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Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint

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Abstract

Objective

Diffusion tensor magnetic resonance imaging (DT-MRI, or DTI) is a promising technique for invasively probing biological tissue structures. However, DTI is known to suffer from much longer acquisition time with respect to conventional MRI and the problem is worsened when dealing with in vivo acquisitions. Therefore, faster DTI for both ex vivo and in vivo scans is highly desired.

Materials and methods

This paper proposes a new compressed sensing (CS) reconstruction method that employs local low-rank (LLR) model and three-dimensional (3D) total variation (TV) constraint to reconstruct cardiac diffusion-weighted (DW) images from highly undersampled k-space data. The LLR model takes the set of DW images corresponding to different diffusion gradient directions as a 3D image volume and decomposes the latter into overlapping 3D blocks. Then, the 3D blocks are stacked as two-dimensional (2D) matrix. Finally, low-rank property is applied to each block matrix and the 3D TV constraint to the 3D image volume. The underlying constrained optimization problem is finally solved using the first-order fast method. The proposed method is evaluated on real ex vivo cardiac DTI data as a prerequisite to in vivo cardiac DTI applications.

Results

The results on real human ex vivo cardiac DTI images demonstrate that the proposed method exhibits lower reconstruction errors for DTI indices, including fractional anisotropy (FA), mean diffusivities (MD), transverse angle (TA), and helix angle (HA), compared to existing CS-based DTI image reconstruction techniques.

Conclusion

The proposed method provides better reconstruction quality and more accurate DTI indices in comparison with the state-of-the-art CS-based DW image reconstruction methods.

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References

  1. Froeling M, Strijkers G, Nederveen A, Chamuleau S, Luijten P (2014) Diffusion tensor MRI of the heart—in vivo imaging of myocardial fiber architecture. Curr Cardiovasc Imaging Rep 7(7):1–11. https://doi.org/10.1007/s12410-014-9276-y

    Article  Google Scholar 

  2. Naumova AV, Yarnykh VL (2014) Assessment of heart microstructure from mouse to man. Circulation 129(17):1720–1722. https://doi.org/10.1161/circulationaha.114.009221

    Article  PubMed  PubMed Central  Google Scholar 

  3. Tournier J-D, Mori S, Leemans A (2011) Diffusion tensor imaging and beyond. Magn Reson Med 65(6):1532–1556. https://doi.org/10.1002/mrm.22924

    Article  PubMed  PubMed Central  Google Scholar 

  4. Yang F, Zhu Y-M, Magnin IE, Luo J-H, Croisille P, Kingsley PB (2012) Feature-based interpolation of diffusion tensor fields and application to human cardiac DT-MRI. Med Image Anal 16(2):459–481. https://doi.org/10.1016/j.media.2011.11.003

    Article  PubMed  Google Scholar 

  5. Basser PJ, Mattiello J, Lebihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66(1):259–267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Basser PJ, Mattiello J, Lebihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin-echo. J Magn Reson Ser B 103(3):247–254. https://doi.org/10.1006/jmrb.1994.1037

    Article  CAS  Google Scholar 

  7. Scollan DF, Holmes A, Winslow R, Forder J (1998) Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging. Am J Physiol Heart Circ Physiol 275(6):H2308–H2318

    Article  CAS  Google Scholar 

  8. Holmes AA, Scollan DF, Winslow RL (2000) Direct histological validation of diffusion tensor MRI in formaldehyde-fixed myocardium. Magn Reson Med 44(1):157–161. https://doi.org/10.1002/1522-2594(200007)44:1%3c157:aid-mrm22%3e3.0.co;2-f

    Article  CAS  PubMed  Google Scholar 

  9. Mekkaoui C, Reese TG, Jackowski MP, Bhat H, Sosnovik DE (2015) Diffusion MRI in the heart. Nmr Biomed. https://doi.org/10.1002/nbm.3426

