Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Cardiovascular Magnetic Resonance
Published in: Journal of Cardiovascular Magnetic Resonance 1/2019

Open Access 01-12-2019 | Research

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

Authors: Ahmed S. Fahmy, Hossam El-Rewaidy, Maryam Nezafat, Shiro Nakamori, Reza Nezafat

Published in: Journal of Cardiovascular Magnetic Resonance | Issue 1/2019

Login to get access

Abstract

Background

Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping.

Methods

A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers.

Results

The FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses).

Conclusion

The proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis.
Appendix
Available only for authorised users
Literature
1.
Messroghli DR, Radjenovic A, Kozerke S, Higgins DM, Sivananthan MU, Ridgway JP. Modified look-locker inversion recovery (MOLLI) for high-resolution T1 mapping of the heart. Magn Reson Med. 2004;52(1):141–6.CrossRef
2.
Piechnik SK, Ferreira VM, Dall’Armellina E, Cochlin LE, Greiser A, Neubauer S, et al. Shortened modified look-locker inversion recovery (ShMOLLI) for clinical myocardial T1-mapping at 1.5 and 3 T within a 9 heartbeat breathhold. J Cardiovasc Magn Reson. 2010;12(1):69.CrossRef
3.
Chow K, Flewitt JA, Green JD, Pagano JJ, Friedrich MG, Thompson RB. Saturation recovery single-shot acquisition (SASHA) for myocardial T1 mapping. Magn Reson Med. 2014;71(6):2082–95.CrossRef
4.
Roujol S, Weingärtner S, Foppa M, Chow K, Kawaji K, Ngo LH, et al. Accuracy, precision, and reproducibility of four T1 mapping sequences: a head-to-head comparison of MOLLI, ShMOLLI, SASHA, and SAPPHIRE. Radiology. 2014;272(3):683–9.CrossRef
5.
Weingärtner S, Roujol S, Akçakaya M, Basha TA, Nezafat R. Free-breathing multislice native myocardial T1 mapping using the slice-interleaved T1 (STONE) sequence. Magn Reson Med. 2015;74(1):115–24.CrossRef
6.
Messroghli DR, Moon JC, Ferreira VM, Grosse-Wortmann L, He T, Kellman P, et al. Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: a consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imagi. J Cardiovasc Magn Reson. 2017;19(1):75.CrossRef
7.
Sibley CT, Noureldin RA, Gai N, Nacif MS, Liu S, Turkbey EB, et al. T1 mapping in cardiomyopathy at cardiac MR: comparison with endomyocardial biopsy. Radiology. 2012;265(3):724–32.CrossRef
8.
Puntmann VO, Carr-White G, Jabbour A, Yu C-Y, Gebker R, Kelle S, et al. T1-mapping and outcome in nonischemic cardiomyopathy. JACC Cardiovasc Imaging. 2016;9(1):40–50.CrossRef
9.
Akçakaya M, Weingärtner S, Roujol S, Nezafat R. On the selection of sampling points for myocardial T1 mapping. Magn Reson Med. 2015;73(5):1741–53.CrossRef
10.
Ferreira VM, Wijesurendra RS, Liu A, Greiser A, Casadei B, Robson MD, et al. Systolic ShMOLLI myocardial T1-mapping for improved robustness to partial-volume effects and applications in tachyarrhythmias. J Cardiovasc Magn Reson. 2015;17(1):77.CrossRef
11.
Jyun-Ming T, Teng-Yi H, Yu-Shen T, Yi-Ru L. Free-breathing MOLLI: application to myocardial T1 mapping. Med Phys. 2012;39(12):7291–302.CrossRef
12.
Xue H, Shah S, Greiser A, Guetter C, Littmann A, Jolly M-P, et al. Motion correction for myocardial T1 mapping using image registration with synthetic image estimation. Magn Reson Med. 2012;67(6):1644–55.CrossRef
13.
Roujol S, Foppa M, Weingärtner S, Manning WJ, Nezafat R. Adaptive registration of varying contrast-weighted images for improved tissue characterization (ARCTIC): application to T1 mapping. Magn Reson Med. 2015;73(4):1469–82.CrossRef
14.
El-Rewaidy H, Nezafat M, Jang J, Nakamori S, Fahmy AS, Nezafat R. Nonrigid active shape model-based registration framework for motion correction of cardiac T1 mapping. Magn Reson Med. 2018;80(2):780–91.CrossRef
15.
Bellm S, Basha TA, Shah RV, Murthy VL, Liew C, Tang M, et al. Reproducibility of myocardial T1 and T2 relaxation time measurement using slice-interleaved T1 and T2 mapping sequences. J Magn Reson Imaging. 2016;44(5):1159–67.CrossRef
16.
Moon JC, Messroghli DR, Kellman P, Piechnik SK, Robson MD, Ugander M, et al. Myocardial T1 mapping and extracellular volume quantification: a Society for Cardiovascular Magnetic Resonance (SCMR) and CMR working Group of the European Society of cardiology consensus statement. J Cardiovasc Magn Reson. 2013;15(1):92.CrossRef
17.
Liu F, Zhou Z, Jang H, Samsonov A, Zhao G, Kijowski R. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med. 2017;79(4):2379–91.CrossRef
18.
Tan LK, Liew YM, Lim E, McLaughlin RA. Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences. Med Image Anal. 2017;39:78–86.CrossRef
19.
Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. 2016;30:108–19.CrossRef
20.
Schnell S, Entezari P, Mahadewia RJ, Malaisrie SC, McCarthy PM, Collins JD, et al. Improved semi-automated 4D-flow MRI analysis in the aorta in patients with congenital aortic anomalies vs tricuspid aortic valves. J Comput Assist Tomogr. 2016;40(1):102–8.CrossRef
21.
Goel A, McColl R, King KS, Whittemore A, Peshock RMA. Fully automated tool to identify the aorta and compute flow using phase-contrast MRI: validation and application in a large population based study. J Magn Reson Imaging. 2014;40(1):221–8.CrossRef
22.
Yang X, Zeng Z, Yi S. Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images. IET Comput Vis. 2017;11(8):643–9.CrossRef
