Retrospective motion correction for cardiac multi‐parametric mapping with dictionary matching‐based image synthesis and a low‐rank constraint
Purpose To develop a model‐based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi‐parametric mapping. Methods The proposed method constructs a hybrid loss that includes a dictionary‐matching loss and a signal low‐rankness loss, whe...
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| Published in | Magnetic resonance in medicine Vol. 93; no. 2; pp. 550 - 562 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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01.02.2025
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| Abstract | Purpose
To develop a model‐based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi‐parametric mapping.
Methods
The proposed method constructs a hybrid loss that includes a dictionary‐matching loss and a signal low‐rankness loss, where the former registers the multi‐contrast original images to a set of motion‐free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non‐MoCo, a pairwise registration method (Pairwise‐MI), and a groupwise registration method (pTVreg) via a free‐breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively.
Results
The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non‐MoCo, Pairwise‐MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non‐MoCo, Pairwise‐MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath‐hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments.
Conclusion
The proposed method significantly improves the motion correction accuracy and mapping quality compared with non‐MoCo and alternative image‐based methods. |
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| AbstractList | To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping.PURPOSETo develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping.The proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively.METHODSThe proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively.The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments.RESULTSThe proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments.The proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods.CONCLUSIONThe proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods. Purpose To develop a model‐based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi‐parametric mapping. Methods The proposed method constructs a hybrid loss that includes a dictionary‐matching loss and a signal low‐rankness loss, where the former registers the multi‐contrast original images to a set of motion‐free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non‐MoCo, a pairwise registration method (Pairwise‐MI), and a groupwise registration method (pTVreg) via a free‐breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively. Results The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non‐MoCo, Pairwise‐MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non‐MoCo, Pairwise‐MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath‐hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments. Conclusion The proposed method significantly improves the motion correction accuracy and mapping quality compared with non‐MoCo and alternative image‐based methods. PurposeTo develop a model‐based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi‐parametric mapping.MethodsThe proposed method constructs a hybrid loss that includes a dictionary‐matching loss and a signal low‐rankness loss, where the former registers the multi‐contrast original images to a set of motion‐free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non‐MoCo, a pairwise registration method (Pairwise‐MI), and a groupwise registration method (pTVreg) via a free‐breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively.ResultsThe proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non‐MoCo, Pairwise‐MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non‐MoCo, Pairwise‐MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath‐hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments.ConclusionThe proposed method significantly improves the motion correction accuracy and mapping quality compared with non‐MoCo and alternative image‐based methods. To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping. The proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively. The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments. The proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods. |
| Author | Chen, Haiyang Emu, Yixin Gao, Juan Chen, Zhuo Aburas, Ahmed Hu, Chenxi |
