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Kan A, Leng Y, Li S, Lin F, Fang Q, Tao X, Hu M, Gong L. The predictive value of coronary microvascular dysfunction for left ventricular reverse remodelling in dilated cardiomyopathy. Front Cardiovasc Med 2023; 10:1301509. [PMID: 38111885 PMCID: PMC10726051 DOI: 10.3389/fcvm.2023.1301509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 11/20/2023] [Indexed: 12/20/2023] Open
Abstract
Aims To evaluate the degree of coronary microvascular dysfunction (CMD) in dilated cardiomyopathy (DCM) patients by cardiac magnetic resonance (CMR) first-pass perfusion parameters and to examine the correlation between myocardial perfusion and left ventricle reverse remodelling (LVRR). Methods In this study, 94 DCM patients and 35 healthy controls matched for age and sex were included. Myocardial perfusion parameters, including upslope, time to maximum signal intensity (Timemax), maximum signal intensity (SImax), baseline signal intensity (SIbaseline), and the difference between maximum and baseline signal intensity (SImax-baseline) were measured. Additionally, left ventricular (LV) structure, function parameters, and late gadolinium enhancement (LGE) were also recorded. The parameters were compared between healthy controls and DCM patients. Univariable and multivariable logistic regression analyses were used to determine the predictors of LVRR. Results With a median follow-up period of 12 months [interquartile range (IQR), 8-13], 41 DCM patients (44%) achieved LVRR. Compared with healthy controls, DCM patients presented CMD with reduced upslope, SIbaseline, and increased Timemax (all p < 0.01). Timemax, SImax, and SImax-baseline were further decreased in LVRR than non-LVRR group (Timemax: 60.35 [IQR, 51.46-74.71] vs. 72.41 [IQR, 59.68-97.70], p = 0.017; SImax: 723.52 [IQR, 209.76-909.27] vs. 810.92 [IQR, 581.30-996.89], p = 0.049; SImax-baseline: 462.99 [IQR, 152.25-580.43] vs. 551.13 [IQR, 402.57-675.36], p = 0.038). In the analysis of multivariate logistic regression, Timemax [odds ratio (OR) 0.98; 95% confidence interval (CI) 0.95-1.00; p = 0.032)], heart rate (OR 1.04; 95% CI 1.01-1.08; p = 0.029), LV remodelling index (OR 1.73; 95% CI 1.06-3.00; p = 0.038) and LGE extent (OR 0.85; 95% CI 0.73-0.96; p = 0.021) were independent predictors of LVRR. Conclusions CMD could be found in DCM patients and was more impaired in patients with non-LVRR than LVRR patients. Timemax at baseline was an independent predictor of LVRR in DCM.
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Affiliation(s)
- Ao Kan
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yinping Leng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Shuhao Li
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Fang Lin
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qimin Fang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xinwei Tao
- Department of Medical, Bayer Healthcare, Shanghai, China
| | - Mengyao Hu
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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2
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Tourais J, Scannell CM, Schneider T, Alskaf E, Crawley R, Bosio F, Sanchez-Gonzalez J, Doneva M, Schülke C, Meineke J, Keupp J, Smink J, Breeuwer M, Chiribiri A, Henningsson M, Correia T. High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration. Front Cardiovasc Med 2022; 9:884221. [PMID: 35571164 PMCID: PMC9099052 DOI: 10.3389/fcvm.2022.884221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/04/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction To develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion). Materials and Methods FOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T2* correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm2) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm2) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1–4, non-diagnostic – fully diagnostic). Results FOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS. Discussion FOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.
