Quantitative Clinical Cardiac Magnetic Resonance Imaging

Ursula Reiter; Clemens Reiter; Corina Kräuter; Volha Nizhnikava; Michael H. Fuchsjäger; Gert Reiter

doi:10.1055/a-0999-5716

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Table of Contents

Rofo 2020; 192(03): 246-256
DOI: 10.1055/a-0999-5716

Review

Quantitative klinische Herz-Magnetresonanztomografie

Article in several languages: English | deutsch

Ursula Reiter

¹Radiology, Medical University of Graz, Austria

,

Clemens Reiter

¹Radiology, Medical University of Graz, Austria

,

Corina Kräuter

¹Radiology, Medical University of Graz, Austria

²Institute of Medical Engineering, Graz University of Technology, Faculty of Computer Science and Biomedical Engineering, Graz, Austria

,

Volha Nizhnikava

¹Radiology, Medical University of Graz, Austria

³Radiology, Respublican Science and Proctical Center of Cardiology, Minsk, Belarus

,

Michael H. Fuchsjäger

¹Radiology, Medical University of Graz, Austria

,

Gert Reiter

¹Radiology, Medical University of Graz, Austria

⁴Research and Development, Siemens Healthcare Diagnostics GmbH, Austria

› Author Affiliations

Abstract

Zusammenfassung

Hintergrund Die kardiale Magnetresonanztomografie (MRT) stellt sowohl in der Beurteilung der Herzfunktion als auch zur nichtinvasiven Gewebsanalyse des Myokards in vielen klinischen Fragestellungen die Referenz-Standard-Methode dar. Speziell die Quantifizierung kardialer Parameter nimmt eine immer zentralere diagnostische und differenzialdiagnostische Rolle ein. Im vorliegenden Review sollen etablierte und vielversprechende neue quantitative Herz-MRT-Parameter der klinischen Routine zusammengefasst, ihre Zusammenhänge beschrieben sowie ihre Abhängigkeiten von substanziellen Einflussfaktoren dargestellt werden.

Methode Die Übersichtsarbeit basiert auf einer PubMed-Literaturrecherche zu den Begriffen „cardiac magnetic resonance“ und „quantification“, „recommendations“, „quantitative evaluation/assessment“, „reference method“, „reference/normal values“, „pitfalls“ sowie „artifacts“ innerhalb des Publikationszeitraums 2000–2019.

Ergebnisse und Schlussfolgerung Funktionelle, Phasenkontrast- und Perfusionsbildgebung sowie Relaxationszeit-Kartierung ermöglichen die Erfassung einer Vielzahl quantitativer Herz-MRT-Parameter. Diese erlauben eine über die visuelle Beurteilung von Herz-MRT-Bildern hinausgehende Charakterisierung der Funktion, Morphologie und Perfusion des Herzens, sei es im Vergleich zu Normalwerten oder im Therapieverlauf. Bei der Interpretation ausgewerteter Herz-MRT-Parameter in der klinischen Routine muss allerdings zunehmend auf Standardisierung geachtet werden, da Aufnahmetechniken und Auswertealgorithmen quantitative Ergebnisse maßgeblich – jedoch mitunter nicht unmittelbar erkennbar – beeinflussen können.

Kernaussagen:

Die Routine-Herz-MRT erlaubt die Bestimmung einer Vielzahl funktioneller und morphologischer quantitativer Parameter.
Quantitative Herz-MRT-Parameter ermöglichen die Erfassung diffuser und globaler myokardialer Veränderungen.
Standardisierte Aufnahmetechniken und Auswertealgorithmen sind zentrale Voraussetzung zur diagnostischen Interpretation quantitativer Herz-MRT-Parameter.

Zitierweise

Reiter U, Reiter C, Kräuter C et al. Quantitative Clinical Cardiac Magnetic Resonance Imaging. Fortschr Röntgenstr 2020; 192: 246 – 256

Key words

heart - MR-imaging - cardiac function - myocardial relaxation time mapping - myocardial perfusion - pitfalls