    Article  PubMed  PubMed Central  Google Scholar 

  10. Pravdin SF, Berdyshev VI, Panfilov AV, Katsnelson LB, Solovyova O, Markhasin VS (2013) Mathematical model of the anatomy and fibre orientation field of the left ventricle of the heart. Biomed Eng. https://doi.org/10.1186/1475-925x-12-54

    Article  Google Scholar 

  11. Lopez-Perez A, Sebastian R, Ferrero JM (2015) Three-dimensional cardiac computational modelling: methods, features and applications. Biomed Eng. https://doi.org/10.1186/s12938-015-0033-5

    Article  Google Scholar 

  12. Helm PA, Tseng HJ, Younes L, McVeigh ER, Winslow RL (2005) Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure. Magn Reson Med 54(4):850–859. https://doi.org/10.1002/mrm.20622

    Article  PubMed  PubMed Central  Google Scholar 

  13. Geerts L, Bovendeerd P, Nicolay K, Arts T (2002) Characterization of the normal cardiac myofiber field in goat measured with MR-diffusion tensor imaging. Am J Physiol Heart Circ Physiol 283(1):H139–H145

    Article  CAS  PubMed  Google Scholar 

  14. Wu EX, Wu Y, Nicholls JM, Wang J, Liao S, Zhu S, Lau C-P, Tse H-F (2007) MR diffusion tensor imaging study of postinfarct myocardium structural remodeling in a porcine model. Magn Reson Med 58(4):687–695. https://doi.org/10.1002/mrm.21350

    Article  PubMed  Google Scholar 

  15. Strijkers GJ, Bouts A, Blankesteijn WM, Peeters THJM, Vilanova A, van Prooijen MC, Sanders HMHF, Heijman E, Nicolay K (2009) Diffusion tensor imaging of left ventricular remodeling in response to myocardial infarction in the mouse. NMR Biomed 22(2):182–190. https://doi.org/10.1002/nbm.1299

    Article  PubMed  Google Scholar 

  16. Yang F, Zhu YM, Rapacchi S, Luo JH, Robini M, Croisille P (2011) Interpolation of vector fields from human cardiac DT-MRI. Phys Med Biol 56(5):1415–1430. https://doi.org/10.1088/0031-9155/56/5/013

    Article  CAS  PubMed  Google Scholar 

  17. Dou JG, Tseng WYI, Reese TG, Wedeen VJ (2003) Combined diffusion and strain MRI reveals structure and function of human myocardial laminar sheets in vivo. Magn Reson Med 50(1):107–113. https://doi.org/10.1002/mrm.10482

    Article  PubMed  Google Scholar 

  18. Wu M-T, Tseng W-YI, Su M-YM, Liu C-P, Chiou K-R, Wedeen VJ, Reese TG, Yang C-F (2006) Diffusion tensor magnetic resonance imaging mapping the fiber architecture remodeling in human myocardium after infarction—correlation with viability and wall motion. Circulation 114(10):1036–1045. https://doi.org/10.1161/circulationhaha.105.545863

    Article  PubMed  Google Scholar 

  19. Nielles-Vallespin S, Mekkaoui C, Gatehouse P, Reese TG, Keegan J, Ferreira PF, Collins S, Speier P, Feiweier T, de Silva R, Jackowski MP, Pennell DJ, Sosnovik DE, Firmin D (2013) In vivo diffusion tensor MRI of the human heart: reproducibility of breath-hold and navigator-based approaches. Magn Reson Med 70(2):454–465. https://doi.org/10.1002/mrm.24488

    Article  PubMed  Google Scholar 

  20. Nguyen C, Fan Z, Sharif B, He Y, Dharmakumar R, Berman DS, Li D (2014) In vivo three-dimensional high resolution cardiac diffusion-weighted MRI: a motion compensated diffusion-prepared balanced steady-state free precession approach. Magn Reson Med 72(5):1257–1267. https://doi.org/10.1002/mrm.25038