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Affiliation(s)
- Joao Tourais
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
- Department of Imaging Physics, Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Cian M. Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Ebraham Alskaf
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard Crawley
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Filippo Bosio
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | | | | | | | | | - Jouke Smink
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Markus Henningsson
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linkoping University, Linkoping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linkoping University, Linkoping, Sweden
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre for Marine Sciences (CCMAR), Faro, Portugal
- *Correspondence: Teresa Correia
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3
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Sandfort V, Jacobs M, Arai AE, Hsu LY. Reliable segmentation of 2D cardiac magnetic resonance perfusion image sequences using time as the 3rd dimension. Eur Radiol 2020; 31:3941-3950. [PMID: 33247342 DOI: 10.1007/s00330-020-07474-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/17/2020] [Accepted: 11/05/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Cardiac magnetic resonance (CMR) first-pass perfusion is an established noninvasive diagnostic imaging modality for detecting myocardial ischemia. A CMR perfusion sequence provides a time series of 2D images for dynamic contrast enhancement of the heart. Accurate myocardial segmentation of the perfusion images is essential for quantitative analysis and it can facilitate automated pixel-wise myocardial perfusion quantification. METHODS In this study, we compared different deep learning methodologies for CMR perfusion image segmentation. We evaluated the performance of several image segmentation methods using convolutional neural networks, such as the U-Net in 2D and 3D (2D plus time) implementations, with and without additional motion correction image processing step. We also present a modified U-Net architecture with a novel type of temporal pooling layer which results in improved performance. RESULTS The best DICE scores were 0.86 and 0.90 for LV myocardium and LV cavity, while the best Hausdorff distances were 2.3 and 2.1 pixels for LV myocardium and LV cavity using 5-fold cross-validation. The methods were corroborated in a second independent test set of 20 patients with similar performance (best DICE scores 0.84 for LV myocardium). CONCLUSIONS Our results showed that the LV myocardial segmentation of CMR perfusion images is best performed using a combination of motion correction and 3D convolutional networks which significantly outperformed all tested 2D approaches. Reliable frame-by-frame segmentation will facilitate new and improved quantification methods for CMR perfusion imaging. KEY POINTS • Reliable segmentation of the myocardium offers the potential to perform pixel level perfusion assessment. • A deep learning approach in combination with motion correction, 3D (2D + time) methods, and a deep temporal connection module produced reliable segmentation results.
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Affiliation(s)
- Veit Sandfort
- Stanford Medicine, Pasteur Drive 300, Stanford, CA, 94305, USA. .,National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Matthew Jacobs
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.,Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC, USA
| | - Andrew E Arai
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Li-Yueh Hsu
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.,Clinical Center, National Institutes of Health, Bethesda, MD, USA
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4
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Scannell CM, Correia T, Villa ADM, Schneider T, Lee J, Breeuwer M, Chiribiri A, Henningsson M. Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging. Sci Rep 2020; 10:12684. [PMID: 32728198 PMCID: PMC7392760 DOI: 10.1038/s41598-020-69747-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/15/2020] [Indexed: 11/08/2022] Open
Abstract
Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. Furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. In this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. The Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. This is followed by a second non-linear correction step using the Dixon water images to remove residual motion. The proposed Dixon motion correction technique was compared to the state-of-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images.
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Affiliation(s)
- Cian M Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Adriana D M Villa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Jack Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Marcel Breeuwer
- Philips Healthcare, Best, The Netherlands
- Department of Biomedical Engineering, Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Markus Henningsson
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
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5
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Kim YC, Kim KR, Choe YH. Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105150. [PMID: 31671341 DOI: 10.1016/j.cmpb.2019.105150] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/07/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to efficient quantification of perfusion defects. The purpose of this study was to investigate the usefulness of uncertainty estimation in deep convolutional neural networks for automatic myocardial segmentation. METHODS A U-Net segmentation model was trained on the cardiac cine data. Monte Carlo dropout sampling of the U-Net model was performed on the dynamic perfusion datasets frame-by-frame to estimate the standard deviation (SD) maps. The uncertainty estimate based on the sum of the SD values was used to select the optimal frames for endocardial and epicardial segmentations. DCE perfusion data from 35 subjects (14 subjects with coronary artery disease, 8 subjects with hypertrophic cardiomyopathy, and 13 healthy volunteers) were evaluated. The Dice similarity scores of the proposed method were compared with those of a semi-automatic U-Net segmentation method, which involved user selection of an image frame for segmentation in the cardiac perfusion dataset. RESULTS The proposed method was fully automatic and did not require manual labeling of the cardiac perfusion image data for model development. The mean Dice similarity score of the proposed automatic method was 0.806 (±0.096), which was comparable to the 0.808 (±0.084) Dice score of the semi-automatic U-Net segmentation method (intraclass correlation coefficient = 0.61, P < 0.001). CONCLUSIONS Our study demonstrated the feasibility of applying an existing model trained on cardiac cine data to dynamic cardiac perfusion data to achieve robust and automatic segmentation of the myocardium. The uncertainty estimates can be used for screening purposes, which would facilitate the cases with high endocardial and epicardial uncertainty estimates to be sent for further evaluation and correction by human experts.
