the aortic root, and 0.1 cm/sec (P = 0.214) for the ascending aorta. Vorticity differed by 167 sec-1 mL-1 (P = 0.468) for the aortic root, and by 59 sec-1 mL-1 (P = 0.481) for the ascending aorta. Vorticity, helicity, and energy loss in the ascending aorta, aortic arch, and descending aorta peaked significantly later than flow rate. Time-averaged velocity and vorticity values correlated significantly in all segments. DATA CONCLUSION Static 4D flow MRI segmentation yields comparable results as multiphase segmentation for flow-related parameters, eliminating the need for time-consuming multiple segmentations. However, multiphase quantification is necessary for assessing peak values of aortic flow-related parameters. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Mitch J F G Ramaekers
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center+, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jos J M Westenberg
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Max F G H M Venner
- Department of Cardiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Joe F Juffermans
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hans C van Assen
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Bouke P Adriaans
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Hildo J Lamb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Joachim E Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Simon Schalla
- Department of Cardiology, Maastricht University Medical Center+, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
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Merton R, Bosshardt D, Strijkers GJ, Nederveen AJ, Schrauben EM, van Ooij P. Reproducibility of 3D thoracic aortic displacement from 3D cine balanced SSFP at 3 T without contrast enhancement. Magn Reson Med 2024; 91:466-480. [PMID: 37831612 DOI: 10.1002/mrm.29856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/02/2023] [Accepted: 08/16/2023] [Indexed: 10/15/2023]
Abstract
PURPOSE Aortic motion has direct impact on the mechanical stresses acting on the aorta. In aortic disease, increased stiffness of the aorta may lead to decreased aortic motion over time, which could be a predictor for aortic dissection or rupture. This study investigates the reproducibility of obtaining 3D displacement and diameter maps quantified using accelerated 3D cine MRI at 3 T. METHODS A noncontrast-enhanced, free-breathing 3D cine sequence based on balanced SSFP and pseudo-spiral undersampling with high spatial isotropic resolution was developed (spatial/temporal resolution [1.6 mm]3 /67 ms). The thoracic aorta of 14 healthy volunteers was prospectively scanned three times at 3 T: twice on the same day and a third time 2 weeks later. Aortic displacement was calculated using iterative closest point nonrigid registration of manual segmentations of the 3D aorta at end-systole and mid-diastole. Interexamination and interobserver regional analysis of mean displacement for five regions of interest was performed using Bland-Altman analysis. Additionally, a complementary voxel-by-voxel analysis was done, allowing a more local inspection of the method. RESULTS No significant differences were found in mean and maximum displacement for any of the regions of interest for the interexamination and interobserver analysis. The maximum displacement measured in the lower half of the ascending aorta was 11.0 ± 3.4 mm (range: 3.0-17.5 mm) for the first scan. The smallest detectable change in mean displacement in the lower half of the ascending aorta was 3 mm. CONCLUSION Detailed 3D cine balanced SSFP at 3 T allows for reproducible quantification of systolic-diastolic mean aortic displacement within acceptable limits.
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Affiliation(s)
- Renske Merton
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Daan Bosshardt
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Gustav J Strijkers
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Biomedical Physics and Engineering, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Movement Sciences, Amsterdam, the Netherlands
| | - Aart J Nederveen
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Amsterdam Movement Sciences, Amsterdam, the Netherlands
| | - Eric M Schrauben
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Pim van Ooij
- Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Amsterdam Movement Sciences, Amsterdam, the Netherlands
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7
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van Tuijl RJ, Timmins KM, Velthuis BK, van Ooij P, Zwanenburg JJM, Ruigrok YM, van der Schaaf IC. Hemodynamic Parameters in the Parent Arteries of Unruptured Intracranial Aneurysms Depend on Aneurysm Size and Are Different Compared to Contralateral Arteries: A 7 Tesla 4D Flow MRI Study. J Magn Reson Imaging 2024; 59:223-230. [PMID: 37144669 DOI: 10.1002/jmri.28756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Different Circle of Willis (CoW) variants have variable prevalences of aneurysm development, but the hemodynamic variation along the CoW and its relation to presence and size of unruptured intracranial aneurysms (UIAs) are not well known. PURPOSE Gain insight into hemodynamic imaging markers of the CoW for UIA development by comparing these outcomes to the corresponding contralateral artery without an UIA using 4D flow magnetic resonance imaging (MRI). STUDY TYPE Retrospective, cross-sectional study. SUBJECTS Thirty-eight patients with an UIA, whereby 27 were women and a mean age of 62 years old. FIELD STRENGTH/SEQUENCE Four-dimensional phase-contrast (PC) MRI with a 3D time-resolved velocity encoded gradient echo sequence at 7 T. ASSESSMENT Hemodynamic parameters (blood flow, velocity pulsatility index [vPI], mean velocity, distensibility, and wall shear stress [peak systolic (WSSMAX ), and time-averaged (WSSMEAN )]) in the parent artery of the UIA were compared to the corresponding contralateral artery without an UIA and were related to UIA size. STATISTICAL TESTS Paired t-tests and Pearson Correlation tests. The threshold for statistical significance was P < 0.05 (two-tailed). RESULTS Blood flow, mean velocity, WSSMAX , and WSSMEAN were significantly higher, while vPI was lower, in the parent artery relative to contralateral artery. The WSSMAX of the parent artery significantly increased linearly while the WSSMEAN decreased linearly with increasing UIA size. CONCLUSIONS Hemodynamic parameters and WSS differ between parent vessels of UIAs and corresponding contralateral vessels. WSS correlates with UIA size, supporting a potential hemodynamic role in aneurysm pathology. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Rick J van Tuijl
