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Bhalodiya JM, Palit A, Tiwari MK, Prasad SK, Bhudia SK, Arvanitis TN, Williams MA. A Novel Hierarchical Template Matching Model for Cardiac Motion Estimation. Sci Rep 2018. [PMID: 29540762 PMCID: PMC5852007 DOI: 10.1038/s41598-018-22543-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Cardiovascular disease diagnosis and prognosis can be improved by measuring patient-specific in-vivo local myocardial strain using Magnetic Resonance Imaging. Local myocardial strain can be determined by tracking the movement of sample muscles points during cardiac cycle using cardiac motion estimation model. The tracking accuracy of the benchmark Free Form Deformation (FFD) model is greatly affected due to its dependency on tunable parameters and regularisation function. Therefore, Hierarchical Template Matching (HTM) model, which is independent of tunable parameters, regularisation function, and image-specific features, is proposed in this article. HTM has dense and uniform points correspondence that provides HTM with the ability to estimate local muscular deformation with a promising accuracy of less than half a millimetre of cardiac wall muscle. As a result, the muscles tracking accuracy has been significantly (p < 0.001) improved (30%) compared to the benchmark model. Such merits of HTM provide reliably calculated clinical measures which can be incorporated into the decision-making process of cardiac disease diagnosis and prognosis.
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Affiliation(s)
- Jayendra M Bhalodiya
- Warwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom.
| | - Arnab Palit
- Warwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom
| | - Manoj K Tiwari
- Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Sanjay K Prasad
- Royal Brompton and Harefield NHS Foundation Trust, London, United Kingdom
| | - Sunil K Bhudia
- Royal Brompton and Harefield NHS Foundation Trust, London, United Kingdom
| | - Theodoros N Arvanitis
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom
| | - Mark A Williams
- Warwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom
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Accurate harmonic phase tracking of tagged MRI using locally-uniform myocardium displacement constraint. Med Eng Phys 2016; 38:1305-1313. [DOI: 10.1016/j.medengphy.2016.08.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 07/24/2016] [Accepted: 08/07/2016] [Indexed: 01/23/2023]
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Sun W, Poot DHJ, Smal I, Yang X, Niessen WJ, Klein S. Stochastic optimization with randomized smoothing for image registration. Med Image Anal 2016; 35:146-158. [PMID: 27423112 DOI: 10.1016/j.media.2016.07.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Revised: 04/28/2016] [Accepted: 07/01/2016] [Indexed: 10/21/2022]
Abstract
Image registration is typically formulated as an optimization process, which aims to find the optimal transformation parameters of a given transformation model by minimizing a cost function. Local minima may exist in the optimization landscape, which could hamper the optimization process. To eliminate local minima, smoothing the cost function would be desirable. In this paper, we investigate the use of a randomized smoothing (RS) technique for stochastic gradient descent (SGD) optimization, to effectively smooth the cost function. In this approach, Gaussian noise is added to the transformation parameters prior to computing the cost function gradient in each iteration of the SGD optimizer. The approach is suitable for both rigid and nonrigid registrations. Experiments on synthetic images, cell images, public CT lung data, and public MR brain data demonstrate the effectiveness of the novel RS technique in terms of registration accuracy and robustness.
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Affiliation(s)
- Wei Sun
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA.