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Affiliation(s)
- Yoon-Chul Kim
- Clinical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Khu Rai Kim
- Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea
| | - Yeon Hyeon Choe
- Department of Radiology and HVSI Imaging Center, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Menchón-Lara RM, Simmross-Wattenberg F, Casaseca-de-la-Higuera P, Martín-Fernández M, Alberola-López C. Reconstruction techniques for cardiac cine MRI. Insights Imaging 2019; 10:100. [PMID: 31549235 PMCID: PMC6757088 DOI: 10.1186/s13244-019-0754-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 05/17/2019] [Indexed: 12/17/2022] Open
Abstract
The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction. Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined. Thus, this paper aims to provide a general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies.
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Affiliation(s)
- Rosa-María Menchón-Lara
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain.
| | - Federico Simmross-Wattenberg
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Pablo Casaseca-de-la-Higuera
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Marcos Martín-Fernández
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Carlos Alberola-López
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
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7
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Tilborghs S, Dresselaers T, Claus P, Claessen G, Bogaert J, Maes F, Suetens P. Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach. Med Image Anal 2018; 52:212-227. [PMID: 30597459 DOI: 10.1016/j.media.2018.12.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 10/19/2018] [Accepted: 12/17/2018] [Indexed: 02/06/2023]
Abstract
T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.g. cardiac motion, respiratory motion or patient motion, hamper accurate T1 and ECV calculation such that retrospective motion correction is required. We propose a new robust non-rigid registration framework combining a data-driven initialization with a model-based registration approach, which uses a model for T1 relaxation to avoid direct registration of images with highly varying contrast. The registration between native T1 and enhanced T1 to obtain a motion free ECV map is also calculated using information from T1 model-fitting. The method was validated on three datasets recorded with two substantially different acquisition protocols (MOLLI (dataset 1 (n=15) and dataset 2 (n=29)) and STONE (dataset 3 (n = 210))), one in breath-hold condition and one free-breathing. The average Dice coefficient increased from 72.6 ± 12.1% to 82.3 ± 7.4% (P < 0.05) and mean boundary error decreased from 2.91 ± 1.51mm to 1.62 ± 0.80mm (P < 0.05) for motion correction in a single T1-weighted image sequence (3 datasets) while average Dice coefficient increased from 63.4 ± 22.5% to 79.2 ± 8.5% (P < 0.05) and mean boundary error decreased from 3.26 ± 2.64mm to 1.77 ± 0.86mm (P < 0.05) between native and enhanced sequences (dataset 1 and 2). Overall, the native T1 SD error decreased from 67.32 ± 32.57ms to 58.11 ± 21.59ms (P < 0.05), enhanced SD error from 30.15 ± 25ms to 22.74 ± 8.94ms (P < 0.05) and ECV SD error from 10.08 ± 9.59% to 5.42 ± 3.21% (P < 0.05) (dataset 1 and 2).