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Kimberley M Timmins
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Birgitta K Velthuis
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pim van Ooij
- Department of Pediatric Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jaco J M Zwanenburg
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ynte M Ruigrok
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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8
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Garzia S, Scarpolini MA, Mazzoli M, Capellini K, Monteleone A, Cademartiri F, Positano V, Celi S. Coupling synthetic and real-world data for a deep learning-based segmentation process of 4D flow MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107790. [PMID: 37708583 DOI: 10.1016/j.cmpb.2023.107790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/07/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Phase contrast magnetic resonance imaging (4D flow MRI) is an imaging technique able to provide blood velocity in vivo and morphological information. This capability has been used to study mainly the hemodynamics of large vessels, such as the thoracic aorta. However, the segmentation of 4D flow MRI data is a complex and time-consuming task. In recent years, neural networks have shown great accuracy in segmentation tasks if large datasets are provided. Unfortunately, in the context of 4D flow MRI, the availability of these data is limited due to its recent adoption in clinical settings. In this study, we propose a pipeline for generating synthetic thoracic aorta phase contrast magnetic resonance angiography (PCMRA) to expand the limited dataset of patient-specific PCMRA images, ultimately improving the accuracy of the neural network segmentation even with a small real dataset. METHODS The pipeline involves several steps. First, a statistical shape model is used to synthesize new artificial geometries to improve data numerosity and variability. Secondly, computational fluid dynamics simulations are employed to simulate the velocity fields and, finally, after a downsampling and a signal-to-noise and velocity limit adjustment in both frequency and spatial domains, volumes are obtained using the PCMRA formula. These synthesized volumes are used in combination with real-world data to train a 3D U-Net neural network. Different settings of real and synthetic data are tested. RESULTS Incorporating synthetic data into the training set significantly improved the segmentation performance compared to using only real data. The experiments with synthetic data achieved a DICE score (DS) value of 0.83 and a better target reconstruction with respect to the case with only real data (DS = 0.65). CONCLUSION The proposed pipeline demonstrated the ability to increase the dataset in terms of numerosity and variability and to improve the segmentation accuracy for the thoracic aorta using PCMRA.
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Affiliation(s)
- Simone Garzia
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Information Engineering, University of Pisa, Via Caruso, Pisa, 56122, Italy
| | - Martino Andrea Scarpolini
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Industrial Engineering, University of Rome "Tor Vergata", Via del Politecnico, Roma, 00133, Italy
| | - Marilena Mazzoli
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy; Department of Information Engineering, University of Pisa, Via Caruso, Pisa, 56122, Italy
| | - Katia Capellini
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy
| | - Angelo Monteleone
- Department of Radiology, Fondazione Toscana G Monasterio, Via Moruzzi, Pisa, 56122, Italy
| | - Filippo Cademartiri
- Department of Radiology, Fondazione Toscana G Monasterio, Via Moruzzi, Pisa, 56122, Italy
| | - Vincenzo Positano
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy
| | - Simona Celi
- BioCardioLab, UOC Bioingegneria, Fondazione Toscana G Monasterio, Via Aurelia Sud, Massa, 54100, Italy.
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9
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Rothenberger SM, Patel NM, Zhang J, Schnell S, Craig BA, Ansari SA, Markl M, Vlachos PP, Rayz VL. Automatic 4D Flow MRI Segmentation Using the Standardized Difference of Means Velocity. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2360-2373. [PMID: 37028010 PMCID: PMC10474251 DOI: 10.1109/tmi.2023.3251734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We present a method to automatically segment 4D flow magnetic resonance imaging (MRI) by identifying net flow effects using the standardized difference of means (SDM) velocity. The SDM velocity quantifies the ratio between the net flow and observed flow pulsatility in each voxel. Vessel segmentation is performed using an F-test, identifying voxels with significantly higher SDM velocity values than background voxels. We compare the SDM segmentation algorithm against pseudo-complex difference (PCD) intensity segmentation of 4D flow measurements in in vitro cerebral aneurysm models and 10 in vitro Circle of Willis (CoW) datasets. We also compared the SDM algorithm to convolutional neural network (CNN) segmentation in 5 thoracic vasculature datasets. The in vitro flow phantom geometry is known, while the ground truth geometries for the CoW and thoracic aortas are derived from high-resolution time-of-flight (TOF) magnetic resonance angiography and manual segmentation, respectively. The SDM algorithm demonstrates greater robustness than PCD and CNN approaches and can be applied to 4D flow data from other vascular territories. The SDM to PCD comparison demonstrated an approximate 48% increase in sensitivity in vitro and 70% increase in the CoW, respectively; the SDM and CNN sensitivities were similar. The vessel surface derived from the SDM method was 46% closer to the in vitro surfaces and 72% closer to the in vitro TOF surfaces than the PCD approach. The SDM and CNN approaches both accurately identify vessel surfaces. The SDM algorithm is a repeatable segmentation method, enabling reliable computation of hemodynamic metrics associated with cardiovascular disease.
