Tricuspid valve flow measurement using a deep learning framework for automated valve‐tracking 2D phase contrast
Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the...
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| Published in | Magnetic resonance in medicine Vol. 92; no. 5; pp. 1838 - 1850 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
Wiley Subscription Services, Inc
01.11.2024
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| Abstract | Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve.
Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels.
The mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = -0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (-1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography.
Automated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge. |
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| AbstractList | Purpose: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve. Methods: Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels. Results: The mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = −0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (−1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography. Conclusion: Automated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge. Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve.PURPOSETricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve.Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels.METHODSNine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels.The mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = -0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (-1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography.RESULTSThe mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = -0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (-1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography.Automated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge.CONCLUSIONAutomated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge. Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve. Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels. The mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = -0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (-1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography. Automated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge. Purpose: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve-tracking 2D method for measuring flow through the dynamic tricuspid valve. Methods: Nine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long-axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D-PC scans acquired in a static slice localized at the end-systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels. Results: The mean tricuspid valve systolic excursion was 17.8± 2.5 mm. The 2D valve-tracking PC net diastolic flow showed excellent correlation with SV by right-ventricle planimetry (bias ± 1.96 SD = −0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (−1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right-ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin-systole. In one patient, valve-tracking PC displayed a high-velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography. Conclusion: Automated valve-tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge. PurposeTricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but they are vitally important to diastolic function evaluation. We developed an automated valve‐tracking 2D method for measuring flow through the dynamic tricuspid valve.MethodsNine healthy subjects and 2 patients were imaged. The approach uses a previously trained deep learning network, TVnet, to automatically track the tricuspid valve plane from long‐axis cine images. Subsequently, the tracking information is used to acquire 2D phase contrast (PC) with a dynamic (moving) acquisition plane that tracks the valve. Direct diastolic net flows evaluated from the dynamic PC sequence were compared with flows from 2D‐PC scans acquired