    Article  PubMed  Google Scholar 

  21. Welsh CL, DiBella EVR, Hsu EW (2015) Higher-order motion-compensation for in vivo cardiac diffusion tensor imaging in rats. IEEE Trans Med Imaging 34(9):1843–1853. https://doi.org/10.1109/tmi.2015.2411571

    Article  PubMed  PubMed Central  Google Scholar 

  22. Dou JG, Reese TG, Tseng WYI, Wedeen VJ (2002) Cardiac diffusion MRI without motion effects. Magn Reson Med 48(1):105–114. https://doi.org/10.1002/mrm.10188

    Article  PubMed  Google Scholar 

  23. Wei H, Viallon M, Delattre BMA, Wang L, Pai VM, Wen H, Xue H, Guetter C, Croisille P, Zhu Y (2013) Assessment of cardiac motion effects on the fiber architecture of the human heart in vivo. IEEE Trans Med Imaging 32(10):1928–1938. https://doi.org/10.1109/tmi.2013.2269195

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wei H, Viallon M, Delattre BMA, Moulin K, Yang F, Croisille P, Zhu Y (2015) Free-breathing diffusion tensor imaging and tractography of the human heart in healthy volunteers using wavelet-based image fusion. IEEE Trans Med Imaging 34(1):306–316. https://doi.org/10.1109/tmi.2014.2356792

    Article  PubMed  Google Scholar 

  25. Holdsworth SJ, Skare S, Newbould RD, Bammer R (2009) Robust GRAPPA-accelerated diffusion-weighted readout-segmented (RS)-EPI. Magn Reson Med 62(6):1629–1640. https://doi.org/10.1002/mrm.22122

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bammer R, Auer M, Keeling SL, Augustin M, Stables LA, Prokesch RW, Stollberger R, Moseley ME, Fazekas F (2002) Diffusion tensor imaging using single-shot SENSE-EPI. Magn Reson Med 48(1):128–136. https://doi.org/10.1002/mrm.10184

    Article  PubMed  Google Scholar 

  27. Bammer R, Keeling SL, Augustin M, Pruessmann KP, Wolf R, Stollberger R, Hartung HP, Fazekas F (2001) Improved diffusion-weighted single-shot echo-planar imaging (EPI) in stroke using sensitivity encoding (SENSE). Magn Reson Med 46(3):548–554. https://doi.org/10.1002/mrm.1226

    Article  CAS  PubMed  Google Scholar 

  28. Jaermann T, Crelier G, Pruessmann KP, Golay X, Netsch T, van Muiswinkel AMC, Mori S, van Zijl PCM, Valavanis A, Kollias S, Boesiger P (2004) SENSE-DTI at 3 T. Magn Reson Med 51(2):230–236. https://doi.org/10.1002/mrm.10707

    Article  CAS  PubMed  Google Scholar 

  29. Larkman DJ, Nunes RG (2007) Parallel magnetic resonance imaging. Phys Med Biol 52(7):R15–R55. https://doi.org/10.1088/0031-9155/52/7/r01

    Article  PubMed  Google Scholar 

  30. Hsu EW, Henriquez CS (2001) Myocardial fiber orientation mapping using reduced encoding diffusion tensor imaging. J Cardiovasc Magn Reson 3(4):339–347. https://doi.org/10.1081/jcmr-100108588

    Article  CAS  PubMed  Google Scholar 

  31. Jiang Y, Hsu EW (2005) Accelerating MR diffusion tensor imaging via filtered reduced-encoding projection-reconstruction. Magn Reson Med 53(1):93–102. https://doi.org/10.1002/mrm.20311

    Article  PubMed  Google Scholar 

  32. Lau AZ, Tunnicliffe EM, Frost R, Koopmans PJ, Tyler DJ, Robson MD (2015) Accelerated human cardiac diffusion tensor imaging using simultaneous multislice imaging. Magn Reson Med 73(3):995–1004. https://doi.org/10.1002/mrm.25200

