Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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Biases in the Assessment of Left Ventricular Function by Compressed Sensing Cardiovascular Cine MRI

  • Yoon, Jong-Hyun (Department of Electrical Engineering, Kwangwoon University) ;
  • Kim, Pan-ki (Department of Radiology, Severance Hospital, Yonsei University) ;
  • Yang, Young-Joong (Department of Electrical Engineering, Kwangwoon University) ;
  • Park, Jinho (Developing Brain Research Laboratory, Children's National Health System) ;
  • Choi, Byoung Wook (Department of Radiology, Severance Hospital, Yonsei University) ;
  • Ahn, Chang-Beom (Department of Electrical Engineering, Kwangwoon University)
  • Received : 2019.02.08
  • Accepted : 2019.04.16
  • Published : 2019.06.30
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Abstract

Purpose: We investigate biases in the assessments of left ventricular function (LVF), by compressed sensing (CS)-cine magnetic resonance imaging (MRI). Materials and Methods: Cardiovascular cine images with short axis view, were obtained for 8 volunteers without CS. LVFs were assessed with subsampled data, with compression factors (CF) of 2, 3, 4, and 8. A semi-automatic segmentation program was used, for the assessment. The assessments by 3 CS methods (ITSC, FOCUSS, and view sharing (VS)), were compared to those without CS. Bland-Altman analysis and paired t-test were used, for comparison. In addition, real-time CS-cine imaging was also performed, with CF of 2, 3, 4, and 8 for the same volunteers. Assessments of LVF were similarly made, for CS data. A fixed compensation technique is suggested, to reduce the bias. Results: The assessment of LVF by CS-cine, includes bias and random noise. Bias appeared much larger than random noise. Median of end-diastolic volume (EDV) with CS-cine (ITSC or FOCUSS) appeared -1.4% to -7.1% smaller, compared to that of standard cine, depending on CF from (2 to 8). End-systolic volume (ESV) appeared +1.6% to +14.3% larger, stroke volume (SV), -2.4% to -16.4% smaller, and ejection fraction (EF), -1.1% to -9.2% smaller, with P < 0.05. Bias was reduced from -5.6% to -1.8% for EF, by compensation applied to real-time CS-cine (CF = 8). Conclusion: Loss of temporal resolution by adopting missing data from nearby cardiac frames, causes an underestimation for EDV, and an overestimation for ESV, resulting in underestimations for SV and EF. The bias is not random. Thus it should be removed or reduced for better diagnosis. A fixed compensation is suggested, to reduce bias in the assessment of LVF.