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10
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Bissell MM, Raimondi F, Ait Ali L, Allen BD, Barker AJ, Bolger A, Burris N, Carhäll CJ, Collins JD, Ebbers T, Francois CJ, Frydrychowicz A, Garg P, Geiger J, Ha H, Hennemuth A, Hope MD, Hsiao A, Johnson K, Kozerke S, Ma LE, Markl M, Martins D, Messina M, Oechtering TH, van Ooij P, Rigsby C, Rodriguez-Palomares J, Roest AAW, Roldán-Alzate A, Schnell S, Sotelo J, Stuber M, Syed AB, Töger J, van der Geest R, Westenberg J, Zhong L, Zhong Y, Wieben O, Dyverfeldt P. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update. J Cardiovasc Magn Reson 2023; 25:40. [PMID: 37474977 PMCID: PMC10357639 DOI: 10.1186/s12968-023-00942-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/30/2023] [Indexed: 07/22/2023] Open
Abstract
Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards.
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Affiliation(s)
- Malenka M Bissell
- Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), LIGHT Laboratories, Clarendon Way, University of Leeds, Leeds, LS2 9NL, UK.
| | | | - Lamia Ait Ali
- Institute of Clinical Physiology CNR, Massa, Italy
- Foundation CNR Tuscany Region G. Monasterio, Massa, Italy
| | - Bradley D Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alex J Barker
- Department of Radiology, Children's Hospital Colorado, University of Colorado Anschutz Medical Center, Aurora, USA
| | - Ann Bolger
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Nicholas Burris
- Department of Radiology, University of Michigan, Ann Arbor, USA
| | - Carl-Johan Carhäll
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Tino Ebbers
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | | | - Alex Frydrychowicz
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Julia Geiger
- Department of Diagnostic Imaging, University Children's Hospital, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Hojin Ha
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, South Korea
| | - Anja Hennemuth
- Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin, Berlin, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Michael D Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Albert Hsiao
- Department of Radiology, University of California, San Diego, CA, USA
| | - Kevin Johnson
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Liliana E Ma
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Duarte Martins
- Department of Pediatric Cardiology, Hospital de Santa Cruz, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Marci Messina
- Department of Radiology, Northwestern Medicine, Chicago, IL, USA
| | - Thekla H Oechtering
- Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck and Universität Zu Lübeck, Lübeck, Germany
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Pim van Ooij
- Department of Radiology & Nuclear Medicine, Amsterdam Cardiovascular Sciences, Amsterdam Movement Sciences, Amsterdam University Medical Centers, Location AMC, Amsterdam, The Netherlands
- Department of Pediatric Cardiology, Division of Pediatrics, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cynthia Rigsby
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Imaging, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Jose Rodriguez-Palomares
- Department of Cardiology, Hospital Universitari Vall d´Hebron,Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red-CV, CIBER CV, Madrid, Spain
| | - Arno A W Roest
- Department of Pediatric Cardiology, Willem-Alexander's Children Hospital, Leiden University Medical Center and Center for Congenital Heart Defects Amsterdam-Leiden, Leiden, The Netherlands
| | | | - Susanne Schnell
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Physics, Institute of Physics, University of Greifswald, Greifswald, Germany
| | - Julio Sotelo
- School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile
| | - Matthias Stuber
- Département de Radiologie Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Ali B Syed
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Johannes Töger
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Rob van der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jos Westenberg
- CardioVascular Imaging Group (CVIG), Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Liang Zhong
- National Heart Centre Singapore, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Yumin Zhong
- Department of Radiology, School of Medicine, Shanghai Children's Medical Center Affiliated With Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Oliver Wieben
- Departments of Radiology and Medical Physics, University of Wisconsin, Madison, WI, USA
| | - Petter Dyverfeldt
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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11
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Huellebrand M, Jarmatz L, Manini C, Laube A, Ivantsits M, Schulz-Menger J, Nordmeyer S, Harloff A, Hansmann J, Kelle S, Hennemuth A. Radiomics-based aortic flow profile characterization with 4D phase-contrast MRI. Front Cardiovasc Med 2023; 10:1102502. [PMID: 37077748 PMCID: PMC10106758 DOI: 10.3389/fcvm.2023.1102502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 03/06/2023] [Indexed: 04/05/2023] Open
Abstract
4D PC MRI of the aorta has become a routinely available examination, and a multitude of single parameters have been suggested for the quantitative assessment of relevant flow features for clinical studies and diagnosis. However, clinically applicable assessment of complex flow patterns is still challenging. We present a concept for applying radiomics for the quantitative characterization of flow patterns in the aorta. To this end, we derive cross-sectional scalar parameter maps related to parameters suggested in literature such as throughflow, flow direction, vorticity, and normalized helicity. Derived radiomics features are selected with regard to their inter-scanner and inter-observer reproducibility, as well as their performance in the differentiation of sex-, age- and disease-related flow properties. The reproducible features were tested on user-selected examples with respect to their suitability for characterizing flow profile types. In future work, such signatures could be applied for quantitative flow assessment in clinical studies or disease phenotyping.