    Article  PubMed  Google Scholar 

  33. Taron J, Martirosian P, Schwenzer NF, Erb M, Kuestner T, Weiss J, Othman A, Notohamiprodjo M, Nikolaou K, Schraml C (2016) Scan time minimization in hepatic diffusion-weighted imaging: evaluation of the simultaneous multislice acceleration technique with different acceleration factors and gradient preparation schemes. Magn Reson Mater Phy Biol Med 29(5):739–749. https://doi.org/10.1007/s10334-016-0553-4

    Article  Google Scholar 

  34. Candes EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509. https://doi.org/10.1109/tit.2005.862083

    Article  Google Scholar 

  35. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306. https://doi.org/10.1109/tit.2006.871582

    Article  Google Scholar 

  36. Candes EJ, Tao T (2006) Near optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory 52(12):5406–5425. https://doi.org/10.1109/tit.2006.885507

    Article  Google Scholar 

  37. Graff CG, Sidky EY (2015) Compressive sensing in medical imaging. Appl Opt 54(8):C23–C44. https://doi.org/10.1364/ao.54.000c23

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wang G, Bresler Y, Ntziachristos V (2011) Compressive sensing for biomedical imaging. IEEE Trans Med Imaging 30(5):1013–1016. https://doi.org/10.1109/tmi.2011.2145070

    Article  PubMed  Google Scholar 

  39. Lustig M, Donoho DL, Santos JM, Pauly JM (2008) Compressed sensing MRI. IEEE Signal Process Mag 25(2):72–82. https://doi.org/10.1109/msp.2007.914728

    Article  Google Scholar 

  40. Hollingsworth KG (2015) Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. Phys Med Biol 60(21):R297–R322. https://doi.org/10.1088/0031-9155/60/21/r297

    Article  PubMed  Google Scholar 

  41. Chen S, Du H, Wu L, Jin J, Qiu B (2017) Compressed sensing MRI via fast linearized preconditioned alternating direction method of multipliers. Biomed Eng 16:5355. https://doi.org/10.1186/s12938-017-0343-x

    Article  Google Scholar 

  42. Ritschl L, Sawall S, Knaup M, Hess A, Kachelriess M (2012) Iterative 4D cardiac micro-CT image reconstruction using an adaptive spatio-temporal sparsity prior. Phys Med Biol 57(6):1517–1525. https://doi.org/10.1088/0031-9155/57/6/1517

    Article  PubMed  Google Scholar 

  43. Hu Z, Zheng H (2014) Improved total variation minimization method for few-view computed tomography image reconstruction. Biomed Eng Online. https://doi.org/10.1186/1475-925x-13-70

    Article  PubMed  PubMed Central  Google Scholar 

  44. Rigie DS, La Riviere PJ (2015) Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization. Phys Med Biol 60(5):1741–1762. https://doi.org/10.1088/0031-9155/60/5/1741

    Article  PubMed  PubMed Central  Google Scholar 

  45. Adluru G, Hsu E, Di Bella EVR (2007) Constrained reconstruction of sparse cardiac MR DTI data. In: paper presented at the functional imaging and modeling of the heart, proceedings, Salt Lake, USA, June

  46. Wu Y, Zhu Y-J, Tang Q-Y, Zou C, Liu W, Dai R-B, Liu X, Wu EX, Ying L, Liang D (2014) Accelerated MR diffusion tensor imaging using distributed compressed sensing. Magn Reson Med 71(2):763–772. https://doi.org/10.1002/mrm.24721

    Article  PubMed  Google Scholar 

  47. Shi X, Ma X, Wu W, Huang F, Yuan C, Guo H (2014) Parallel imaging and compressed sensing combined framework for accelerating high-resolution diffusion tensor imaging using inter-image correlation. Magn Reson Med 73:1775–1785

    Article  PubMed  Google Scholar 

  48. Hao Gao, Li L, Hu aX (2013) Compressive Diffusion MRI- Part 1 Why Low-Rank. In: paper presented at the proceedings of the 21th annual meeting of ISMRM, Salt Lake, April