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| Cites_doi | 10.1016/j.media.2022.102615 10.1186/s12968-023-00973-6 10.4070/kcj.2020.0157 10.1007/978-3-031-43990-2_22 10.1002/mrm.25270 10.1186/s12968-015-0141-1 10.1016/j.media.2018.12.004 10.3389/fcvm.2022.953823 10.1002/mrm.28753 10.1002/mrm.25421 10.1109/42.796284 10.1002/mrm.23153 10.1007/s00259-021-05384-2 10.3348/kjr.2017.18.1.113 10.1002/jmri.27023 10.1002/mrm.24139 10.1002/jmri.29206 10.1007/978-3-031-16446-0_28 10.1109/ISBI.2004.1398686 10.1002/mrm.29143 10.1109/TMI.2019.2897538 10.1002/mrm.25439 10.1002/mrm.20110 10.1016/j.media.2015.12.004 10.1002/mrm.25402 10.1007/s10334-017-0668-2 10.1109/TMI.2016.2610583 10.1186/1532-429X-11-56 10.1109/CVPRW.2016.84 10.1016/j.jcmg.2017.09.010 10.1109/TMI.2014.2337321 10.3389/fcvm.2021.768245 10.1186/1532-429X-14-63 10.1109/ISBI.2013.6556655 10.1109/JPROC.2019.2936998 |
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| Keywords | multi‐parametric mapping motion correction cardiac quantitative MRI dictionary matching low‐rank constraint |
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| References_xml | – volume: 9 start-page: 9 year: 2022 article-title: Simultaneous multi‐parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: basic concepts and status of clinical development publication-title: Front Cardiovasc Med – volume: 31 start-page: 115 year: 2018 end-page: 129 article-title: Cardiac magnetic resonance T1 and extracellular volume mapping with motion correction and co‐registration based on fast elastic image registration publication-title: Magn Reson Mater Phy – volume: 14 start-page: 63 year: 2012 article-title: Extracellular volume fraction mapping in the myocardium, part 1: evaluation of an automated method publication-title: J Cardiovasc Magn Reson – volume: 38 start-page: 1788 year: 2019 end-page: 1800 article-title: VoxelMorph: a learning framework for deformable medical image registration publication-title: IEEE Trans Med Imaging – volume: 11 start-page: 56 year: 2009 article-title: T2 quantification for improved detection of myocardial edema publication-title: J Cardiovasc Magn Reson – volume: 25 start-page: 63 year: 2023 article-title: Free‐breathing simultaneous native myocardial T1, T2 and T1ρ mapping with cartesian acquisition and dictionary matching publication-title: J Cardiovasc Magn Reson – volume: 74 start-page: 489 year: 2015 end-page: 498 article-title: Accelerated MR parameter mapping with low‐rank and sparsity constraints publication-title: Magn Reson Med – volume: 74 start-page: 523 year: 2015 end-page: 528 article-title: Fast group matching for MR fingerprinting reconstruction publication-title: Magn Reson Med – volume: 33 start-page: 2311 year: 2014 end-page: 2322 article-title: SVD compression for magnetic resonance fingerprinting in the time domain publication-title: IEEE Trans Med Imaging – volume: 50 start-page: 658 year: 2020 end-page: 676 article-title: CMR parametric mapping as a tool for myocardial tissue characterization publication-title: Korean Circ J – volume: 18 start-page: 113 year: 2017 end-page: 131 article-title: Myocardial T1 and T2 mapping: techniques and clinical applications publication-title: Korean J Radiol – volume: 17 start-page: 46 year: 2015 article-title: Impact of motion correction on reproducibility and spatial variability of quantitative myocardial T2 mapping publication-title: J Cardiovasc Magn Reson – volume: 86 start-page: 1226 year: 2021 end-page: 1240 article-title: Free‐breathing simultaneous T1, T2, and T2* quantification in the myocardium publication-title: Magn Reson Med – volume: 8 start-page: 8 year: 2021 article-title: MOCOnet: robust motion correction of cardiovascular magnetic resonance T1 mapping using convolutional neural networks publication-title: Front Cardiovasc Med – volume: 11 start-page: 926 year: 2018 end-page: 928 article-title: Definition of left ventricular segments for cardiac magnetic resonance imaging publication-title: JACC Cardiovasc Imaging – volume: 52 start-page: 141 year: 2004 end-page: 146 article-title: Modified look‐locker inversion recovery (MOLLI) for high‐resolution T1 mapping of the heart publication-title: Magn Reson Med – volume: 68 start-page: 1570 year: 2012 end-page: 1578 article-title: Myocardial T2 mapping with respiratory navigator and automatic nonrigid motion correction publication-title: Magn Reson Med – volume: 87 start-page: 2347 year: 2022 end-page: 2362 article-title: Cartesian dictionary‐based native T1 and T2 mapping of the myocardium publication-title: Magn Reson Med – start-page: 626 year: 2016 end-page: 633 – year: 2023 article-title: Cardiac MR fingerprinting: overview, technical developments, and applications publication-title: J Magn Reson Imaging – start-page: 291 year: 2022 end-page: 300 – start-page: 1038 year: 2013 end-page: 1041 – volume: 52 start-page: 1340 year: 2020 end-page: 1351 article-title: Cardiac T * mapping: techniques and clinical applications publication-title: J