Skip to main content
SpringerLink
Log in

Adaptive anisotropic gaussian filtering to reduce acquisition time in cardiac diffusion tensor imaging

  • Original Paper
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

Diffusion tensor imaging (DTI) is used to quantify myocardial fiber orientation based on helical angles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). However, increasing NEX and/or DED increases acquisition time (TA). Therefore, in this study, we propose to reduce TA by implementing a 3D adaptive anisotropic Gaussian filter (AAGF) on the DTI data acquired from ex-vivo healthy and infarcted porcine hearts. DTI was performed on ex-vivo hearts [9-healthy, 3-myocardial infarction (MI)] with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on the primary eigenvectors of the diffusion tensor prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland–Altman’s technique was used to determine optimal combination of DED and NEX that generated the best HA maps in the least possible TA. Lastly, the effect of implementing AAGF on the infarcted porcine hearts was also investigated. RMSE in HA estimation for AAGF was lower compared to AVF or MF. Post-filtering (AAGF) fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Pathological alterations caused in HA orientation in the MI model were preserved post-filtering (AAGF). Our results demonstrate that AAGF reduces TA without affecting the integrity of the myocardial microstructure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Hunter PJ, Pullan AJ, Smaill BH (2003) Modeling total heart function. Annu Rev Biomed Eng 5:147–177

    Article  CAS  PubMed  Google Scholar 

  2. Kanai A, Salama G (1995) Optical mapping reveals that repolarization spreads anisotropically and is guided by fiber orientation in guinea pig hearts. Circ Res 77(4):784–802

    Article  CAS  PubMed  Google Scholar 

  3. Taccardi B, Lux RL, Ershler PR et al (1997) Anatomical architecture and electrical activity of the heart. Acta Cardiol 52(2):91–105

    CAS  PubMed  Google Scholar 

  4. LeGrice IJ, Takayama Y, Covell JW (1995) Transverse shear along myocardial cleavage planes provides a mechanism for normal systolic wall thickening. Circ Res 77(1):182–193

    Article  CAS  PubMed  Google Scholar 

  5. Waldman LK, Nosan D, Villarreal F, Covell JW (1988) Relation between transmural deformation and local myofiber direction in canine left ventricle. Circ Res 63(3):550–562

    Article  CAS  PubMed  Google Scholar 

  6. Cutler MJ, Jeyaraj D, Rosenbaum DS (2011) Cardiac electrical remodeling in health and disease. Trends Pharmacol Sci 32(3):174–180

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Weisman HF, Bush DE, Mannisi JA, Bulkley BH (1985) Global cardiac remodeling after acute myocardial infarction: a study in the rat model. J Am Coll Cardiol 5(6):1355–1362

    Article  CAS  PubMed  Google Scholar 

  8. Tezuka F (1975) Muscle-fiber orientation in normal and hypertrophied hearts. Tohoku J Exp Med 117(3):289–297

    Article  CAS  PubMed  Google Scholar 

  9. Scollan DF, Holmes A, Winslow R, Forder J (1998) Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging. Am J Physiol 275(6 Pt 2):H2308–H2318

    CAS  PubMed  Google Scholar 

  10. 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 

  11. Wu MT, Tseng WY, Su MY et al (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

    Article  PubMed  Google Scholar 

  12. Tseng WY, Dou J, Reese TG, Wedeen VJ (2006) Imaging myocardial fiber disarray and intramural strain hypokinesis in hypertrophic cardiomyopathy with MRI. J Magn Reson Imaging 23(1):1–8

    Article  PubMed  Google Scholar 

  13. Qin X, Wang S, Shen M et al (2015) Simulating cardiac ultrasound image based on MR diffusion tensor imaging. Med Phys 42(9):5144–5156

    Article  PubMed  Google Scholar 

  14. Wu Y, Zou C, Liu W et al (2013) Effect of B-value in revealing postinfarct myocardial microstructural remodeling using MR diffusion tensor imaging. Magn Reson Imaging 31(6):847–856

