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Berglund J, Liljeblad M, Baron T. Unwrapping phase contrast MRI by iterative graph cuts. Magn Reson Med 2024; 92:1484-1495. [PMID: 38725423 DOI: 10.1002/mrm.30138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE To develop and evaluate a phase unwrapping method for cine phase contrast MRI based on graph cuts. METHODS A proposed Iterative Graph Cuts method was evaluated in 10 cardiac patients with two-dimensional flow quantification which was repeated at low venc settings to provoke wrapping. The images were also unwrapped by a path-following method (ROMEO), and a Laplacian-based method (LP). Net flow was quantified using semi-automatic vessel segmentation. High venc images were also wrapped retrospectively to asses the residual amount of wrapped voxels. RESULTS The absolute net flow error after unwrapping at venc = 100 cm/s was 1.8 mL, which was 0.83 mL smaller than for LP. The repeatability error at high venc without unwrapping was 2.5 mL. The error at venc = 50 cm/s was 7.5 mL, which was 8.2 mL smaller than for ROMEO and 5.7 mL smaller than for LP. For retrospectively wrapped images with synthetic venc of 100/50/25 cm/s, the residual amount of wrapped voxels was 0.00/0.12/0.79%, which was 0.09/0.26/8.0 percentage points smaller than for LP. With synthetic venc of 25 cm/s, omitting magnitude information resulted in 3.2 percentage points more wrapped voxels, and only spatial/temporal unwrapping resulted in 4.6/21 percentage points more wrapped voxels compared to spatiotemporal unwrapping. CONCLUSION Iterative Graph Cuts enables unwrapping of cine phase contrast MRI with very small errors, except for at extreme blood velocities, with equal or better performance compared to ROMEO and LP. The use of magnitude information and spatiotemporal unwrapping is recommended.
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Affiliation(s)
- Johan Berglund
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
- Molecular Imaging and Medical Physics, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Mio Liljeblad
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
| | - Tomasz Baron
- Cardiology and Clinical Physiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
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Zhang Q, Fotaki A, Ghadimi S, Wang Y, Doneva M, Wetzl J, Delfino JG, O'Regan DP, Prieto C, Epstein FH. Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation. J Cardiovasc Magn Reson 2024; 26:101051. [PMID: 38909656 PMCID: PMC11331970 DOI: 10.1016/j.jocmr.2024.101051] [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: 03/17/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. METHODS Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. RESULTS These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. CONCLUSIONS Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
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Affiliation(s)
- Qiang Zhang
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK.
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | | | - Jens Wetzl
- Siemens Healthineers AG, Erlangen, Germany.
| | - Jana G Delfino
- US Food and Drug Administration, Center for Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), Silver Spring, MD, USA.
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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Maroun A, Scott MB, Catania R, Berhane H, Jarvis K, Allen BD, Barker AJ, Markl M. Multiyear Interval Changes in Aortic Wall Shear Stress in Patients with Bicuspid Aortic Valve Assessed by 4D Flow MRI. J Magn Reson Imaging 2024. [PMID: 38426608 DOI: 10.1002/jmri.29305] [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: 10/11/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND In patients with bicuspid aortic valve (BAV), 4D flow MRI can quantify regions exposed to abnormal aortic hemodynamics, including high wall shear stress (WSS), a known stimulus for arterial wall dysfunction. However, the long-term multiscan reproducibility of 4D flow MRI-derived hemodynamic parameters is unknown. PURPOSE To investigate the long-term stability of 4D flow MRI-derived peak velocity, WSS, and WSS-derived heatmaps in patients with BAV undergoing multiyear surveillance imaging. STUDY TYPE Retrospective. POPULATION 20 BAV patients (mean age 48.4 ± 13.9 years; 14 males) with five 4D flow MRI scans, with intervals of at least 6 months between scans, and 125 controls (mean age: 50.7 ± 15.8 years; 67 males). FIELD STRENGTH/SEQUENCE 1.5 and 3.0T, prospectively ECG and respiratory navigator-gated aortic 4D flow MRI. ASSESSMENT Automated AI-based 4D flow analysis pipelines were used for data preprocessing, aorta 3D segmentation, and quantification of ascending aorta (AAo) peak velocity, peak systolic WSS, and heatmap-derived relative area of elevated WSS compared to WSS ranges in age and sex-matched normative control populations. Growth rate was derived from the maximum AAo diameters measured on the first and fifth MRI scans. STATISTICAL TESTS One-way repeated measures analysis of variance. P < 0.05 indicated significance. RESULTS One hundred 4D flow MRI exams (five per patient) were analyzed. The mean total follow-up duration was 5.5 ± 1.1 years, and the average growth rate was 0.3 ± 0.2 mm/year. Peak velocity, peak systolic WSS, and relative area of elevated WSS did not change significantly over the follow-up period (P = 0.64, P = 0.69, and P = 0.35, respectively). The patterns and areas of elevated WSS demonstrated good reproducibility on semiquantitative assessment. CONCLUSION 4D flow MRI-derived peak velocity, WSS, and WSS-derived heatmaps showed good multiyear and multiscan stability in BAV patients with low aortic growth rates. These findings underscore the reliability of these metrics in monitoring BAV patients for potential risk of dilation. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Anthony Maroun
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Michael B Scott
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Roberta Catania
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Haben Berhane
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Kelly Jarvis
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Bradley D Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Alex J Barker
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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Huang TL, Lu NH, Huang YH, Twan WH, Yeh LR, Liu KY, Chen TB. Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images. Sci Rep 2023; 13:21849. [PMID: 38071254 PMCID: PMC10710441 DOI: 10.1038/s41598-023-49159-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.