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Affiliation(s)
- Sofie Tilborghs
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Herestraat 49 - 7003, Leuven, 3000, Belgium.
| | - Tom Dresselaers
- Department of Imaging and Pathology, Radiology, KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Herestraat 49 - 7003, Leuven, 3000, Belgium
| | - Piet Claus
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Herestraat 49 - 7003, Leuven, 3000, Belgium
| | - Guido Claessen
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Jan Bogaert
- Department of Imaging and Pathology, Radiology, KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Herestraat 49 - 7003, Leuven, 3000, Belgium
| | - Frederik Maes
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Herestraat 49 - 7003, Leuven, 3000, Belgium
| | - Paul Suetens
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Herestraat 49 - 7003, Leuven, 3000, Belgium
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8
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Lehnert J, Wübbeler G, Kolbitsch C, Chiribiri A, Coquelin L, Ebrard G, Smith N, Schaeffter T, Elster C. Pixel-wise quantification of myocardial perfusion using spatial Tikhonov regularization. Phys Med Biol 2018; 63:215017. [PMID: 30372423 DOI: 10.1088/1361-6560/aae758] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Quantification of myocardial perfusion by contrast-enhanced cardiovascular magnetic resonance imaging (CMR) aims for an observer independent and reproducible risk assessment of cardiovascular disease. Currently, the data used for the pixel-wise analysis of cardiac perfusion are either filtered prior to a fitting procedure, which inherently reduces the spatial resolution of data; or all pixels are considered without any regularization or prior filtering, which yields an unstable fit in the presence of low signal-to-noise ratio. Here, we propose a new pixel-wise analysis based on spatial Tikhonov regularization which exploits the spatial smoothness of the data and ensures accurate quantification even for images with low signal-to-noise ratio. The regularization parameter is determined automatically by an L-curve criterion. We study the performance of our method on a numerical phantom and demonstrate that the method reduces significantly the root-mean square error in the perfusion estimate compared to a non-regularized fit. In patient data our method allows us to recover the myocardial perfusion and to distinguish between healthy and ischemic regions.
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Affiliation(s)
- Judith Lehnert
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany. Author to whom any correspondence should be addressed
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9
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Pontre B, Cowan BR, DiBella E, Kulaseharan S, Likhite D, Noorman N, Tautz L, Tustison N, Wollny G, Young AA, Suinesiaputra A. An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI. IEEE J Biomed Health Inform 2017; 21:1315-1326. [PMID: 28880152 PMCID: PMC5658235 DOI: 10.1109/jbhi.2016.2597145] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Cardiac magnetic resonance perfusion examinations enable noninvasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (Ktrans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine, or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information, or cross correlation. There were no significant differences in Ktrans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum-of-squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of nonrigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project ( www.cardiacatlas.org).
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10
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Kim YC, Chung Y, Choe YH. Automatic localization of anatomical landmarks in cardiac MR perfusion using random forests. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.07.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Yin L, Xu HY, Zheng SS, Zhu Y, Xiao JX, Zhou W, Yu SS, Gong LG. 3.0 T magnetic resonance myocardial perfusion imaging for semi-quantitative evaluation of coronary microvascular dysfunction in hypertrophic cardiomyopathy. Int J Cardiovasc Imaging 2017; 33:1949-1959. [DOI: 10.1007/s10554-017-1189-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 06/09/2017] [Indexed: 01/21/2023]
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12
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Gupta V, Bustamante M, Fredriksson A, Carlhäll CJ, Ebbers T. Improving left ventricular segmentation in four-dimensional flow MRI using intramodality image registration for cardiac blood flow analysis. Magn Reson Med 2017; 79:554-560. [PMID: 28303611 DOI: 10.1002/mrm.26674] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 02/06/2017] [Accepted: 02/17/2017] [Indexed: 11/08/2022]
Abstract