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Affiliation(s)
- Markus Huellebrand
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany
- Correspondence: Markus Huellebrand
| | - Lina Jarmatz
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Chiara Manini
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Ann Laube
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Matthias Ivantsits
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Jeanette Schulz-Menger
- Charité Universitätsmedizin Berlin, Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Helios Hospital Berlin-Buch, Department of Cardiology and Nephrology, Berlin, Germany
| | - Sarah Nordmeyer
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Andreas Harloff
- Department of Neurology, University Medical Center Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jochen Hansmann
- Department of Radiology, Theresienkrankenhaus und St. Hedwig-Klinik, Mannheim, Germany
| | - Sebastian Kelle
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Anja Hennemuth
- Deutsches Herzzentrum der Charité, Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Fraunhofer Institute for Digital Medicine MEVIS, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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12
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Bustamante M, Viola F, Engvall J, Carlhäll C, Ebbers T. Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning. J Magn Reson Imaging 2023; 57:191-203. [PMID: 35506525 PMCID: PMC10946960 DOI: 10.1002/jmri.28221] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue. PURPOSE To develop and evaluate a deep learning-based segmentation method to automatically segment the cardiac chambers and great thoracic vessels from 4D flow MRI. STUDY TYPE Retrospective. SUBJECTS A total of 205 subjects, including 40 healthy volunteers and 165 patients with a variety of cardiac disorders were included. Data were randomly divided into training (n = 144), validation (n = 20), and testing (n = 41) sets. FIELD STRENGTH/SEQUENCE A 3 T/time-resolved velocity encoded 3D gradient echo sequence (4D flow MRI). ASSESSMENT A 3D neural network based on the U-net architecture was trained to segment the four cardiac chambers, aorta, and pulmonary artery. The segmentations generated were compared to manually corrected atlas-based segmentations. End-diastolic (ED) and end-systolic (ES) volumes of the four cardiac chambers were calculated for both segmentations. STATISTICAL TESTS Dice score, Hausdorff distance, average surface distance, sensitivity, precision, and miss rate were used to measure segmentation accuracy. Bland-Altman analysis was used to evaluate agreement between volumetric parameters. RESULTS The following evaluation metrics were computed: mean Dice score (0.908 ± 0.023) (mean ± SD), Hausdorff distance (1.253 ± 0.293 mm), average surface distance (0.466 ± 0.136 mm), sensitivity (0.907 ± 0.032), precision (0.913 ± 0.028), and miss rate (0.093 ± 0.032). Bland-Altman analyses showed good agreement between volumetric parameters for all chambers. Limits of agreement as percentage of mean chamber volume (LoA%), left ventricular: 9.3%, 13.5%, left atrial: 12.4%, 16.9%, right ventricular: 9.9%, 15.6%, and right atrial: 18.7%, 14.4%; for ED and ES, respectively. DATA CONCLUSION The addition of this technique to the 4D flow MRI assessment pipeline could expedite and improve the utility of this type of acquisition in the clinical setting. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Mariana Bustamante
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
| | - Federica Viola
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Jan Engvall
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Carl‐Johan Carlhäll
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
- Department of Clinical Physiology in Linköping, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Tino Ebbers
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
- Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
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13
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Bai X, Fu M, Li Z, Gao P, Zhao H, Li R, Sui B. Distribution and regional variation of wall shear stress in the curved middle cerebral artery using four-dimensional flow magnetic resonance imaging. Quant Imaging Med Surg 2022; 12:5462-5473. [PMID: 36465823 PMCID: PMC9703110 DOI: 10.21037/qims-22-67] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/30/2022] [Indexed: 12/05/2023]
Abstract
BACKGROUND To investigate the distribution and regional variation of wall shear stress (WSS) in the curved middle cerebral artery (MCA) in healthy individuals using four-dimensional (4D) flow magnetic resonance imaging (MRI). METHODS A total of 44 healthy participants (18 males; mean ages: 27.16±5.69 years) were included in this cross-sectional study. The WSS parameters of mean, minimum, and maximum values, the coefficient of variation of time-averaged WSS (TAWSSCV), and the maximum values of the oscillatory shear index (OSI) were calculated and compared in the curved proximal (M1) segments. Three cross-sectional planes were selected: the location perpendicular to the beginning of the long axis of the curved M1 segment of the MCA (proximal section), the most curved M1 location (curved M1 section), and the location before the insular (M2) segment bifurcation (distal section). The WSS and OSI parameters of the proximal, curved, and distal sections of the curved M1 segment were compared, including the inner and outer curvatures of the curved M1 section. RESULTS Of the curved M1 segments, the curved M1 section had significantly lower minimum TAWSS values than the proximal (P=0.031) and distal sections (P=0.002), and the curved M1 section had significantly higher maximum OSI values than the distal section (P=0.001). The TAWSSCV values at the curved M1 section were significantly higher than the proximal (P=0.001) and distal sections (P<0.001). At the curved M1 section, the inner curvature showed a significantly lower minimum TAWSS (P=0.013) and higher maximum OSI values (P=0.002) than the outer curvature. CONCLUSIONS There are distribution variation of WSS and OSI parameters at the curved M1 section of the curved MCA, and the inner curvature of the curved M1 section has the lowest WSS and highest OSI distribution. The local hemodynamic features of the curved MCA may be related to the predilection for atherosclerotic plaque development.