| | - Dirk H J Poot
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Department of Image Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Ihor Smal
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
| | - Xuan Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Wiro J Niessen
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Department of Image Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
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Liu H, Yan M, Song E, Wang J, Wang Q, Jin R, Jin L, Hung CC. Myocardial motion estimation of tagged cardiac magnetic resonance images using tag motion constraints and multi-level b-splines interpolation. Magn Reson Imaging 2015; 34:579-95. [PMID: 26712656 DOI: 10.1016/j.mri.2015.12.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 12/14/2015] [Indexed: 11/30/2022]
Abstract
Myocardial motion estimation of tagged cardiac magnetic resonance (TCMR) images is of great significance in clinical diagnosis and the treatment of heart disease. Currently, the harmonic phase analysis method (HARP) and the local sine-wave modeling method (SinMod) have been proven as two state-of-the-art motion estimation methods for TCMR images, since they can directly obtain the inter-frame motion displacement vector field (MDVF) with high accuracy and fast speed. By comparison, SinMod has better performance over HARP in terms of displacement detection, noise and artifacts reduction. However, the SinMod method has some drawbacks: 1) it is unable to estimate local displacements larger than half of the tag spacing; 2) it has observable errors in tracking of tag motion; and 3) the estimated MDVF usually has large local errors. To overcome these problems, we present a novel motion estimation method in this study. The proposed method tracks the motion of tags and then estimates the dense MDVF by using the interpolation. In this new method, a parameter estimation procedure for global motion is applied to match tag intersections between different frames, ensuring specific kinds of large displacements being correctly estimated. In addition, a strategy of tag motion constraints is applied to eliminate most of errors produced by inter-frame tracking of tags and the multi-level b-splines approximation algorithm is utilized, so as to enhance the local continuity and accuracy of the final MDVF. In the estimation of the motion displacement, our proposed method can obtain a more accurate MDVF compared with the SinMod method and our method can overcome the drawbacks of the SinMod method. However, the motion estimation accuracy of our method depends on the accuracy of tag lines detection and our method has a higher time complexity.
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Affiliation(s)
- Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key Laboratory of Education ministry for Image Processing and Intelligence Control, Wuhan, Hubei, China.
| | - Meng Yan
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key Laboratory of Education ministry for Image Processing and Intelligence Control, Wuhan, Hubei, China; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis and Treatment.
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key Laboratory of Education ministry for Image Processing and Intelligence Control, Wuhan, Hubei, China.
| | - Jie Wang
- State Grid Hubei Electric Power Research Institute, Wuhan, Hubei, China.
| | - Qian Wang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, Hubei, China.
| | - Renchao Jin
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key Laboratory of Education ministry for Image Processing and Intelligence Control, Wuhan, Hubei, China.
| | - Lianghai Jin
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key Laboratory of Education ministry for Image Processing and Intelligence Control, Wuhan, Hubei, China.
| | - Chih-Cheng Hung
- Center for Machine Vision and Security Research, Kennesaw State University, Marietta, GA, USA; Sino-US Intelligent Information Processing Joint Laboratory, Anyang Normal University, Anyang, Henan, China.
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Eldeeb SM, Khalifa AM, Fahmy AS. Hybrid intensity- and phase-based optical flow tracking of tagged MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:1059-62. [PMID: 25570144 DOI: 10.1109/embc.2014.6943776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Accurate tracking of the myocardium tissues in tagged Magnetic Resonance Images (MRI) is essential for evaluating the cardiac function. Current tracking methods utilize either the image intensity or the image phase as landmarks that can be tracked. In either case, the performance is vulnerable to the image quality and the fading of the tag lines. In this work, we propose a hybrid optical flow tracking method that combines both the intensity and the phase features of the image. The method is validated using numerical cardiac phantom as well as real MRI data experiments. Both experiments showed that the proposed method outperforms current intensity-based optical flow tracking and the phase-based HARP method with maximum error of 1 pixel at extreme conditions of tag fading.
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Ardekani S, Gunter G, Jain S, Weiss RG, Miller MI, Younes L. Estimating dense cardiac 3D motion using sparse 2D tagged MRI cross-sections. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:5101-4. [PMID: 25571140 DOI: 10.1109/embc.2014.6944772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this work, we describe a new method, an extension of the Large Deformation Diffeomorphic Metric Mapping to estimate three-dimensional deformation of tagged Magnetic Resonance Imaging Data. Our approach relies on performing non-rigid registration of tag planes that were constructed from set of initial reference short axis tag grids to a set of deformed tag curves. We validated our algorithm using in-vivo tagged images of normal mice. The mapping allows us to compute root mean square distance error between simulated tag curves in a set of long axis image planes and the acquired tag curves in the same plane. Average RMS error was 0.31 ± 0.36(SD) mm, which is approximately 2.5 voxels, indicating good matching accuracy.