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Affiliation(s)
- Te-Li Huang
- Department of Radiology, Kaohsiung Veterans General Hospital, No. 386, Dazhong 1st Rd., Zuoying Dist., Kaohsiung, 81362, Taiwan
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan.
- Department of Pharmacy, Tajen University, No.20, Weixin Rd., Yanpu Township, Pingtung, 90741, Taiwan.
- Department of Radiology, E-DA Hospital, I-Shou University, No.1, Yida Rd., Jiao-Su Village, Yan-Chao District, Kaohsiung, 82445, Taiwan.
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan
| | - Wen-Hung Twan
- Department of Life Sciences, National Taitung University, No.369, Sec. 2, University Rd., Taitung, 95092, Taiwan
| | - Li-Ren Yeh
- Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, No.1, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Hospital, I-Shou University, No.1, Yida Rd., Jiao-Su Village, Yan-Chao District, Kaohsiung, 82445, Taiwan
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Rd., Jiaosu Village, Yanchao District, Kaohsiung, 82445, Taiwan.
- Institute of Statistics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu, 30010, Taiwan.
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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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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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Weiss EK, Jarvis K, Maroun A, Malaisrie SC, Mehta CK, McCarthy PM, Bonow RO, Avery RJ, Allen BD, Carr JC, Rigsby CK, Markl M. Systolic reverse flow derived from 4D flow cardiovascular magnetic resonance in bicuspid aortic valve is associated with aortic dilation and aortic valve stenosis: a cross sectional study in 655 subjects. J Cardiovasc Magn Reson 2023; 25:3. [PMID: 36698129 PMCID: PMC9878800 DOI: 10.1186/s12968-022-00906-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 12/04/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Bicuspid aortic valve (BAV) disease is associated with increased risk of aortopathy. In addition to current intervention guidelines, BAV mediated changes in aortic 3D hemodynamics have been considered as risk stratification measures. We aimed to evaluate the association of 4D flow cardiovascular magnetic resonance (CMR) derived voxel-wise aortic reverse flow with aortic dilation and to investigate the role of aortic valve regurgitation (AR) and stenosis (AS) on reverse flow in systole and diastole. METHODS 510 patients with BAV (52 ± 14 years) and 120 patients with trileaflet aortic valve (TAV) (61 ± 11 years) and mid-ascending aorta diameter (MAAD) > 35 mm who underwent CMR including 4D flow CMR were retrospectively included. An age and sex-matched healthy control cohort (n = 25, 49 ± 12 years) was selected. Voxel-wise reverse flow was calculated in the aorta and quantified by the mean reverse flow in the ascending aorta (AAo) during systole and diastole. RESULTS BAV patients without AS and AR demonstrated significantly increased systolic and diastolic reverse flow (222% and 13% increases respectively, p < 0.01) compared to healthy controls and also had significantly increased systolic reverse flow compared to TAV patients with aortic dilation (79% increase, p < 0.01). In patients with isolated AR, systolic and diastolic AAo reverse flow increased significantly with AR severity (c = - 83.2 and c = - 205.6, p < 0.001). In patients with isolated AS, AS severity was associated with an increase in both systolic (c = - 253.1, p < 0.001) and diastolic (c = - 87.0, p = 0.02) AAo reverse flow. Right and left/right and non-coronary fusion phenotype showed elevated systolic reverse flow (> 17% increase, p < 0.01). Right and non-coronary fusion phenotype showed decreased diastolic reverse flow (> 27% decrease, p < 0.01). MAAD was an independent predictor of systolic (p < 0.001), but not diastolic, reverse flow (p > 0.1). CONCLUSION 4D flow CMR derived reverse flow associated with BAV was successfully captured even in the absence of AR or AS and in comparison to TAV patients with aortic dilation. Diastolic AAo reverse flow increased with AR severity while AS severity strongly correlated with increased systolic reverse flow in the AAo. Additionally, increasing MAAD was independently associated with increasing systolic AAo reverse flow. Thus, systolic AAo reverse flow may be a valuable metric for evaluating disease severity in future longitudinal outcome studies.
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Affiliation(s)
- Elizabeth K. Weiss
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611 USA
| | - Kelly Jarvis
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611 USA
| | - Anthony Maroun
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611 USA
| | - S. Chris Malaisrie
- Division of Cardiac Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Christopher K. Mehta
- Division of Cardiac Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Patrick M. McCarthy
- Division of Cardiac Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Robert O. Bonow
- Division of Cardiology, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Ryan J. Avery
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611 USA
| | - Bradley D. Allen
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611 USA
| | - James C. Carr
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611 USA
| | - Cynthia K. Rigsby
- Department of Medical Imaging, Lurie Children’s Hospital, Chicago, IL USA
| | - Michael Markl
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 North Michigan Avenue Suite 1600, Chicago, IL 60611 USA
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Peper ES, van Ooij P, Jung B, Huber A, Gräni C, Bastiaansen JAM. Advances in machine learning applications for cardiovascular 4D flow MRI. Front Cardiovasc Med 2022; 9:1052068. [PMID: 36568555 PMCID: PMC9780299 DOI: 10.3389/fcvm.2022.1052068] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow.
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Affiliation(s)
- Eva S. Peper
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland,*Correspondence: Eva S. Peper,
| | - Pim van Ooij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands,Department of Pediatric Cardiology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bernd Jung
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Adrian Huber
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jessica A. M. Bastiaansen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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