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Affiliation(s)
- Xiaoyan Bai
- Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mingzhu Fu
- Center for Biomedical Imaging Research, Biomedical Engineering Department, School of Medicine, Tsinghua University, Beijing, China
| | - Zhiye Li
- Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Peiyi Gao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Haiqing Zhao
- Department of Radiology, Beijing Chui Yang Liu Hospital, Beijing, China
| | - Rui Li
- Center for Biomedical Imaging Research, Biomedical Engineering Department, School of Medicine, Tsinghua University, Beijing, China
| | - Binbin Sui
- Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
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14
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van Tuijl RJ, Ruigrok YM, Ophelders MEH, Vos IN, van der Schaaf IC, Zwanenburg JJM, Velthuis BK. Relationship between diameter asymmetry and blood flow in the pre-communicating (A1) segment of the anterior cerebral arteries. J Neuroradiol 2022; 50:402-406. [DOI: 10.1016/j.neurad.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/03/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022]
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15
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4D Flow MRI in Ascending Aortic Aneurysms: Reproducibility of Hemodynamic Parameters. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
(1) Background: Aorta hemodynamics have been associated with aortic remodeling, but the reproducibility of its assessment has been evaluated marginally in patients with thoracic aortic aneurysm (TAA). The current study evaluated intra- and interobserver reproducibility of 4D flow MRI-derived hemodynamic parameters (normalized flow displacement, flow jet angle, wall shear stress (WSS) magnitude, axial WSS, circumferential WSS, WSS angle, vorticity, helicity, and local normalized helicity (LNH)) in TAA patients; (2) Methods: The thoracic aorta of 20 patients was semi-automatically segmented on 4D flow MRI data in 5 systolic phases by 3 different observers. Each time-dependent segmentation was manually improved and partitioned into six anatomical segments. The hemodynamic parameters were quantified per phase and segment. The coefficient of variation (COV) and intraclass correlation coefficient (ICC) were calculated; (3) Results: A total of 2400 lumen segments were analyzed. The mean aneurysm diameter was 50.8 ± 2.7 mm. The intra- and interobserver analysis demonstrated a good reproducibility (COV = 16–30% and ICC = 0.84–0.94) for normalized flow displacement and jet angle, a very good-to-excellent reproducibility (COV = 3–26% and ICC = 0.87–1.00) for all WSS components, helicity and LNH, and an excellent reproducibility (COV = 3–10% and ICC = 0.96–1.00) for vorticity; (4) Conclusion: 4D flow MRI-derived hemodynamic parameters are reproducible within the thoracic aorta in TAA patients.
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16
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Rijnberg FM, Westenberg JJM, van Assen HC, Juffermans JF, Kroft LJM, van den Boogaard PJ, Terol Espinosa de Los Monteros C, Warmerdam EG, Leiner T, Grotenhuis HB, Jongbloed MRM, Hazekamp MG, Roest AAW, Lamb HJ. 4D flow cardiovascular magnetic resonance derived energetics in the Fontan circulation correlate with exercise capacity and CMR-derived liver fibrosis/congestion. J Cardiovasc Magn Reson 2022; 24:21. [PMID: 35346249 PMCID: PMC8962091 DOI: 10.1186/s12968-022-00854-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 03/15/2022] [Indexed: 12/12/2022] Open
Abstract
AIM This study explores the relationship between in vivo 4D flow cardiovascular magnetic resonance (CMR) derived blood flow energetics in the total cavopulmonary connection (TCPC), exercise capacity and CMR-derived liver fibrosis/congestion. BACKGROUND The Fontan circulation, in which both caval veins are directly connected with the pulmonary arteries (i.e. the TCPC) is the palliative approach for single ventricle patients. Blood flow efficiency in the TCPC has been associated with exercise capacity and liver fibrosis using computational fluid dynamic modelling. 4D flow CMR allows for assessment of in vivo blood flow energetics, including kinetic energy (KE) and viscous energy loss rate (EL). METHODS Fontan patients were prospectively evaluated between 2018 and 2021 using a comprehensive cardiovascular and liver CMR protocol, including 4D flow imaging of the TCPC. Peak oxygen consumption (VO2) was determined using cardiopulmonary exercise testing (CPET). Iron-corrected whole liver T1 (cT1) mapping was performed as a marker of liver fibrosis/congestion. KE and EL in the TCPC were computed from 4D flow CMR and normalized for inflow. Furthermore, blood flow energetics were compared between standardized segments of the TCPC. RESULTS Sixty-two Fontan patients were included (53% male, 17.3 ± 5.1 years). Maximal effort CPET was obtained in 50 patients (peak VO2 27.1 ± 6.2 ml/kg/min, 56 ± 12% of predicted). Both KE and EL in the entire TCPC (n = 28) were significantly correlated with cT1 (r = 0.50, p = 0.006 and r = 0.39, p = 0.04, respectively), peak VO2 (r = - 0.61, p = 0.003 and r = - 0.54, p = 0.009, respectively) and % predicted peak VO2 (r = - 0.44, p = 0.04 and r = - 0.46, p = 0.03, respectively). Segmental analysis indicated that the most adverse flow energetics were found in the Fontan tunnel and left pulmonary artery. CONCLUSIONS Adverse 4D flow CMR derived KE and EL in the TCPC correlate with decreased exercise capacity and increased levels of liver fibrosis/congestion. 4D flow CMR is promising as a non-invasive screening tool for identification of patients with adverse TCPC flow efficiency.