    Article  PubMed  Google Scholar 

  15. Wu Y, Zhang LJ, Zou C, Tse HF, Wu EX (2011) Transmural heterogeneity of left ventricular myocardium remodeling in postinfarct porcine model revealed by MR diffusion tensor imaging. J Magn Reson Imaging 34(1):43–49

    Article  CAS  PubMed  Google Scholar 

  16. Mekkaoui C, Huang S, Chen HH et al (2012) Fiber architecture in remodeled myocardium revealed with a quantitative diffusion CMR tractography framework and histological validation. J Cardiovasc Magn Reson 14:70

    Article  PubMed  PubMed Central  Google Scholar 

  17. Martin-Fernandez M, Munoz-Moreno E, Cammoun L et al (2009) Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data. Med Image Anal 13(1):19–35

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hamarneh G, Hradsky J (2006) Bilateral filtering of diffusion tensor MR images. In: 2006 IEEE international symposium on signal processing and information technology, Vols 1 and 2, pp. 507–512

  19. Basu S, Fletcher T, Whitaker R (2006) Rician noise removal in diffusion tensor MRI. Medical image computing and computer-assisted intervention—Miccai 2006, Pt 1, 2006. 4190 pp. 117–125

  20. Pizurica A, Philips W, Lemahieu I, Acheroy M (2003) A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans Med Imaging 22(3):323–331

    Article  PubMed  Google Scholar 

  21. Parker GJ, Schnabel JA, Symms MR, Werring DJ, Barker GJ (2000) Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging. J Magn Reson Imaging 11(6):702–710

    Article  CAS  PubMed  Google Scholar 

  22. Chen B, Hsu EW (2005) Noise removal in magnetic resonance diffusion tensor imaging. Magn Reson Med 54(2):393–401

    Article  PubMed  Google Scholar 

  23. Castano-Moraga CA, Lenglet C, Deriche R, Ruiz-Alzola J (2007) A Riemannian approach to anisotropic filtering of tensor fields. Signal Process 87(2):263–276

    Article  Google Scholar 

  24. McGraw T, Vemuri BC, Chen Y, Rao M, Mareci T (2004) DT-MRI denoising and neuronal fiber tracking. Med Image Anal 8(2):95–111

    Article  CAS  PubMed  Google Scholar 

  25. Bai H, Gao Y, Wang S, Luo L (2008) A robust diffusion tensor estimation method for DTI. J Comput Res Dev 45:1232–1238

    Google Scholar 

  26. Liu M, Vemuri BC, Deriche R (2013) A robust variational approach for simultaneous smoothing and estimation of DTI. Neuroimage 67:33–41

    Article  PubMed  PubMed Central  Google Scholar 

  27. Pennec X, Fillard P, Ayache N (2006) A Riemannian framework for tensor computing. Int J Comput Vision 66(1):41–66

    Article  Google Scholar 

  28. Fillard P, Pennec X, Arsigny V, Ayache N (2007) Clinical DT-MRI estimation, smoothing, and fiber tracking with log-Euclidean metrics. IEEE Trans Med Imaging 26(11):1472–1482

    Article  PubMed  Google Scholar 

  29. Kim W, Jeong MH, Sim DS et al (2011) A porcine model of ischemic heart failure produced by intracoronary injection of ethyl alcohol. Heart Vessels 26(3):342–348

    Article  PubMed  Google Scholar 

  30. Holmes JW, Borg TK, Covell JW (2005) Structure and mechanics of healing myocardial infarcts. Annu Rev Biomed Eng 7:223–253

    Article  CAS  PubMed  Google Scholar 

  31. Lin LI (1989) A concordance correlation-coefficient to evaluate reproducibility. Biometrics 45(1):255–268

    Article  CAS  PubMed  Google Scholar 

  32. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310

    Article  CAS  PubMed  Google Scholar 

  33. Kocica MJ, Corno AF, Carreras-Costa F et al (2006) The helical ventricular myocardial band: global, three-dimensional, functional architecture of the ventricular myocardium. Eur J Cardio Thorac 29:S21–S40