PURPOSE Assessment of blood flow in the left ventricle using four-dimensional flow MRI requires accurate left ventricle segmentation that is often hampered by the low contrast between blood and the myocardium. The purpose of this work is to improve left-ventricular segmentation in four-dimensional flow MRI for reliable blood flow analysis. METHOD The left ventricle segmentations are first obtained using morphological cine-MRI with better in-plane resolution and contrast, and then aligned to four-dimensional flow MRI data. This alignment is, however, not trivial due to inter-slice misalignment errors caused by patient motion and respiratory drift during breath-hold based cine-MRI acquisition. A robust image registration based framework is proposed to mitigate such errors automatically. Data from 20 subjects, including healthy volunteers and patients, was used to evaluate its geometric accuracy and impact on blood flow analysis. RESULTS High spatial correspondence was observed between manually and automatically aligned segmentations, and the improvements in alignment compared to uncorrected segmentations were significant (P < 0.01). Blood flow analysis from manual and automatically corrected segmentations did not differ significantly (P > 0.05). CONCLUSION Our results demonstrate the efficacy of the proposed approach in improving left-ventricular segmentation in four-dimensional flow MRI, and its potential for reliable blood flow analysis. Magn Reson Med 79:554-560, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Vikas Gupta
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Mariana Bustamante
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Alexandru Fredriksson
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Carl-Johan Carlhäll
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Physiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Tino Ebbers
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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13
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Peng P, Lekadir K, Gooya A, Shao L, Petersen SE, Frangi AF. A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA (NEW YORK, N.Y.) 2016; 29:155-95. [PMID: 26811173 PMCID: PMC4830888 DOI: 10.1007/s10334-015-0521-4] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 12/01/2015] [Accepted: 12/17/2015] [Indexed: 01/19/2023]
Abstract
Cardiovascular magnetic resonance (CMR) has become a key imaging modality in clinical cardiology practice due to its unique capabilities for non-invasive imaging of the cardiac chambers and great vessels. A wide range of CMR sequences have been developed to assess various aspects of cardiac structure and function, and significant advances have also been made in terms of imaging quality and acquisition times. A lot of research has been dedicated to the development of global and regional quantitative CMR indices that help the distinction between health and pathology. The goal of this review paper is to discuss the structural and functional CMR indices that have been proposed thus far for clinical assessment of the cardiac chambers. We include indices definitions, the requirements for the calculations, exemplar applications in cardiovascular diseases, and the corresponding normal ranges. Furthermore, we review the most recent state-of-the art techniques for the automatic segmentation of the cardiac boundaries, which are necessary for the calculation of the CMR indices. Finally, we provide a detailed discussion of the existing literature and of the future challenges that need to be addressed to enable a more robust and comprehensive assessment of the cardiac chambers in clinical practice.
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Affiliation(s)
- Peng Peng
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | | | - Ali Gooya
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK
| | - Ling Shao
- Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
| | - Steffen E Petersen
- Centre Lead for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Alejandro F Frangi
- Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, S1 3JD, UK.
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14
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Ahlander BM, Maret E, Brudin L, Starck SA, Engvall J. An echo-planar imaging sequence is superior to a steady-state free precession sequence for visual as well as quantitative assessment of cardiac magnetic resonance stress perfusion. Clin Physiol Funct Imaging 2015; 37:52-61. [PMID: 26147785 DOI: 10.1111/cpf.12267] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 05/08/2015] [Indexed: 11/26/2022]
Abstract
BACKGROUND To assess myocardial perfusion, steady-state free precession cardiac magnetic resonance (SSFP, CMR) was compared with gradient-echo-echo-planar imaging (GRE-EPI) using myocardial perfusion scintigraphy (MPS) as reference. METHODS Cardiac magnetic resonance perfusion was recorded in 30 patients with SSFP and in another 30 patients with GRE-EPI. Timing and extent of inflow delay to the myocardium was visually assessed. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were calculated. Myocardial scar was visualized with a phase-sensitive inversion recovery sequence (PSIR). All scar positive segments were considered pathologic. In MPS, stress and rest images were used as in clinical reporting. The CMR contrast wash-in slope was calculated and compared with the stress score from the MPS examination. CMR scar, CMR perfusion and MPS were assessed separately by one expert for each method who was blinded to other aspects of the study. RESULTS Visual assessment of CMR had a sensitivity for the detection of an abnormal MPS at 78% (SSFP) versus 91% (GRE-EPI) and a specificity of 58% (SSFP) versus 84% (GRE-EPI). Kappa statistics for SSFP and MPS was 0·29, for GRE-EPI and MPS 0·72. The ANOVA of CMR perfusion slopes for all segments versus MPS score (four levels based on MPS) had correlation r = 0·64 (SSFP) and r = 0·96 (GRE-EPI). SNR was for normal segments 35·63 ± 11·80 (SSFP) and 17·98 ± 8·31 (GRE-EPI), while CNR was 28·79 ± 10·43 (SSFP) and 13·06 ± 7·61 (GRE-EPI). CONCLUSION GRE-EPI displayed higher agreement with the MPS results than SSFP despite significantly lower signal intensity, SNR and CNR.