Keywords

References

  1. Lamb HJ, Doornbos J, van der Velde EA, Kruit MC, Reiber JH, de Roos A. Echo planar MRI of the heart on a standard system: validation of measurements of left ventricular function and mass. J Comput Assist Tomogr 1996;20:942-949 https://doi.org/10.1097/00004728-199611000-00014
  2. Epstein FH. MRI of left ventricular function. J Nucl Cardiol 2007;14:729-744 https://doi.org/10.1016/j.nuclcard.2007.07.006
  3. Donoho DL. Compressed sensing. IEEE Trans Inf Theory 2006;52:1289-1306 https://doi.org/10.1109/TIT.2006.871582
  4. Baraniuk RG. Compressive sensing [lecture notes]. IEEE Signal Process Mag 2007;24:118-124 https://doi.org/10.1109/MSP.2007.4286571
  5. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182-1195 https://doi.org/10.1002/mrm.21391
  6. Candes EJ, Wakin MB. An introduction to compressive sampling. IEEE Signal Process Mag 2008;25:21-30 https://doi.org/10.1109/MSP.2007.914731
  7. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999;42:952-962 https://doi.org/10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S
  8. Liang D, Liu B, Wang J, Ying L. Accelerating SENSE using compressed sensing. Magn Reson Med 2009;62:1574-1584 https://doi.org/10.1002/mrm.22161
  9. Otazo R, Kim D, Axel L, Sodickson DK. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med 2010;64:767-776 https://doi.org/10.1002/mrm.22463
  10. Feng L, Grimm R, Block KT, et al. Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 2014;72:707-717 https://doi.org/10.1002/mrm.24980
  11. Benkert T, Feng L, Sodickson DK, Chandarana H, Block KT. Free-breathing volumetric fat/water separation by combining radial sampling, compressed sensing, and parallel imaging. Magn Reson Med 2017;78:565-576 https://doi.org/10.1002/mrm.26392
  12. Park J, Hong HJ, Yang YJ, Ahn CB. Fast cardiac CINE MRI by iterative truncation of small transformed coefficients. Investig Magn Reson Imaging 2015;19:19-30 https://doi.org/10.13104/imri.2015.19.1.19
  13. Aurigemma G, Reichek N, Schiebler M, Axel L. Evaluation of aortic regurgitation by cardiac cine magnetic resonance imaging: planar analysis and comparison to Doppler echocardiography. Cardiology 1991;78:340-347 https://doi.org/10.1159/000174815
  14. Rodevan O, Bjornerheim R, Ljosland M, Maehle J, Smith HJ, Ihlen H. Left atrial volumes assessed by three- and twodimensional echocardiography compared to MRI estimates. Int J Card Imaging 1999;15:397-410 https://doi.org/10.1023/A:1006276513186
  15. Bak SH, Kim SM, Park S, Kim M, Choe YH. Assessment of left ventricular function with single breath-hold magnetic resonance cine imaging in patients with arrhythmia. Investig Magn Reson Imaging 2017;21:20-27 https://doi.org/10.13104/imri.2017.21.1.20
  16. Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal 2017;35:159-171 https://doi.org/10.1016/j.media.2016.05.009
  17. White HD, Norris RM, Brown MA, Brandt PW, Whitlock RM, Wild CJ. Left ventricular end-systolic volume as the major determinant of survival after recovery from myocardial infarction. Circulation 1987;76:44-51 https://doi.org/10.1161/01.CIR.76.1.44
  18. Lorenz CH, Walker ES, Morgan VL, Klein SS, Graham TP, Jr. Normal human right and left ventricular mass, systolic function, and gender differences by cine magnetic resonance imaging. J Cardiovasc Magn Reson 1999;1:7-21 https://doi.org/10.3109/10976649909080829
  19. Wu E, Ortiz JT, Tejedor P, et al. Infarct size by contrast enhanced cardiac magnetic resonance is a stronger predictor of outcomes than left ventricular ejection fraction or end-systolic volume index: prospective cohort study. Heart 2008;94:730-736 https://doi.org/10.1136/hrt.2007.122622
  20. Muthurangu V, Lurz P, Critchely JD, Deanfield JE, Taylor AM, Hansen MS. Real-time assessment of right and left ventricular volumes and function in patients with congenital heart disease by using high spatiotemporal resolution radial k-t SENSE. Radiology 2008;248:782-791 https://doi.org/10.1148/radiol.2482071717
  21. Miller S, Simonetti OP, Carr J, Kramer U, Finn JP. MR Imaging of the heart with cine true fast imaging with steady-state precession: influence of spatial and temporal resolutions on left ventricular functional parameters. Radiology 2002;223:263-269 https://doi.org/10.1148/radiol.2231010235
  22. Hsiao A, Lustig M, Alley MT, et al. Rapid pediatric cardiac assessment of flow and ventricular volume with compressed sensing parallel imaging volumetric cine phasecontrast MRI. AJR Am J Roentgenol 2012;198:W250-259 https://doi.org/10.2214/AJR.11.6969
  23. Goebel J, Nensa F, Schemuth HP, et al. Compressed sensing cine imaging with high spatial or high temporal resolution for analysis of left ventricular function. J Magn Reson Imaging 2016;44:366-374 https://doi.org/10.1002/jmri.25162
  24. Vincenti G, Monney P, Chaptinel J, et al. Compressed sensing single-breath-hold CMR for fast quantification of LV function, volumes, and mass. JACC Cardiovasc Imaging 2014;7:882-892 https://doi.org/10.1016/j.jcmg.2014.04.016
  25. Bassett EC, Kholmovski EG, Wilson BD, et al. Evaluation of highly accelerated real-time cardiac cine MRI in tachycardia. NMR Biomed 2014;27:175-182 https://doi.org/10.1002/nbm.3049
  26. Lin ACW, Strugnell W, Riley R, et al. Higher resolution cine imaging with compressed sensing for accelerated clinical left ventricular evaluation. J Magn Reson Imaging 2017;45:1693-1699 https://doi.org/10.1002/jmri.25525
  27. Kido T, Kido T, Nakamura M, et al. Compressed sensing realtime cine cardiovascular magnetic resonance: accurate assessment of left ventricular function in a single-breath-hold. J Cardiovasc Magn Reson 2016;18:50 https://doi.org/10.1186/s12968-016-0271-0
  28. Bluemke DA, Boxerman JL, Atalar E, McVeigh ER. Segmented K-space cine breath-hold cardiovascular MR imaging: Part 1. Principles and technique. AJR Am J Roentgenol 1997;169:395-400 https://doi.org/10.2214/ajr.169.2.9242742
  29. Scheffler K, Lehnhardt S. Principles and applications of balanced SSFP techniques. Eur Radiol 2003;13:2409-2418 https://doi.org/10.1007/s00330-003-1957-x
  30. Jung H, Sung K, Nayak KS, Kim EY, Ye JC. k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med 2009;61:103-116 https://doi.org/10.1002/mrm.21757
  31. Markl M, Hennig J. Phase contrast MRI with improved temporal resolution by view sharing: k-space related velocity mapping properties. Magn Reson Imaging 2001;19:669-676 https://doi.org/10.1016/S0730-725X(01)00386-1
  32. Lang RM, Bierig M, Devereux RB, et al. Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. J Am Soc Echocardiogr 2005;18:1440-1463 https://doi.org/10.1016/j.echo.2005.10.005
  33. Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging 2015;16:233-270 https://doi.org/10.1093/ehjci/jev014

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