    Article  Google Scholar 

  34. Ross DN (2006) Torrent-Guasp’s anatomical legacy. Eur J Cardiothorac Surg 29(Suppl 1):S18–S20

    Article  PubMed  Google Scholar 

  35. Jones DK (2004) The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn Reson Med 51(4):807–815

    Article  PubMed  Google Scholar 

  36. Nielles-Vallespin S, Mekkaoui C, Gatehouse P et al (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

    Article  PubMed  Google Scholar 

  37. Lau AZ, Tunnicliffe EM, Frost R et al (2015) Accelerated human cardiac diffusion tensor imaging using simultaneous multislice imaging. Magn Reson Med 73(3):995–1004

    Article  PubMed  Google Scholar 

  38. Gamper U, Boesiger P, Kozerke S (2007) Diffusion imaging of the in vivo heart using spin echoes–considerations on bulk motion sensitivity. Magn Reson Med 57(2):331–337

    Article  PubMed  Google Scholar 

  39. Nguyen C, Fan Z, Sharif B et al (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

    Article  PubMed  Google Scholar 

  40. Wu EX, Wu Y, Nicholls JM et al (2007) MR diffusion tensor imaging study of postinfarct myocardium structural remodeling in a porcine model. Magn Reson Med 58(4):687–695

    Article  PubMed  Google Scholar 

  41. Smerup M, Agger P, Nielsen EA et al (2013) Regional and epi- to endocardial differences in transmural angles of left ventricular cardiomyocytes measured in ex vivo pig hearts: functional implications. Anat Rec (Hoboken) 296(11):1724–1734

    Article  Google Scholar 

  42. Crawford GN, Barer R (1951) The action of formaldehyde on living cells as studied by phase-contrast microscopy. J Microscopical Sci 92(4):403–452

    CAS  Google Scholar 

Download references

Acknowledgments

The authors thank DHLRI Interventional Cardiology Catheterization Core Lab and Joseph Matthew for their help in preparing the animal models. We also thank Siemens Healthcare for supporting this project by providing us with the required pulse sequence.

Funding

This manuscript has been supported by Grant sponsor: American Heart Association; Grant Number: 13SDG14690027; Grant Sponsor: Center for Clinical and Translational Sciences; Grant Number: UL1TR000090; Grant Sponsor: NIH–NHLBI; Grant Number: NIH-R01HL24096.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The Ohio State University, 205 Dreese Laboratories, 2015 Neil Avenue, Columbus, OH, 43210, USA

    Ria Mazumder & Bradley D. Clymer

  2. Department of Radiology, The Ohio State University, Room 460, 395 West 12th Avenue, 4th Floor, Columbus, OH, 43210, USA

    Ria Mazumder, Richard D. White & Arunark Kolipaka

  3. Department of Biomedical Informatics, Center for Biostatistics, Room 320D, Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA

    Xiaokui Mo

  4. Division of Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University, 244 Davis Heart and Lung Research Institute, 473 W. 12th Avenue, Columbus, OH, 43210, USA

    Richard D. White & Arunark Kolipaka

Authors
  1. Ria Mazumder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bradley D. Clymer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaokui Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard D. White
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arunark Kolipaka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arunark Kolipaka.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to disclose.

Ethical approval

All animal procedures were performed in accordance with the university’s institutional animal care and use committee guidelines.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mazumder, R., Clymer, B.D., Mo, X. et al. Adaptive anisotropic gaussian filtering to reduce acquisition time in cardiac diffusion tensor imaging. Int J Cardiovasc Imaging 32, 921–934 (2016). https://doi.org/10.1007/s10554-016-0848-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-016-0848-6

Keywords

Search

Navigation