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Affiliation(s)
| | - Eva Maret
- Department of Radiology, Ryhov County Hospital, Jonkoping, Sweden.,Department of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden
| | - Lars Brudin
- Department of Clinical Physiology, Kalmar County Hospital, Kalmar, Sweden
| | - Sven-Ake Starck
- Department of Natural Science and Biomedicine, School of Health Sciences, Jonkoping University, Jonkoping, Sweden.,Department of Oncology, Hospital Physics, Ryhov County Hospital, Jonkoping, Sweden
| | - Jan Engvall
- Department of Medical and Health Sciences, Linkoping University, Linkoping, Sweden.,Department of Clinical Physiology, County Council of Ostergotland, Linkoping, Sweden.,Center of Medical Image Science and Visualisation, Linkoping University, Linkoping, Sweden
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15
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Wollny G, Kellman P. Free breathing myocardial perfusion data sets for performance analysis of motion compensation algorithms. Gigascience 2014; 3:23. [PMID: 25392734 PMCID: PMC4226922 DOI: 10.1186/2047-217x-3-23] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 10/13/2014] [Indexed: 12/02/2022] Open
Abstract
Background Perfusion quantification by using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) has proved to be a reliable tool for the diagnosis of coronary artery disease that leads to reduced blood flow to the myocardium. The image series resulting from such acquisition usually exhibits a breathing motion that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. Various algorithms have been presented to facilitate such a motion compensation, but the lack of publicly available data sets hinders a proper, reproducible comparison of these algorithms. Material Free breathing perfusion MRI series of ten patients considered clinically to have a stress perfusion defect were acquired; for each patient a rest and a stress study was executed. Manual segmentations of the left ventricle myocardium and the right-left ventricle insertion point are provided for all images in order to make a unified validation of the motion compensation algorithms and the perfusion analysis possible. In addition, all the scripts and the software required to run the experiments are provided alongside the data, and to enable interested parties to directly run the experiments themselves, the test bed is also provided as a virtual hard disk. Findings To illustrate the utility of the data set two motion compensation algorithms with publicly available implementations were applied to the data and earlier reported results about the performance of these algorithms could be confirmed. Conclusion The data repository alongside the evaluation test bed provides the option to reliably compare motion compensation algorithms for myocardial perfusion MRI. In addition, we encourage that researchers add their own annotations to the data set, either to provide inter-observer comparisons of segmentations, or to make other applications possible, for example, the validation of segmentation algorithms.