  49. Ma S, Nguyen C, Christodoulou A, Luthringer D, Kobashigawa J, Lee S-E, Chang H-J, Li D (2017) Accelerated cardiac diffusion tensor imaging using joint low-rank and sparsity constraints. IEEE Trans Biomed Eng. https://doi.org/10.1109/tbme.2017.2787111

    Article  PubMed  Google Scholar 

  50. Gao H, Li L, Zhang K, Zhou W, Hu X (2014) PCLR: phase-constrained low-rank model for compressive diffusion-weighted MRI. Magn Reson Med 72(5):1330–1341. https://doi.org/10.1002/mrm.25052

    Article  PubMed  Google Scholar 

  51. Welsh CL, DiBella EVR, Adluru G, Hsu EW (2013) Model-based reconstruction of undersampled diffusion tensor k-space data. Magn Reson Med 70(2):429–440. https://doi.org/10.1002/mrm.24486

    Article  PubMed  Google Scholar 

  52. Zhu Y, Wu Y, Zheng Y, Wu EX, Ying L, Liang D (2012) A model-based method with joint sparsity constraint for direct diffusion tensor estimation. In: 2012 IEEE 9th international symposium on biomedical imaging:510–513. https://doi.org/10.1109/isbi.2012.6235597

  53. Dong Z, Dai E, Wang F, Zhang Z, Ma X, Yuan C, Guo H (2018) Model-based reconstruction for simultaneous multislice and parallel imaging accelerated multishot diffusion tensor imaging. Med Phys 45(7):3196–3204. https://doi.org/10.1002/mp.12974

    Article  PubMed  Google Scholar 

  54. Lugauer F, Nickel D, Wetzl J, Kiefer B, Hornegger J, Maier A (2017) Accelerating multi-echo water-fat MRI with a joint locally low-rank and spatial sparsity-promoting reconstruction. Magn Reson Mater Phy Biol Med 30(2):189–202. https://doi.org/10.1007/s10334-016-0595-7

    Article  CAS  Google Scholar 

  55. Trzasko J, Manduca A, Borisch E (2011) Local versus global low-rank promotion in dynamic MRI series reconstruction. In: paper presented at the Proc Int Symp Magn Reson Med

  56. Zhang T, Pauly JM, Levesque IR (2015) Accelerating parameter mapping with a locally low rank constraint. Magn Reson Med 73(2):655–661. https://doi.org/10.1002/mrm.25161

    Article  PubMed  Google Scholar 

  57. Huang J, Zhang S, Metaxas D (2011) Efficient MR image reconstruction for compressed MR imaging. Med Image Anal 15(5):670–679. https://doi.org/10.1016/j.media.2011.06.001

    Article  PubMed  Google Scholar 

  58. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202. https://doi.org/10.1137/080716542

    Article  Google Scholar 

  59. Helm PA, Younes L, Beg MF, Ennis DB, Leclercq C, Faris OP, McVeigh E, Kass D, Miller MI, Winslow RL (2006) Evidence of structural remodeling in the dyssynchronous failing heart. Circ Res 98(1):125–132. https://doi.org/10.1161/01.RES.0000199396.30688.eb

    Article  CAS  PubMed  Google Scholar 

  60. Helm P, Beg MF, Miller MI, Winslow RL (2005) Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging. Ann N Y Acad Sci 1047(1):296–307. https://doi.org/10.1196/annals.1341.026

    Article  PubMed  Google Scholar 

  61. Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195. https://doi.org/10.1002/mrm.21391

    Article  PubMed  Google Scholar 

  62. Tsai CM, Nishimura DG (2000) Reduced aliasing artifacts using variable-density k-space sampling trajectories. Magn Reson Med 43(3):452–458. https://doi.org/10.1002/(sici)1522-2594(200003)43:3%3c452:aid-mrm18%3e3.0.co;2-b