23.
Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal. 2017;35:159–71.CrossRef
24.
Tran PV. A fully convolutional neural network for cardiac segmentation in short-Axis MRI. ArXiv: 1604.00494. 2016;
25.
Avendi MR, Kheradvar A, Jafarkhani H. Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magn Reson Med. 2017;78(6):2439–48.CrossRef
26.
27.
Kayalibay B, Jensen G, van der Smagt P. CNN-based segmentation of medical imaging data. ArXiv: 1701.03056. 2017
28.
Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.CrossRef
29.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention -- MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, vol. 3. Cham: Springer International Publishing; 2015. p. 234–41.CrossRef
30.
Ioffe S, Szegedy C. Batch Normalization: Accelerating deep network training by reducing internal covariate shift. ArXiv:1502.03167. 2015;
31.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15:1929–58.
32.
Kingma DP, Ba J. Adam: a method for stochastic optimization. In: Proceedings of international conference on learning representations. 2015.
33.
Krogh A, Hertz JA. Simple weight decay can improve generalization. In: Advances in neural information processing systems (NIPS)-Volume 4. USA: Morgan-Kaufmann; 1992. p. 950–7.
34.
Maragos P, Schafer R. Morphological skeleton representation and coding of binary images. IEEE Trans Acoust. 1986;34(5):1228–44.CrossRef
35.
Bengio Y. Deep Learning of Representations for Unsupervised and Transfer Learning. In: Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, vol. 27; 2011. p. 17–37.
36.
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35:1285–98.CrossRef
37.
Hussain Z, Gimenez F, Yi D, Rubin D. Differential data augmentation techniques for medical imaging classification tasks. AMIA Annu Symp Proc. 2017;2017:979–84.PubMed
38.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: proceedings of the 25th international conference on neural information processing systems, vol. 1. USA: Curran Associates Inc; 2012. p. 1097–105.
39.
Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, et al. Statistical validation of image segmentation quality based on a spatial overlap index1. Acad Radiol. 2004;11(2):178–89.CrossRef
40.
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging. 2016;35(5):1240–51.CrossRef
41.
Roth HR, Lu L, Liu J, Yao J, Seff A, Cherry K, et al. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging. 2016;35(5):1170–81.CrossRef
42.
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging. 2016;35:1299–312.CrossRef
43.
Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, et al. Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging. 2018;37(2):384–95.CrossRef
44.
Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, et al. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J Cardiovasc Magn Reson. 2018;20:65.CrossRef
45.
Vigneault DM, Xie W, Ho CY, Bluemke DA, Noble JA. Ω-net (omega-net): fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks. Med Image Anal. 2018;48:95–106.CrossRef
46.
Gupta SN, Solaiyappan M, Beache GM, Arai AE, Foo TKF. Fast method for correcting image misregistration due to organ motion in time-series MRI data. Magn Reson Med. 2003;49(3):506–14.CrossRef
47.
Ma C, Varghese T. Lagrangian displacement tracking using a polar grid between endocardial and epicardial contours for cardiac strain imaging. Med Phys. 2012;39(4):1779–92.CrossRef
48.
Ma C, Wang X, Varghese T. Segmental analysis of cardiac short-Axis views using Lagrangian radial and circumferential strain. Ultrason Imaging. 2016;38(6):363–83.CrossRef
49.
Lee H-Y, Codella N, Cham M, Prince M, Weinsaft J, Wang Y. Left ventricle segmentation using Graph searching on Intensity and Gradient and A priori knowledge (lvGIGA) for short axis cardiac MRI. J Magn Reson Imaging. 2008;28(6):1393–401.CrossRef
50.
Childs H, Ma L, Ma M, Clarke J, Cocker M, Green J, et al. Comparison of long and short axis quantification of left ventricular volume parameters by cardiovascular magnetic resonance, with ex-vivo validation. J Cardiovasc Magn Reson. 2011;13(1):40.CrossRef
Metadata
Title
Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks
Authors
Ahmed S. Fahmy
Hossam El-Rewaidy
Maryam Nezafat
Shiro Nakamori
Reza Nezafat
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Journal of Cardiovascular Magnetic Resonance / Issue 1/2019
Electronic ISSN: 1532-429X
DOI
https://doi.org/10.1186/s12968-018-0516-1

Other articles of this Issue 1/2019

Journal of Cardiovascular Magnetic Resonance 1/2019 Go to the issue

Technical notes

Multipoint 5D flow cardiovascular magnetic resonance - accelerated cardiac- and respiratory-motion resolved mapping of mean and turbulent velocities

Review

Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Research

Multiparametric cardiovascular magnetic resonance imaging in acute myocarditis: a comparison of different measurement approaches

Research

Stress increases intracardiac 4D flow cardiovascular magnetic resonance -derived energetics and vorticity and relates to VO2max in Fontan patients

Research

Diagnostic performance of cardiovascular magnetic resonance native T1 and T2 mapping in pediatric patients with acute myocarditis

Technical notes

Fetal XCMR: a numerical phantom for fetal cardiovascular magnetic resonance imaging