in a static slice localized at the end‐systolic valve position, and also ventricular stroke volumes (SVs) using both planimetry and 2D PC of the great vessels.ResultsThe mean tricuspid valve systolic excursion was 17.8 ± 2.5 mm. The 2D valve‐tracking PC net diastolic flow showed excellent correlation with SV by right‐ventricle planimetry (bias ± 1.96 SD = −0.2 ± 10.4 mL, intraclass correlation coefficient [ICC] = 0.92) and aortic PC (−1.0 ± 13.8 mL, ICC = 0.87). In comparison, static tricuspid valve 2D PC also showed a strong correlation but had greater bias (p = 0.01) versus the right‐ventricle SV (10.6 ± 16.1 mL, ICC = 0.61). In most (8 of 9) healthy subjects, trace regurgitation was measured at begin‐systole. In one patient, valve‐tracking PC displayed a high‐velocity jet (380 cm/s) with maximal velocity agreeing with echocardiography.ConclusionAutomated valve‐tracking 2D PC is a feasible route toward evaluation of tricuspid regurgitant velocities, potentially solving a major clinical challenge. |
| Author | Lamy, Jérôme Steele, Jeremy Heiberg, Einar Huber, Steffen Seemann, Felicia Xiang, Jie Gonzales, Ricardo A. Wieben, Oliver Peters, Dana C. |
| Author_xml | – sequence: 1 givenname: Jérôme orcidid: 0000-0003-1931-2971 surname: Lamy fullname: Lamy, Jérôme organization: Department of Radiology and Biomedical Imaging Yale University New Haven Connecticut USA, Sorbonne Université, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB Paris France – sequence: 2 givenname: Ricardo A. orcidid: 0000-0002-9384-4602 surname: Gonzales fullname: Gonzales, Ricardo A. organization: Oxford Center for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine University of Oxford Oxford UK – sequence: 3 givenname: Jie orcidid: 0000-0003-1165-749X surname: Xiang fullname: Xiang, Jie organization: Department of Radiology and Biomedical Imaging Yale University New Haven Connecticut USA – sequence: 4 givenname: Felicia orcidid: 0000-0003-3074-5380 surname: Seemann fullname: Seemann, Felicia organization: Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute National Institutes of Health Bethesda Maryland USA – sequence: 5 givenname: Steffen surname: Huber fullname: Huber, Steffen organization: Department of Radiology and Biomedical Imaging Yale University New Haven Connecticut USA – sequence: 6 givenname: Jeremy surname: Steele fullname: Steele, Jeremy organization: Department of Radiology and Biomedical Imaging Yale University New Haven Connecticut USA – sequence: 7 givenname: Oliver surname: Wieben fullname: Wieben, Oliver organization: Department of Medical Physics University of Wisconsin Madison Wisconsin USA – sequence: 8 givenname: Einar surname: Heiberg fullname: Heiberg, Einar organization: Department of Clinical Sciences Lund University Lund Sweden – sequence: 9 givenname: Dana C. orcidid: 0000-0001-7556-4642 surname: Peters fullname: Peters, Dana C. organization: Department of Radiology and Biomedical Imaging Yale University New Haven Connecticut USA |
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| Cites_doi | 10.1136/bmjopen-2019-033084 10.1016/0730-725X(87)90402-4 10.1186/1471-2342-10-1 10.1016/j.echo.2016.01.011 10.1186/s12968-021-00824-2 10.1148/radiol.2492080146 10.1007/s10554-015-0715-x 10.1016/j.jacc.2017.12.009 10.1002/(SICI)1522-2594(199911)42:5<970::AID-MRM18>3.0.CO;2-I 10.1002/jmri.1159 10.1007/978-3-030-87231-1_55 10.1186/s12968-021-00783-8 10.4250/jcu.2016.24.2.144 10.1186/s12968-017-0426-7 10.1186/s12968-015-0174-5 10.1016/j.jacc.2014.12.047 10.1002/jmri.1880070410 10.1186/s12968-020-00612-4 10.1016/j.jcmg.2018.07.033 10.1161/JAHA.118.009362 10.1002/jmri.26971 10.1186/s12880-017-0189-5 10.1002/mrm.10171 10.1148/radiol.2018180807 10.1016/j.jcmg.2019.01.006 10.1186/s12968-020-00610-6 10.1002/jmri.26040 10.1016/j.compbiomed.2017.11.015 10.1136/openhrt-2020-001323 10.1016/j.jcmg.2020.01.008 10.1002/jmri.24578 10.1002/mrm.29082 10.1109/ISBI52829.2022.9761595 10.1097/RLI.0b013e3181ae99b5 10.1093/ehjci/jeac141.019 10.1161/CIRCIMAGING.116.005207 10.1378/chest.08-0277 10.1136/hrt.2010.212084 10.1080/10255842.2014.931055 |
