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Rivera-Rivera LA, Vikner T, Eisenmenger L, Johnson SC, Johnson KM. Four-dimensional flow MRI for quantitative assessment of cerebrospinal fluid dynamics: Status and opportunities. NMR IN BIOMEDICINE 2024; 37:e5082. [PMID: 38124351 PMCID: PMC11162953 DOI: 10.1002/nbm.5082] [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] [Received: 05/16/2023] [Revised: 10/03/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023]
Abstract
Neurological disorders can manifest with altered neurofluid dynamics in different compartments of the central nervous system. These include alterations in cerebral blood flow, cerebrospinal fluid (CSF) flow, and tissue biomechanics. Noninvasive quantitative assessment of neurofluid flow and tissue motion is feasible with phase contrast magnetic resonance imaging (PC MRI). While two-dimensional (2D) PC MRI is routinely utilized in research and clinical settings to assess flow dynamics through a single imaging slice, comprehensive neurofluid dynamic assessment can be limited or impractical. Recently, four-dimensional (4D) flow MRI (or time-resolved three-dimensional PC with three-directional velocity encoding) has emerged as a powerful extension of 2D PC, allowing for large volumetric coverage of fluid velocities at high spatiotemporal resolution within clinically reasonable scan times. Yet, most 4D flow studies have focused on blood flow imaging. Characterizing CSF flow dynamics with 4D flow (i.e., 4D CSF flow) is of high interest to understand normal brain and spine physiology, but also to study neurological disorders such as dysfunctional brain metabolite waste clearance, where CSF dynamics appear to play an important role. However, 4D CSF flow imaging is challenged by the long T1 time of CSF and slower velocities compared with blood flow, which can result in longer scan times from low flip angles and extended motion-sensitive gradients, hindering clinical adoption. In this work, we review the state of 4D CSF flow MRI including challenges, novel solutions from current research and ongoing needs, examples of clinical and research applications, and discuss an outlook on the future of 4D CSF flow.
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Affiliation(s)
- Leonardo A Rivera-Rivera
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Tomas Vikner
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Radiation Sciences, Radiation Physics and Biomedical Engineering, Umeå University, Umeå, Sweden
| | - Laura Eisenmenger
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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Singh RK, Nayak NP, Behl T, Arora R, Anwer MK, Gulati M, Bungau SG, Brisc MC. Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences. Diagnostics (Basel) 2024; 14:139. [PMID: 38248016 PMCID: PMC11154438 DOI: 10.3390/diagnostics14020139] [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: 11/10/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.
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Affiliation(s)
- Rahul Kumar Singh
- Energy Cluster, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (R.K.S.); (N.P.N.)
| | - Nirlipta Priyadarshini Nayak
- Energy Cluster, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (R.K.S.); (N.P.N.)
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Mohali 140306, Punjab, India
| | - Rashmi Arora
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, Punjab, India;
| | - Md. Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia;
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara 1444411, Punjab, India;
- Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology Sydney, Ultimo, NSW 20227, Australia
| | - Simona Gabriela Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, 410028 Oradea, Romania
- Doctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, Romania
| | - Mihaela Cristina Brisc
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania;
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Yousefpour Shahrivar R, Karami F, Karami E. Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches. Biomimetics (Basel) 2023; 8:519. [PMID: 37999160 PMCID: PMC10669151 DOI: 10.3390/biomimetics8070519] [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: 08/29/2023] [Revised: 10/05/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.
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Affiliation(s)
- Ramin Yousefpour Shahrivar
- Department of Biology, College of Convergent Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, 14515-775, Iran
| | - Fatemeh Karami
- Department of Medical Genetics, Applied Biophotonics Research Center, Science and Research Branch, Islamic Azad University, Tehran, 14515-775, Iran
| | - Ebrahim Karami
- Department of Engineering and Applied Sciences, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
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Nath R, Kazemi A, Callahan S, Stoddard MF, Amini AA. 4Dflow-VP-Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI. Magn Reson Med 2023; 90:2175-2189. [PMID: 37496183 PMCID: PMC10615364 DOI: 10.1002/mrm.29791] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 07/28/2023]
Abstract
PURPOSE To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI. METHODS A novel deep learning-based approach is proposed to estimate pressure gradient across stenosis from four-dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity-to-Presure Network (4Dflow-VP-Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD-simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow-VP-Net was compared to catheter-based pressure measurements for available flow rates, in vitro and Doppler echocardiography-based pressure measurement, in vivo. RESULTS Relative pressures calculated by 4Dflow-VP-Net and in vitro pressure catheterization revealed strong correlation (r2 = 0.91). Correlations analysis of TVPG from reference CFD and 4Dflow-VP-Net for 450 simulated flow conditions showed strong correlation (r2 = 0.99). TVPG from in vitro MRI had a correlation coefficient of r2 = 0.98 with reference CFD. 4Dflow-VP-Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (r2 = 0.85). Bland-Altman analysis of TVPG measurements showed mean bias and limits of agreement of -0.20 ± 2.07 mmHg and 0.19 ± 0.45 mmHg for CFD-simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow-VP-Net (10.99 ± 6.77 mmHg) was observed. CONCLUSION The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.