    Article  CAS  PubMed  Google Scholar 

  63. Streeter DD Jr, Spotnitz HM, Patel DP, Ross J Jr, Sonnenblick EH (1969) Fiber orientation in the canine left ventricle during diastole and systole. Circ Res 24(3):339–347

    Article  PubMed  Google Scholar 

  64. Carrillo RE, McEwen JD, Wiaux Y (2012) Sparsity averaging reweighted analysis (SARA): a novel algorithm for radio-interferometric imaging. Mon Not R Astron Soc 426(2):1223–1234. https://doi.org/10.1111/j.1365-2966.2012.21605.x

    Article  Google Scholar 

  65. Lagae A, Dutre P (2008) A comparison of methods for generating Poisson disk distributions. Comput Graphics Forum 27(1):114–129. https://doi.org/10.1111/j.1467-8659.2007.01100.x

    Article  Google Scholar 

  66. Marseille GJ, deBeer R, Fuderer M, Mehlkopf AF, vanOrmondt D (1996) Nonuniform phase-encode distributions for MRI scan time reduction. J Magn Reson Ser B 111(1):70–75. https://doi.org/10.1006/jmrb.1996.0061

    Article  CAS  Google Scholar 

  67. Mani M, Jacob M, Kelley D, Magnotta V (2017) Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS). Magn Reson Med 78(2):494–507. https://doi.org/10.1002/mrm.26382

    Article  CAS  PubMed  Google Scholar 

  68. Ong F, Cheng JY, Lustig M (2018) General phase regularized reconstruction using phase cycling. Magn Reson Med 80(1):112–125. https://doi.org/10.1002/mrm.27011

    Article  PubMed  Google Scholar 

  69. N-k Chen, Guidon A, Chang H-C, Song AW (2013) A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE). Neuroimage 72:41–47. https://doi.org/10.1016/j.neuroimage.2013.01.038

    Article  Google Scholar 

  70. Zhang Q, Coolen BF, Nederveen AJ, Strijkers GJ (2019) Three-dimensional diffusion imaging with spiral encoded navigators from stimulated echoes (3D-DISPENSE). Magn Reson Med 81(2):1052–1065. https://doi.org/10.1002/mrm.27470

    Article  PubMed  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (no. 61701105, 61661010, 61601057), the Natural Science Foundation of Heilongjiang Province of China (no. QC2017066), the Nature Science Foundation of Guizhou province (Qiankehe J No.20152044), the Project funded by China Postdoctoral Science Foundation (no. 2017M610199), and the Program PHC-Cai Yuanpei 2018 (no 41400TC).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Heilongjiang, 150040, Harbin, China

    Jianping Huang

  2. Metislab, LIA CNRS, Harbin Institute of Technology, Heilongjiang, 150001, Harbin, China

    Jianping Huang, Wanyu Liu & Yuemin Zhu

  3. Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China

    Lihui Wang

  4. College of Engineering, Bohai University, Jinzhou, 121013, China

    Chunyu Chu

  5. CREATIS, CNRS UMR5220, Inserm U1206, INSA Lyon, University of Lyon, Lyon, France

    Jianping Huang & Yuemin Zhu

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Contributions

The work presented in this paper corresponds to a collaborative development by all authors. JH and WL conceived and designed the experiments; JH and LW performed the numerical experiments; JH, LW, CC and YZ analyzed the experimental results; JH and YZ wrote the manuscript and improved this manuscript’s English language and style.

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Correspondence to Jianping Huang.

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This manuscript does not contain clinical studies or patient data. The DW data of the human hearts in this study from the website http://cvrgrid.org/data/exvivo.

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Huang, J., Wang, L., Chu, C. et al. Accelerating cardiac diffusion tensor imaging combining local low-rank and 3D TV constraint. Magn Reson Mater Phy 32, 407–422 (2019). https://doi.org/10.1007/s10334-019-00747-1

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