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| Keywords | deep learning tricuspid valve phase contrast slice‐following regurgitation |
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| References | e_1_2_8_28_1 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_2_1 e_1_2_8_5_1 Otto CM (e_1_2_8_3_1) 2021; 143 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_41_1 e_1_2_8_40_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_30_1 |
| References_xml | – ident: e_1_2_8_10_1 doi: 10.1136/bmjopen-2019-033084 – ident: e_1_2_8_29_1 doi: 10.1016/0730-725X(87)90402-4 – ident: e_1_2_8_30_1 doi: 10.1186/1471-2342-10-1 – volume: 143 start-page: e72 year: 2021 ident: e_1_2_8_3_1 article-title: 2020 ACC/AHA guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines publication-title: Circulation. – ident: e_1_2_8_8_1 doi: 10.1016/j.echo.2016.01.011 – ident: e_1_2_8_41_1 doi: 10.1186/s12968-021-00824-2 – ident: e_1_2_8_36_1 doi: 10.1148/radiol.2492080146 – ident: e_1_2_8_39_1 doi: 10.1007/s10554-015-0715-x – ident: e_1_2_8_4_1 doi: 10.1016/j.jacc.2017.12.009 – ident: e_1_2_8_21_1 doi: 10.1002/(SICI)1522-2594(199911)42:5<970::AID-MRM18>3.0.CO;2-I – ident: e_1_2_8_22_1 doi: 10.1002/jmri.1159 – ident: e_1_2_8_25_1 doi: 10.1007/978-3-030-87231-1_55 – ident: e_1_2_8_17_1 doi: 10.1186/s12968-021-00783-8 – ident: e_1_2_8_35_1 doi: 10.4250/jcu.2016.24.2.144 – ident: e_1_2_8_11_1 doi: 10.1186/s12968-017-0426-7 – ident: e_1_2_8_37_1 doi: 10.1186/s12968-015-0174-5 – ident: e_1_2_8_5_1 doi: 10.1016/j.jacc.2014.12.047 – ident: e_1_2_8_32_1 doi: 10.1002/jmri.1880070410 – ident: e_1_2_8_15_1 doi: 10.1186/s12968-020-00612-4 – ident: e_1_2_8_18_1 doi: 10.1016/j.jcmg.2018.07.033 – ident: e_1_2_8_20_1 doi: 10.1161/JAHA.118.009362 – ident: e_1_2_8_24_1 doi: 10.1002/jmri.26971 – ident: e_1_2_8_23_1 doi: 10.1186/s12880-017-0189-5 – ident: e_1_2_8_28_1 doi: 10.1002/mrm.10171 – ident: e_1_2_8_14_1 doi: 10.1148/radiol.2018180807 – ident: e_1_2_8_27_1 doi: 10.1016/j.jcmg.2019.01.006 – ident: e_1_2_8_31_1 doi: 10.1186/s12968-020-00610-6 – ident: e_1_2_8_13_1 doi: 10.1002/jmri.26040 – ident: e_1_2_8_34_1 doi: 10.1016/j.compbiomed.2017.11.015 – ident: e_1_2_8_7_1 doi: 10.1136/openhrt-2020-001323 – ident: e_1_2_8_6_1 doi: 10.1016/j.jcmg.2020.01.008 – ident: e_1_2_8_16_1 doi: 10.1002/jmri.24578 – ident: e_1_2_8_38_1 doi: 10.1002/mrm.29082 – ident: e_1_2_8_40_1 doi: 10.1109/ISBI52829.2022.9761595 – ident: e_1_2_8_12_1 doi: 10.1097/RLI.0b013e3181ae99b5 – ident: e_1_2_8_26_1 doi: 10.1093/ehjci/jeac141.019 – ident: e_1_2_8_19_1 doi: 10.1161/CIRCIMAGING.116.005207 – ident: e_1_2_8_2_1 doi: 10.1378/chest.08-0277 – ident: e_1_2_8_9_1 doi: 10.1136/hrt.2010.212084 – ident: e_1_2_8_33_1 doi: 10.1080/10255842.2014.931055 |
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| Snippet | Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow evaluation, but... PurposeTricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow... Purpose: Tricuspid valve flow velocities are challenging to measure with cardiovascular MR, as the rapidly moving valvular plane prohibits direct flow... |
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| SubjectTerms | Adult Algorithms Aorta Automation Bias Blood Flow Velocity Correlation coefficients Deep Learning Diastole Echocardiography Engineering and Technology Female Flow measurement Flow velocity Humans Image acquisition Image contrast Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging, Cine - methods Male Measurement methods Medical Engineering Medical Image Processing Medical Imaging Medicinsk bildbehandling Medicinsk bildvetenskap Medicinteknik Middle Aged Phase contrast Regurgitation Reproducibility of Results slice-following Stroke Volume - physiology Systole Systole - physiology Teknik Tracking Tricuspid valve Tricuspid Valve - diagnostic imaging Two dimensional flow Velocity Ventricle |
| Title | Tricuspid valve flow measurement using a deep learning framework for automated valve‐tracking 2D phase contrast |
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