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Affiliation(s)
- Ruponti Nath
- Medical Imaging Lab, Department of Electrical and Computer Engineering, University of Louisville, Louisville, Kentucky, USA
- Robley Rex Veterans Affairs Medical Center, Louisville, Kentucky, USA
| | - Amirkhosro Kazemi
- Medical Imaging Lab, Department of Electrical and Computer Engineering, University of Louisville, Louisville, Kentucky, USA
- Robley Rex Veterans Affairs Medical Center, Louisville, Kentucky, USA
| | - Sean Callahan
- Medical Imaging Lab, Department of Electrical and Computer Engineering, University of Louisville, Louisville, Kentucky, USA
- Robley Rex Veterans Affairs Medical Center, Louisville, Kentucky, USA
| | - Marcus F. Stoddard
- Robley Rex Veterans Affairs Medical Center, Louisville, Kentucky, USA
- Cardiovascular Division, University of Louisville School of Medicine, Louisville, Kentucky, USA
| | - Amir A. Amini
- Medical Imaging Lab, Department of Electrical and Computer Engineering, University of Louisville, Louisville, Kentucky, USA
- Robley Rex Veterans Affairs Medical Center, Louisville, Kentucky, USA
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Xu S, Wang F, Mai P, Peng Y, Shu X, Nie R, Zhang H. Mechanism Analysis of Vascular Calcification Based on Fluid Dynamics. Diagnostics (Basel) 2023; 13:2632. [PMID: 37627891 PMCID: PMC10453151 DOI: 10.3390/diagnostics13162632] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/05/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Vascular calcification is the abnormal deposition of calcium phosphate complexes in blood vessels, which is regarded as the pathological basis of multiple cardiovascular diseases. The flowing blood exerts a frictional force called shear stress on the vascular wall. Blood vessels have different hydrodynamic properties due to discrepancies in geometric and mechanical properties. The disturbance of the blood flow in the bending area and the branch point of the arterial tree produces a shear stress lower than the physiological magnitude of the laminar shear stress, which can induce the occurrence of vascular calcification. Endothelial cells sense the fluid dynamics of blood and transmit electrical and chemical signals to the full-thickness of blood vessels. Through crosstalk with endothelial cells, smooth muscle cells trigger osteogenic transformation, involved in mediating vascular intima and media calcification. In addition, based on the detection of fluid dynamics parameters, emerging imaging technologies such as 4D Flow MRI and computational fluid dynamics have greatly improved the early diagnosis ability of cardiovascular diseases, showing extremely high clinical application prospects.
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Affiliation(s)
- Shuwan Xu
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
| | - Feng Wang
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
| | - Peibiao Mai
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
| | - Yanren Peng
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, China; (Y.P.); (X.S.)
| | - Xiaorong Shu
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, China; (Y.P.); (X.S.)
| | - Ruqiong Nie
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, China; (Y.P.); (X.S.)
| | - Huanji Zhang
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
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Wieben O, Roberts GS, Corrado PA, Johnson KM, Roldán-Alzate A. Four-Dimensional Flow MR Imaging: Technique and Advances. Magn Reson Imaging Clin N Am 2023; 31:433-449. [PMID: 37414470 DOI: 10.1016/j.mric.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
4D Flow MRI is an advanced imaging technique for comprehensive non-invasive assessment of the cardiovascular system. The capture of the blood velocity vector field throughout the cardiac cycle enables measures of flow, pulse wave velocity, kinetic energy, wall shear stress, and more. Advances in hardware, MRI data acquisition and reconstruction methodology allow for clinically feasible scan times. The availability of 4D Flow analysis packages allows for more widespread use in research and the clinic and will facilitate much needed multi-center, multi-vendor studies in order to establish consistency across scanner platforms and to enable larger scale studies to demonstrate clinical value.