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Affiliation(s)
- Gert Wollny
- Biomedical Imaging Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain ; Ciber BBN, Zaragoza, Spain
| | - Peter Kellman
- Laboratory of Cardiac Energetics, National Heart, Lung and Blood Institute, National Institutes of Health, DHHS, Bethesda, MD, USA
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16
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Xu HY, Yang ZG, Sun JY, Wen LY, Zhang G, Zhang S, Guo YK. The regional myocardial microvascular dysfunction differences in hypertrophic cardiomyopathy patients with or without left ventricular outflow tract obstruction: assessment with first-pass perfusion imaging using 3.0-T cardiac magnetic resonance. Eur J Radiol 2014; 83:665-72. [PMID: 24521610 DOI: 10.1016/j.ejrad.2014.01.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2013] [Revised: 12/16/2013] [Accepted: 01/05/2014] [Indexed: 02/05/2023]
Abstract
PURPOSE To assess regional myocardial microvascular dysfunction differences in hypertrophic cardiomyopathy (HCM) patients with or without left ventricular outflow tract obstruction using 3.0-T cardiac magnetic resonance (CMR) first-pass perfusion imaging. MATERIALS AND METHODS Forty-two HCM patients, including 25 HCM patients with left ventricular outflow tract obstruction (HOCM), 17 HCM patients without left ventricular outflow tract obstruction (NOHCM), and 14 healthy subjects underwent CMR. The left ventricular (LV) function, left ventricular end-diastolic wall thickness (EDTH), and diameter of left ventricular outflow tract (LVOT) were measured and calculated. Based on the signal-time curve of the first-pass myocardium perfusion imaging, perfusion parameters including upslope, time to peak, and peak intensity, were assessed and compared by using one-way analysis of variance and independent t tests. RESULTS On the first-pass perfusion imaging, lower upslope and peak intensity and longer time to peak were found in HCM patients compared with normal subjects (all p<0.05). In contrast to the NOHCM group, the average time to peak of the HOCM group was increased (13.30 ± 4.82 s vs 16.28 ± 4.90 s, p<0.05), but first-pass perfusion upslope was reduced (4.96 ± 2.55 vs 2.58 ± 0.77, p<0.05). According to the bull's-eye model, the HOCM group's average thickness of basal segments was thicker than the NOHCM group, especially the anteroseptal, inferolateral, and anterior wall values, with a corresponding lower first-pass perfusion upslope than the NOHCM group (all p<0.05). A significant correlation was observed between first-pass perfusion upslope and LV EDTH (r=-0.551, p<0.001) and LVOT diameter (r=0.472, p<0.001). CONCLUSIONS The regional myocardial microvascular dysfunction differences in hypertrophic cardiomyopathy (HCM) patients with or without left ventricular outflow tract obstruction can be detected with first-pass perfusion CMR imaging.
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Affiliation(s)
- Hua-yan Xu
- Department of Radiology, National Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan 610041, China
| | - Zhi-gang Yang
- Department of Radiology, National Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan 610041, China.
| | - Jia-yu Sun
- Department of Radiology, National Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan 610041, China
| | - Ling-yi Wen
- Department of Radiology, National Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan 610041, China
| | - Ge Zhang
- Department of Radiology, National Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan 610041, China
| | - Shuai Zhang
- Department of Radiology, National Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, Sichuan 610041, China
| | - Ying-kun Guo
- Department of Radiology, West China Second University Hospital, Sichuan University, China
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Cordero-Grande L, Merino-Caviedes S, Aja-Fernández S, Alberola-López C. Groupwise elastic registration by a new sparsity-promoting metric: application to the alignment of cardiac magnetic resonance perfusion images. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013; 35:2638-2650. [PMID: 24051725 DOI: 10.1109/tpami.2013.74] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.
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Abstract
First-pass cardiac MR perfusion (CMRP) imaging allows identification of hypo-perfused areas in the myocardium and therefore helps in early detection of coronary artery disease (CAD). However, its efficacy is often limited by respiratory motion artifacts, especially in stress-induced sequences. These distortions lead to unreliable estimates of perfusion linked parameters, such as the myocardial perfusion reserve index (MPRI). We propose a novel, robust motion correction method that suppresses motion artifacts in the frequency domain. The method is validated using rest and stress perfusion datasets of 10 patients and is compared to a state-of-the-art independent component analysis based method. Contrary to the latter, the proposed method reduces the remaining motion to less than the pixel size and allows the reliable computation of the MPRI. The strong agreement between perfusion parameters based on expert contours and after applying the proposed method enables the near-automated quantitative analyses of stress MR perfusion sequences in a clinical setting.
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