Magn Reson Imaging – volume: 18 start-page: 712 year: 1999 end-page: 721 article-title: Nonrigid registration using free‐form deformations: application to breast MR images publication-title: IEEE Trans Med Imaging – volume: 36 start-page: 385 year: 2017 end-page: 395 article-title: Isotropic Total variation regularization of displacements in parametric image registration publication-title: IEEE Trans Med Imaging – volume: 29 start-page: 65 year: 2016 end-page: 78 article-title: PCA‐based groupwise image registration for quantitative MRI publication-title: Med Image Anal – year: 2023 – volume: 67 start-page: 1644 year: 2012 end-page: 1655 article-title: Motion correction for myocardial T1 mapping using image registration with synthetic image estimation publication-title: Magn Reson Med – start-page: 908 year: 2004 end-page: 911 – volume: 74 start-page: 365 year: 2015 end-page: 371 article-title: Simultaneous T1 and T2 quantification of the myocardium using cardiac balanced‐SSFP inversion recovery with interleaved sampling acquisition (CABIRIA) publication-title: Magn Reson Med – volume: 73 start-page: 1469 year: 2015 end-page: 1482 article-title: Adaptive registration of varying contrast‐weighted images for improved tissue characterization (ARCTIC): application to T1 mapping publication-title: Magn Reson Med – volume: 82 year: 2022 article-title: TransMorph: transformer for unsupervised medical image registration publication-title: Med Image Anal – volume: 48 start-page: 4189 year: 2021 end-page: 4200 article-title: Magnetic resonance fingerprinting: an overview publication-title: Eur J Nucl Med Mol Imaging – volume: 108 start-page: 69 year: 2020 end-page: 85 article-title: Machine learning for rapid magnetic resonance fingerprinting tissue property quantification publication-title: Proc IEEE – volume: 52 start-page: 212 year: 2019 end-page: 227 article-title: Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach publication-title: Med Image Anal – ident: e_1_2_6_37_1 doi: 10.1016/j.media.2022.102615 – ident: e_1_2_6_27_1 doi: 10.1186/s12968-023-00973-6 – ident: e_1_2_6_3_1 doi: 10.4070/kcj.2020.0157 – ident: e_1_2_6_31_1 doi: 10.1007/978-3-031-43990-2_22 – ident: e_1_2_6_9_1 doi: 10.1002/mrm.25270 – ident: e_1_2_6_10_1 doi: 10.1186/s12968-015-0141-1 – ident: e_1_2_6_17_1 doi: 10.1016/j.media.2018.12.004 – ident: e_1_2_6_7_1 doi: 10.3389/fcvm.2022.953823 – ident: e_1_2_6_23_1 doi: 10.1002/mrm.28753 – ident: e_1_2_6_20_1 doi: 10.1002/mrm.25421 – ident: e_1_2_6_19_1 doi: 10.1109/42.796284 – ident: e_1_2_6_14_1 doi: 10.1002/mrm.23153 – ident: e_1_2_6_28_1 doi: 10.1007/s00259-021-05384-2 – ident: e_1_2_6_2_1 doi: 10.3348/kjr.2017.18.1.113 – ident: e_1_2_6_6_1 doi: 10.1002/jmri.27023 – ident: e_1_2_6_8_1 doi: 10.1002/mrm.24139 – ident: e_1_2_6_29_1 doi: 10.1002/jmri.29206 – ident: e_1_2_6_30_1 doi: 10.1007/978-3-031-16446-0_28 – ident: e_1_2_6_21_1 doi: 10.1109/ISBI.2004.1398686 – ident: e_1_2_6_18_1 doi: 10.1002/mrm.29143 – ident: e_1_2_6_25_1 – ident: e_1_2_6_36_1 doi: 10.1109/TMI.2019.2897538 – ident: e_1_2_6_35_1 doi: 10.1002/mrm.25439 – ident: e_1_2_6_4_1 doi: 10.1002/mrm.20110 – ident: e_1_2_6_11_1 doi: 10.1016/j.media.2015.12.004 – ident: e_1_2_6_22_1 doi: 10.1002/mrm.25402 – ident: e_1_2_6_13_1 doi: 10.1007/s10334-017-0668-2 – ident: e_1_2_6_24_1 doi: 10.1109/TMI.2016.2610583 – ident: e_1_2_6_5_1 doi: 10.1186/1532-429X-11-56 – ident: e_1_2_6_12_1 doi: 10.1109/CVPRW.2016.84 – ident: e_1_2_6_26_1 doi: 10.1016/j.jcmg.2017.09.010 – ident: e_1_2_6_34_1 doi: 10.1109/TMI.2014.2337321 – ident: e_1_2_6_32_1 doi: 10.3389/fcvm.2021.768245 – ident: e_1_2_6_15_1 doi: 10.1186/1532-429X-14-63 – ident: e_1_2_6_16_1 doi: 10.1109/ISBI.2013.6556655 – ident: e_1_2_6_33_1 doi: 10.1109/JPROC.2019.2936998 |
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To develop a model‐based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac... To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric... PurposeTo develop a model‐based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac... |
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| SubjectTerms | Adult Algorithms cardiac quantitative MRI Dictionaries dictionary matching Epicardium Female Healthy Volunteers Heart Heart - diagnostic imaging Humans Image contrast Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted - methods Image quality low‐rank constraint Magnetic Resonance Imaging - methods Male Mapping Matching Methods Motion motion correction multi‐parametric mapping Reproducibility of Results Retrospective Studies Segments Signal quality Synthetic data Tracking errors |
| Title | Retrospective motion correction for cardiac multi‐parametric mapping with dictionary matching‐based image synthesis and a low‐rank constraint |
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