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Affiliation(s)
- Oliver Wieben
- Department of Medical Physics, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, 1111 Highland Avenue, Suite 1127, Madison, WI 53705-2275, USA; Department of Radiology, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, 1111 Highland Avenue, Suite 1127, Madison, WI 53705-2275, USA.
| | - Grant S Roberts
- Department of Medical Physics, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, 1111 Highland Avenue, Madison, WI 53705-2275, USA
| | - Philip A Corrado
- Accuray Incorporated, 1414 Raleigh Road, Suite 330, DurhamChapel Hill, NC 27517, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, 1111 Highland Avenue, Room 1133, Madison, WI 53705-2275, USA; Department of Radiology, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, 1111 Highland Avenue, Room 1133, Madison, WI 53705-2275, USA
| | - Alejandro Roldán-Alzate
- Department of Mechanical Engineering, University of Wisconsin-Madison, Room: 3035, 1513 University Avenue, Madison, WI 53706, USA; Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
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7
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Udine M, Loke YH, Goudar S, Donofrio MT, Truong U, Krishnan A. The current state and potential innovation of fetal cardiac MRI. Front Pediatr 2023; 11:1219091. [PMID: 37520049 PMCID: PMC10375913 DOI: 10.3389/fped.2023.1219091] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Fetal cardiac MRI is a rapidly evolving form of diagnostic testing with utility as a complementary imaging modality for the diagnosis of congenital heart disease and assessment of the fetal cardiovascular system. Previous technical limitations without cardiac gating for the fetal heart rate has been overcome with recent technology. There is potential utility of fetal electrocardiography for direct cardiac gating. In addition to anatomic assessment, innovative technology has allowed for assessment of blood flow, 3D datasets, and 4D flow, providing important insight into fetal cardiovascular physiology. Despite remaining technical barriers, with increased use of fCMR worldwide, it will become an important clinical tool to improve the prenatal care of fetuses with CHD.
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Affiliation(s)
- Michelle Udine
- Division of Cardiology, Children’s National Hospital, Washington, DC, United States
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Ramaekers MJFG, Westenberg JJM, Adriaans BP, Nijssen EC, Wildberger JE, Lamb HJ, Schalla S. A clinician's guide to understanding aortic 4D flow MRI. Insights Imaging 2023; 14:114. [PMID: 37395817 DOI: 10.1186/s13244-023-01458-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 06/03/2023] [Indexed: 07/04/2023] Open
Abstract
Four-dimensional flow magnetic resonance imaging is an emerging technique which may play a role in diagnosis and risk-stratification of aortic disease. Some knowledge of flow dynamics and related parameters is necessary to understand and apply this technique in clinical workflows. The purpose of the current review is to provide a guide for clinicians to the basics of flow imaging, frequently used flow-related parameters, and their relevance in the context of aortic disease.Clinical relevance statement Understanding normal and abnormal aortic flow could improve clinical care in patients with aortic disease.
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Affiliation(s)
- Mitch J F G Ramaekers
- Department of Cardiology and Radiology and Nuclear Medicine, Maastricht University Medical Center +, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands.
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands.
- Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
| | - Jos J M Westenberg
- Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Bouke P Adriaans
- Department of Cardiology and Radiology and Nuclear Medicine, Maastricht University Medical Center +, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Estelle C Nijssen
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center +, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands
| | - Joachim E Wildberger
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center +, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands
| | - Hildo J Lamb
- Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Simon Schalla
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Center +, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands
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Barrera-Naranjo A, Marin-Castrillon DM, Decourselle T, Lin S, Leclerc S, Morgant MC, Bernard C, De Oliveira S, Boucher A, Presles B, Bouchot O, Christophe JJ, Lalande A. Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets. J Imaging 2023; 9:123. [PMID: 37367471 DOI: 10.3390/jimaging9060123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI.
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Affiliation(s)
| | | | | | - Siyu Lin
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Sarah Leclerc
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Marie-Catherine Morgant
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, 21078 Dijon, France
| | - Chloé Bernard
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, 21078 Dijon, France
| | | | - Arnaud Boucher
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Benoit Presles
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
| | - Olivier Bouchot
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, 21078 Dijon, France
| | | | - Alain Lalande
- IFTIM, ICMUB Laboratory, University of Burgundy, 21078 Dijon, France
- Department of Medical Imaging, University Hospital of Dijon, 21078 Dijon, France
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