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Affiliation(s)
- Friso M Rijnberg
- Department of Cardiothoracic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
| | - Jos J M Westenberg
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hans C van Assen
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Joe F Juffermans
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Lucia J M Kroft
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | | | - Tim Leiner
- Department of Radiology, Utrecht Medical Center, Utrecht, The Netherlands
| | - Heynric B Grotenhuis
- Department of Pediatric Cardiology, Utrecht Medical Center, Utrecht, The Netherlands
| | - Monique R M Jongbloed
- Department of Cardiology and Anatomy and Embryology, Leiden University Medical Center, Leiden, The Netherlands
| | - Mark G Hazekamp
- Department of Cardiothoracic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands
| | - Arno A W Roest
- Department of Pediatric Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Hildo J Lamb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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17
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Isoda H, Fukuyama A. Quality Control for 4D Flow MR Imaging. Magn Reson Med Sci 2022; 21:278-292. [PMID: 35197395 PMCID: PMC9680545 DOI: 10.2463/mrms.rev.2021-0165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/08/2022] [Indexed: 01/06/2023] Open
Abstract
In recent years, 4D flow MRI has become increasingly important in clinical applications for the blood vessels in the whole body, heart, and cerebrospinal fluid. 4D flow MRI has advantages over 2D cine phase-contrast (PC) MRI in that any targeted area of interest can be analyzed post-hoc, but there are some factors to be considered, such as ensuring measurement accuracy, a long imaging time and post-processing complexity, and interobserver variability.Due to the partial volume phenomenon caused by low spatial and temporal resolutions, the accuracy of flow measurement in 4D flow MRI is reduced. For spatial resolution, it is recommended to include at least four voxels in the vessel of interest, and if possible, six voxels. In large vessels such as the aorta, large voxels can be secured and SNR can be maintained, but in small cerebral vessels, SNR is reduced, resulting in reduced accuracy. A temporal resolution of less than 40 ms is recommended. The velocity-to-noise ratio (VNR) of low-velocity blood flow is low, resulting in poor measurement accuracy. The use of dual velocity encoding (VENC) or multi-VENC is recommended to avoid velocity wrap around and to increase VNR. In order to maintain sufficient spatio-temporal resolution, a longer imaging time is required, leading to potential patient movement during examination and a corresponding decrease in measurement accuracy.For the clinical application of new technologies, including various acceleration techniques, in vitro and in vivo accuracy verification based on existing accuracy-validated 2D cine PC MRI and 4D flow MRI, as well as accuracy verification on the conservation of mass' principle, should be performed, and intraobserver repeatability, interobserver reproducibility, and test-retest reproducibility should be checked.
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Affiliation(s)
- Haruo Isoda
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Biomedical Imaging Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Atsushi Fukuyama
- Faculty of Health Sciences, Department of Radiological Sciences, Japan Healthcare University, Sapporo, Hokkaido, Japan
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18
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van Tuijl RJ, Ruigrok YM, Geurts LJ, van der Schaaf IC, Biessels GJ, Rinkel GJE, Velthuis BK, Zwanenburg JJM. Does the Internal Carotid Artery Attenuate Blood-Flow Pulsatility in Small Vessel Disease? A 7 T 4D-Flow MRI Study. J Magn Reson Imaging 2022; 56:527-535. [PMID: 34997655 PMCID: PMC9546379 DOI: 10.1002/jmri.28062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/23/2021] [Accepted: 12/23/2021] [Indexed: 11/18/2022] Open
Abstract
Background Increased cerebral blood‐flow pulsatility is associated with cerebral small vessel disease (cSVD). Reduced pulsatility attenuation over the internal carotid artery (ICA) could be a contributing factor to the development of cSVD and could be associated with intracranial ICA calcification (iICAC). Purpose To compare pulsatility, pulsatility attenuation, and distensibility along the ICA between patients with cSVD and controls and to assess the association between iICAC and pulsatility and distensibility. Study Type Retrospective, explorative cross‐sectional study. Subjects A total of 17 patients with cSVD, manifested as lacunar infarcts or deep intracerebral hemorrhage, and 17 age‐ and sex‐matched controls. Field Strength/Sequence Three‐dimensional (3D) T1‐weighted gradient echo imaging and 4D phase‐contrast (PC) MRI with a 3D time‐resolved velocity encoded gradient echo sequence at 7 T. Assessment Blood‐flow velocity pulsatility index (vPI) and arterial distensibility were calculated for seven ICA segments (C1–C7). iICAC presence and volume were determined from available brain CT scans (acquired as part of standard clinical care) in patients with cSVD. Statistical Tests Independent t‐tests and linear mixed models. The threshold for statistically significance was P < 0.05 (two tailed). Results The cSVD group showed significantly higher ICA vPI and significantly lower distensibility compared to controls. Controls showed significant attenuation of vPI over the carotid siphon (−4.9% ± 3.6%). In contrast, patients with cSVD showed no attenuation, but a significant increase of vPI (+6.5% ± 3.1%). iICAC presence and volume correlated positively with vPI (r = 0.578) in patients with cSVD and negatively with distensibility (r = −0.386). Conclusion Decreased distensibility and reduced pulsatility attenuation are associated with increased iICAC and may contribute to cSVD. Confirmation in a larger prospective study is required. Evidence Level 2 Technical Efficacy Stage 2
