Abstract
Mitral regurgitation (MR) is the most common heart valve disease. Prolonged regurgitation can cause changes in the heart size, lead to impaired systolic and diastolic capacity, and even threaten life. In clinical practice, MR is evaluated by the proximal isovelocity surface area (PISA) method, where manual measurements of the regurgitation velocity and the value of PISA radius from multiple ultrasound images are required to obtain the mitral regurgitant stroke volume (MRSV) and effective regurgitant orifice area (EROA). In this paper, we propose a fully automatic method for MR quantification, which follows the pipeline of ECG-based cycle detection, Doppler spectrum segmentation, PISA radius segmentation, and MR quantification. Specifically, for the Doppler spectrum segmentation, we proposed a novel adaptive-weighting multi-channel segmentation network, PISA-net, to accurately identify the upper and lower contours of the PISA radius from a pair of coupled M-mode PISA image and corresponding M-mode decolored image. Using the complementary information of the two coupled images and combing with the spatial attention module, the proposed PISA-net can well identify the contours of the PISA radius and therefore lead to accurate quantification of MR parameters. To the best of our knowledge, this is the first study of automatic MR quantification. Experimental results demonstrated the effectiveness of the whole pipeline, especially the PISA-net for PISA radius segmentation. The full method achieves a high Pearson correlation of 0.994 for both MRSV and EROA, implying its great potential in the clinical application of MR diagnosis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bargiggia, G.S., et al.: A new method for quantitation of mitral regurgitation based on color flow doppler imaging of flow convergence proximal to regurgitant orifice. Circulation 84, 1481–1489 (1991)
Chen, C., et al.: Noninvasive estimation of regurgitant flow rate and volume in patients with mitral regurgitation by doppler color mapping of accelerating flow field. J. Am. Coll. Cardiol. 21(2), 374–83 (1993)
Dujardin, K.S., Enriquez-Sarano, M., Bailey, K.R., Nishimura, R.A., Seward, J.B., Tajik, A.J.: Grading of mitral regurgitation by quantitative doppler echocardiography: calibration by left ventricular angiography in routine clinical practice. Circulation 96(10), 3409–15 (1997)
Enriquez-Sarano, M., Miller, F.A.J., Hayes, S.N., Bailey, K.R., Tajik, A.J., Seward, J.B.: Effective mitral regurgitant orifice area: clinical use and pitfalls of the proximal isovelocity surface area method. J. Am. Coll. Cardiol. 25(3), 703–9 (1995)
Enriquez-Sarano, M., Sinak, L.J., Tajik, A.J., Bailey, K.R., Seward, J.B.: Changes in effective regurgitant orifice throughout systole in patients with mitral valve prolapse. a clinical study using the proximal isovelocity surface area method. Circulation 92(10), 2951–2958 (1995)
Giesler, M., et al.: Color doppler echocardiographic determination of mitral regurgitant flow from the proximal velocity profile of the flow convergence region. Am. J. Cardiol. 71(2), 217–24 (1993)
Greenspan, H., Shechner, O., Scheinowitz, M., Feinberg, M.: Doppler echocardiography flow-velocity image analysis for patients with atrial fibrillation. Ultrasound Med. Biol. 31(8), 1031–40 (2005)
Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2011–2023 (2017)
Hung, J.W., Otsuji, Y., Handschumacher, M.D., Schwammenthal, E., Levine, R.A.: Mechanism of dynamic regurgitant orifice area variation in functional mitral regurgitation: physiologic insights from the proximal flow convergence technique. J. Am. Coll. Cardiol. 33(2), 538–45 (1999)
Liang, J., et al.: Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis. Med. Image Anal. 79, 102461 (2022)
Militaru, S., et al.: Validation of semiautomated quantification of mitral valve regurgitation by three-dimensional color doppler transesophageal echocardiography. J. Am. Soc. Echocardiogr. 33(3), 342–354 (2020). https://doi.org/10.1016/j.echo.2019.10.013, https://www.sciencedirect.com/science/article/pii/S0894731719311150
Nkomo, V.T., Gardin, J.M., Skelton, T.N., Gottdiener, J.S., Scott, C.G., Enriquez-Sarano, M.: Burden of valvular heart diseases: a population-based study. Lancet 368, 1005–1011 (2006)
Oktay, O., et al.: Attention U-Net: Learning where to look for the pancreas. ArXiv abs/1804.03999 (2018)
Schwammenthal, E., Chen, C., Benning, F., Block, M., Breithardt, G., Levine, R.A.: Dynamics of mitral regurgitant flow and orifice area: physiologic application of the proximal flow convergence method: Clinical data and experimental testing. Circulation 90, 307–322 (1994)
Singh, A., et al.: A novel approach for semiautomated three-dimensional quantification of mitral regurgitant volume reflects a more physiologic approach to mitral regurgitation. J. Am. Soc. Echocardiogr. 35(9), 940–946 (2022). https://doi.org/10.1016/j.echo.2022.05.005, https://www.sciencedirect.com/science/article/pii/S089473172200253X
Sun, H.L., Wu, T.J., Ng, C.C., Chien, C.C., Huang, C.C., Chie, W.C.: Efficacy of oropharyngeal lidocaine instillation on hemodynamic responses to orotracheal intubation. J. Clin. Anesth. 21(2), 103–7 (2009)
Tschirren, J., Lauer, R.M., Sonka, M.: Automated analysis of doppler ultrasound velocity flow diagrams. IEEE Trans. Med. Imaging 20, 1422–1425 (2001)
Vandervoort, P.M., et al.: Application of color doppler flow mapping to calculate effective regurgitant orifice area. an in vitro study and initial clinical observations. Circulation 88(3), 1150–1156 (1993)
Wang, Z.W., Slabaugh, G.G., Zhou, M., Fang, T.: Automatic tracing of blood flow velocity in pulsed doppler images. In: 2008 IEEE International Conference on Automation Science and Engineering, pp. 218–222 (2008)
Yamachika, S., et al.: Usefulness of color doppler proximal isovelocity surface area method in quantitating valvular regurgitation. J. Am. Soc. Echocardiogr. 10(2), 159–168 (1997). https://doi.org/10.1016/S0894-7317(97)70089-0, https://www.sciencedirect.com/science/article/pii/S0894731797700890
Zhou, S.K., et al.: A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Acknowledgement
The work is partially supported by the Natural Science Foundation of China (62171290), the Shenzhen Science and Technology Program (20220810145705001, JCYJ20190808115419619, SGDX20201103095613036), Medical Scientific Research Foundation of Guangdong Province (No. A2021370).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, K. et al. (2023). Mitral Regurgitation Quantification from Multi-channel Ultrasound Images via Deep Learning. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-43987-2_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43986-5
Online ISBN: 978-3-031-43987-2
eBook Packages: Computer ScienceComputer Science (R0)