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Trans-dimensional MCMC methods for fully automatic motion analysis in tagged MRI. ACTA ACUST UNITED AC 2011. [PMID: 22003664 DOI: 10.1007/978-3-642-23623-5_72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method allowing quantitative analysis of regional heart dynamics. Its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we propose a novel probabilistic method for tag tracking, implemented by means of Bayesian particle filtering and a trans-dimensional Markov chain Monte Carlo (MCMC) approach, which efficiently combines information about the imaging process and tag appearance with prior knowledge about the heart dynamics obtained by means of non-rigid image registration. Experiments using synthetic image data (with ground truth) and real data (with expert manual annotation) from preclinical (small animal) and clinical (human) studies confirm that the proposed method yields higher consistency, accuracy, and intrinsic tag reliability assessment in comparison with other frequently used tag tracking methods.
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Smal I, Carranza-Herrezuelo N, Klein S, Wielopolski P, Moelker A, Springeling T, Bernsen M, Niessen W, Meijering E. Reversible jump MCMC methods for fully automatic motion analysis in tagged MRI. Med Image Anal 2011; 16:301-24. [PMID: 21963294 DOI: 10.1016/j.media.2011.08.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 08/03/2011] [Accepted: 08/22/2011] [Indexed: 11/18/2022]
Abstract
Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method for studying regional heart dynamics. It offers great potential for quantitative analysis of a variety of kine(ma)tic parameters, but its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we evaluate the performance of four frequently used concepts found in the literature (optical flow, harmonic phase (HARP) magnetic resonance imaging, active contour fitting, and non-rigid image registration) for cardiac motion analysis in 2D tMRI image sequences, using both synthetic image data (with ground truth) and real data from preclinical (small animal) and clinical (human) studies. In addition we propose a new probabilistic method for tag tracking that serves as a complementary step to existing methods. The new method is based on a Bayesian estimation framework, implemented by means of reversible jump Markov chain Monte Carlo (MCMC) methods, and combines information about the heart dynamics, the imaging process, and tag appearance. The experimental results demonstrate that the new method improves the performance of even the best of the four previous methods. Yielding higher consistency, accuracy, and intrinsic tag reliability assessment, the proposed method allows for improved analysis of cardiac motion.
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Affiliation(s)
- Ihor Smal
- Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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Ibrahim ESH. Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications. J Cardiovasc Magn Reson 2011; 13:36. [PMID: 21798021 PMCID: PMC3166900 DOI: 10.1186/1532-429x-13-36] [Citation(s) in RCA: 203] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2011] [Accepted: 07/28/2011] [Indexed: 02/06/2023] Open
Abstract
Cardiovascular magnetic resonance (CMR) tagging has been established as an essential technique for measuring regional myocardial function. It allows quantification of local intramyocardial motion measures, e.g. strain and strain rate. The invention of CMR tagging came in the late eighties, where the technique allowed for the first time for visualizing transmural myocardial movement without having to implant physical markers. This new idea opened the door for a series of developments and improvements that continue up to the present time. Different tagging techniques are currently available that are more extensive, improved, and sophisticated than they were twenty years ago. Each of these techniques has different versions for improved resolution, signal-to-noise ratio (SNR), scan time, anatomical coverage, three-dimensional capability, and image quality. The tagging techniques covered in this article can be broadly divided into two main categories: 1) Basic techniques, which include magnetization saturation, spatial modulation of magnetization (SPAMM), delay alternating with nutations for tailored excitation (DANTE), and complementary SPAMM (CSPAMM); and 2) Advanced techniques, which include harmonic phase (HARP), displacement encoding with stimulated echoes (DENSE), and strain encoding (SENC). Although most of these techniques were developed by separate groups and evolved from different backgrounds, they are in fact closely related to each other, and they can be interpreted from more than one perspective. Some of these techniques even followed parallel paths of developments, as illustrated in the article. As each technique has its own advantages, some efforts have been made to combine different techniques together for improved image quality or composite information acquisition. In this review, different developments in pulse sequences and related image processing techniques are described along with the necessities that led to their invention, which makes this article easy to read and the covered techniques easy to follow. Major studies that applied CMR tagging for studying myocardial mechanics are also summarized. Finally, the current article includes a plethora of ideas and techniques with over 300 references that motivate the reader to think about the future of CMR tagging.
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Introduction to the special issue on biomedical image technologies and methods. Comput Med Imaging Graph 2010; 34:415-7. [PMID: 20576402 DOI: 10.1016/j.compmedimag.2010.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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