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Affiliation(s)
- Rick J van Tuijl
- Department of Radiology, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Ynte M Ruigrok
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Lennart J Geurts
- Department of Radiology, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Irene C van der Schaaf
- Department of Radiology, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Gabriël J E Rinkel
- Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Birgitta K Velthuis
- Department of Radiology, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Jaco J M Zwanenburg
- Department of Radiology, Brain Center, University Medical Center Utrecht, Utrecht University, the Netherlands
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19
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Ascending aorta curvature and flow displacement are associated with accelerated aortic growth at long-term follow-up: A MRI study in Marfan and thoracic aortic aneurysm patients. IJC HEART & VASCULATURE 2022; 38:100926. [PMID: 34977327 PMCID: PMC8683588 DOI: 10.1016/j.ijcha.2021.100926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/05/2021] [Indexed: 01/16/2023]
Abstract
Background Aortic aneurysm formation is associated with increased risk of aortic dissection. Current diagnostic strategies are focused on diameter growth, the predictive value of aortic morphology and function remains underinvestigated. We aimed to assess the long-term prognostic value of ascending aorta (AA) curvature radius, regional pulse wave velocity (PWV) and flow displacement (FD) on aortic dilatation/elongation and evaluated adverse outcomes (proximal aortic surgery, dissection/rupture, death) in Marfan and non-syndromic thoracic aortic aneurysm (NTAA) patients. Methods Long-term magnetic resonance imaging (MRI) and clinical follow-up of two previous studies consisting of 21 Marfan and 40 NTAA patients were collected. Baseline regional PWV, AA curvature radius and normalized FD were assessed as well as diameter and length growth rate at follow-up. Multivariate linear regression was performed to evaluate whether baseline predictors were associated with aortic growth.=. Results Of the 61 patients, 49 patients were included with MRI follow-up (n = 44) and/or adverse aortic events (n = 7). Six had undergone aortic surgery, no dissection/rupture occurred and one patient died during follow-up. During 8.0 [7.3-10.7] years of follow-up, AA growth rate was 0.40 ± 0.31 mm/year. After correction for confounders, AA curvature radius (p = 0.01), but not FD or PWV, was a predictor of AA dilatation. Only FD was associated with AA elongation (p = 0.01). Conclusion In Marfan and non-syndromic thoracic aortic aneurysm patients, ascending aorta curvature radius and flow displacement are associated with accelerated aortic growth at long-term follow-up. These markers may aid in the risk stratification of ascending aorta elongation and aneurysm formation.
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20
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Chen CW, Fang YF, Tseng YH, Wong MY, Lin YH, Hsu YC, Lin BS, Huang YK. Before and after Endovascular Aortic Repair in the Same Patients with Aortic Dissection: A Cohort Study of Four-Dimensional Phase-Contrast Magnetic Resonance Imaging. Diagnostics (Basel) 2021; 11:diagnostics11101912. [PMID: 34679608 PMCID: PMC8534695 DOI: 10.3390/diagnostics11101912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/09/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022] Open
Abstract
(1) Background: We used four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) to evaluate the impact of an endovascular aortic repair (TEVAR) on aortic dissection. (2) Methods: A total of 10 patients received 4D PC-MRI on a 1.5-T MR both before and after TEVAR. (3) Results: The aortas were repaired with either a GORE TAG Stent (Gore Medical; n = 7) or Zenith Dissection Endovascular Stent (Cook Medical; n = 3). TEVAR increased the forward flow volume of the true lumen (TL) (at the abdominal aorta, p = 0.047). TEVAR also reduced the regurgitant fraction in the TL at the descending aorta but increased it in the false lumen (FL). After TEVAR, the stroke distance increased in the TL (at descending and abdominal aorta, p = 0.018 and 0.015), indicating more effective blood transport per heartbeat. Post-stenting quantitative flow revealed that the reductions in stroke volume, backward flow volume, and absolute stroke volume were greater when covered stents were used than when bare stents were used in the FL of the descending aorta. Bare stents had a higher backward flow volume than covered stents did. (4) Conclusions: TEVAR increased the stroke volume in the TL and increased the regurgitant fraction in the FL in patients with aortic dissection.
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Affiliation(s)
- Chien-Wei Chen
- Department of Diagnostic Radiology, Chia Yi Chang Gung Memorial Hospital, Putzu City 61363, Taiwan; (C.-W.C.); (Y.-C.H.)
- Department of Diagnostic Radiology, Chang Gung University, Taoyuan 33302, Taiwan
| | - Yueh-Fu Fang
- Department of Thoracic Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 33302, Taiwan; (Y.-F.F.); (Y.-H.T.)
- Department of Thoracic Medicine, Chang Gung University, College of Medicine, Taoyuan 33302, Taiwan
| | - Yuan-Hsi Tseng
- Department of Thoracic Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan 33302, Taiwan; (Y.-F.F.); (Y.-H.T.)
- Department of Thoracic Medicine, Chang Gung University, College of Medicine, Taoyuan 33302, Taiwan
| | - Min Yi Wong
- Division of Thoracic and Cardiovascular Surgery, Chia Yi Chang Gung Memorial Hospital, Putzu City 61363, Taiwan; (M.Y.W.); (Y.-H.L.)
- Division of Thoracic and Cardiovascular Surgery, Chang Gung University, Taoyuan 33302, Taiwan
| | - Yu-Hui Lin
- Division of Thoracic and Cardiovascular Surgery, Chia Yi Chang Gung Memorial Hospital, Putzu City 61363, Taiwan; (M.Y.W.); (Y.-H.L.)
- Division of Thoracic and Cardiovascular Surgery, Chang Gung University, Taoyuan 33302, Taiwan
| | - Yin-Chen Hsu
- Department of Diagnostic Radiology, Chia Yi Chang Gung Memorial Hospital, Putzu City 61363, Taiwan; (C.-W.C.); (Y.-C.H.)
- Department of Diagnostic Radiology, Chang Gung University, Taoyuan 33302, Taiwan
| | - Bor-Shyh Lin
- Institute of Imaging and Biomedical Photonics, National Yang Ming Chiao Tung University, Tainan 71150, Taiwan;
- Department of Medical Research, Chi-Mei Medical Center, Tainan 30010, Taiwan
| | - Yao-Kuang Huang
- Division of Thoracic and Cardiovascular Surgery, Chia Yi Chang Gung Memorial Hospital, Putzu City 61363, Taiwan; (M.Y.W.); (Y.-H.L.)
- Division of Thoracic and Cardiovascular Surgery, Chang Gung University, Taoyuan 33302, Taiwan
- Correspondence:
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21
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Boban M. Editorial for "Traveling Volunteers - A Multi-Vendor, Multi-Center Study on Reproducibility and Comparability of 4D Flow Derived Aortic Hemodynamics in Cardiovascular Magnetic Resonance". J Magn Reson Imaging 2021; 55:223-224. [PMID: 34159678 DOI: 10.1002/jmri.27800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 11/10/2022] Open
Affiliation(s)
- Marko Boban
- Faculty for Dental Medicine and Health, University of "JJ Strossmayer", Osijek, Croatia.,Medical Faculty Rijeka, University of Rijeka, Rijeka, Croatia.,Medical Faculty Osijek, University of "JJ Strossmayer", Osijek, Croatia.,Working Group for Cardiac Magnetic Resonance of the European Society for Cardiology, Biot, France.,International Society for Magnetic Resonance Imaging, Weizmann Institute for Science, Rehovot, Israel
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22
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Perinajová R, Juffermans JF, Westenberg JJM, van der Palen RLF, van den Boogaard PJ, Lamb HJ, Kenjereš S. Geometrically induced wall shear stress variability in CFD-MRI coupled simulations of blood flow in the thoracic aortas. Comput Biol Med 2021; 133:104385. [PMID: 33894502 DOI: 10.1016/j.compbiomed.2021.104385] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 04/02/2021] [Accepted: 04/03/2021] [Indexed: 01/16/2023]
Abstract
Aortic aneurysm is associated with aberrant blood flow and wall shear stress (WSS). This can be studied by coupling magnetic resonance imaging (MRI) with computational fluid dynamics (CFD). For patient-specific simulations, extra attention should be given to the variation in segmentation of the MRI data-set and its effect on WSS. We performed CFD simulations of blood flow in the aorta for ten different volunteers and provided corresponding WSS distributions. The aorta of each volunteer was segmented four times. The same inlet and outlet boundary conditions were applied for all segmentation variations of each volunteer. Steady-state CFD simulations were performed with inlet flow based on phase-contrast MRI during peak systole. We show that the commonly used comparison of mean and maximal values of WSS, based on CFD in the different segments of the thoracic aorta, yields good to excellent correlation (0.78-0.95) for rescan and moderate to excellent correlation (0.64-1.00) for intra- and interobserver reproducibility. However, the effect of geometrical variations is higher for the voxel-to-voxel comparison of WSS. With this analysis method, the correlation for different segments of the whole aorta is poor to moderate (0.43-0.66) for rescan and poor to good (0.48-0.73) for intra- and interobserver reproducibility. Therefore, we advise being critical about the CFD results based on the MRI segmentations to avoid possible misinterpretation. While the global values of WSS are similar for different modalities, the variation of results is high when considering the local distributions.
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Affiliation(s)
- Romana Perinajová
- Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology and J.M. Burgerscentrum Research School for Fluid Mechanics, Delft, the Netherlands.
| | - Joe F Juffermans
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Jos J M Westenberg
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Roel L F van der Palen
- Division of Pediatric Cardiology, Department of Pediatrics, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Hildo J Lamb
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Saša Kenjereš
- Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology and J.M. Burgerscentrum Research School for Fluid Mechanics, Delft, the Netherlands.
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23
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Wentland AL. Editorial for "Reproducibility of Aorta Segmentation on 4D Flow MRI in Healthy Volunteers". J Magn Reson Imaging 2020; 53:1280-1281. [PMID: 33135303 DOI: 10.1002/jmri.27432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 10/22/2020] [Indexed: 11/08/2022] Open
Affiliation(s)
- Andrew L Wentland
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wisconsin, USA
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