Full Text

References

Literatur
1 Kramer CM, Barkhausen J, Flamm SD. et al. Society for Cardiovascular Magnetic Resonance Board of Trustees Task Force on Standardized Protocols. Standardized cardiovascular magnetic resonance (CMR) protocols 2013 update. J Cardiovasc Magn Reson 2013; 15: 91 . doi:10.1186/1532-429X-15-91
2 Puntmann VO, Valbuena S, Hinojar R. et al. Society for Cardiovascular Magnetic Resonance (SCMR) expert consensus for CMR imaging endpoints in clinical research: part I – analytical validation and clinical qualification. J Cardiovasc Magn Reson 2018; 20: 67 . doi:10.1186/s12968-018-0484-5
3 Peterzan MA, Rider OJ, Anderson LJ. The Role of Cardiovascular Magnetic Resonance Imaging in Heart Failure. Card Fail Rev 2016; 2: 115-122 . doi:10.15420/cfr.2016.2.2.115
4 Schulz-Menger J, Bluemke DA, Bremerich J. et al. Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing. J Cardiovasc Magn Reson 2013; 15: 35,429X-15-35 . doi:10.1186/1532-429X-15-35
5 Fratz S, Chung T, Greil GF. et al. Guidelines and protocols for cardiovascular magnetic resonance in children and adults with congenital heart disease: SCMR expert consensus group on congenital heart disease. J Cardiovasc Magn Reson 2013; 15: 51,429X-15-51 . doi:10.1186/1532-429X-15-51
6 Messroghli DR, Moon JC, Ferreira VM. et al. Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI). J Cardiovasc Magn Reson 2017; 19: 75,017-0389-8 . doi:10.1186/s12968-017-0408-9
7 Merz CN, Pepine CJ, Walsh MN. et al. Ischemia and No Obstructive Coronary Artery Disease (INOCA): Developing Evidence-based Therapies and Research Agenda for the Next Decade. Circulation 2017; 135: 1075-1092 . doi:10.1161/CIRCULATIONAHA.116.024534
8 Robinson AA, Salerno M, Kramer CM. Contemporary Issues in Quantitative Myocardial Perfusion CMR Imaging. Current Cardiovascular Imaging Reports 2019; 12: 9 . doi:10.1007/s12410-019-9484-6
9 Kawel-Boehm N, Maceira A, Valsangiacomo-Buechel ER. et al. Normal values for cardiovascular magnetic resonance in adults and children. J Cardiovasc Magn Reson 2015; 17: 29,015-0111-7 . doi:10.1186/s12968-015-0111-7
10 Ridgway JP. Cardiovascular magnetic resonance physics for clinicians: part I. J Cardiovasc Magn Reson 2010; 12: 71 . doi:10.1186/1532-429X-12-71
11 Biglands JD, Radjenovic A, Ridgway JP. Cardiovascular magnetic resonance physics for clinicians: Part II. J Cardiovasc Magn Reson 2012; 14: 66 . doi:10.1186/1532-429X-14-66
12 Krishnamurthy R, Cheong B, Muthupillai R. Tools for cardiovascular magnetic resonance imaging. Cardiovasc Diagn Ther 2014; 4: 104-125 . doi:10.3978/j.issn.2223-3652.2014.03.06
13 Ferreira PF, Gatehouse PD, Mohiaddin RH. et al. Cardiovascular magnetic resonance artefacts. J Cardiovasc Magn Reson 2013; 15: 41 . doi:10.1186/1532-429X-15-41
14 Finn JP, Nael K, Deshpande V. et al. Cardiac MR imaging: state of the technology. Radiology 2006; 241: 338-354
15 Olivieri LJ, Cross RR, O'Brien KE. et al. Optimized protocols for cardiac magnetic resonance imaging in patients with thoracic metallic implants. Pediatr Radiol 2015; 45: 1455-1464 . doi:10.1007/s00247-015-3366-0
16 Kido T, Kido T, Nakamura M. et al. Compressed sensing real-time cine cardiovascular magnetic resonance: accurate assessment of left ventricular function in a single-breath-hold. J Cardiovasc Magn Reson 2016; 18: 50,016-0271-0 . doi:10.1186/s12968-016-0271-0
17 Wood PW, Choy JB, Nanda NC. et al. Left ventricular ejection fraction and volumes: it depends on the imaging method. Echocardiography 2014; 31: 87-100 . doi:10.1111/echo.12331
18 Pellikka PA, She L, Holly TA. et al. Variability in Ejection Fraction Measured By Echocardiography, Gated Single-Photon Emission Computed Tomography, and Cardiac Magnetic Resonance in Patients With Coronary Artery Disease and Left Ventricular Dysfunction. JAMA Netw Open 2018; 1: e181456 . doi:10.1001/jamanetworkopen.2018.1456
19 Reiter G, Reiter U, Rienmüller R. et al. On the value of geometry-based models for left ventricular volumetry in magnetic resonance imaging and electron beam tomography: a Bland-Altman analysis. Eur J Radiol 2004; 52: 110-118
20 Kawaji K, Codella NC, Prince MR. et al. Automated segmentation of routine clinical cardiac magnetic resonance imaging for assessment of left ventricular diastolic dysfunction. Circ Cardiovasc Imaging 2009; 2: 476-484 . doi:10.1161/CIRCIMAGING.109.879304
21 Nacif MS, Almeida ALC, Young AA. et al. Three-Dimensional Volumetric Assessment of Diastolic Function by Cardiac Magnetic Resonance Imaging: The Multi-Ethnic Study of Atherosclerosis (MESA). Arq Bras Cardiol 2017; 108: 552-563 . doi:10.5935/abc.20170063
22 Kowallick JT, Morton G, Lamata P. et al. Quantification of atrial dynamics using cardiovascular magnetic resonance: inter-study reproducibility. J Cardiovasc Magn Reson 2015; 17: 36,015-0140-2 . doi:10.1186/s12968-015-0140-2
23 Contijoch F, Witschey WR, Rogers K. et al. User-initialized active contour segmentation and golden-angle real-time cardiovascular magnetic resonance enable accurate assessment of LV function in patients with sinus rhythm and arrhythmias. J Cardiovasc Magn Reson 2015; 17: 37 . doi:10.1186/s12968-015-0146-9
24 Contijoch F, Rogers K, Rears H. et al. Quantification of Left Ventricular Function With Premature Ventricular Complexes Reveals Variable Hemodynamics. Circ Arrhythm Electrophysiol 2016; 9: e003520 . doi:10.1161/CIRCEP.115.003520
25 Ibrahim el-SH. Myocardial tagging by cardiovascular magnetic resonance: evolution of techniques – pulse sequences, analysis algorithms, and applications. J Cardiovasc Magn Reson 2011; 13: 36,429X-13-36 . doi:10.1186/1532-429X-13-36
26 Augustine D, Lewandowski AJ, Lazdam M. et al. Global and regional left ventricular myocardial deformation measures by magnetic resonance feature tracking in healthy volunteers: comparison with tagging and relevance of gender. J Cardiovasc Magn Reson 2013; 15: 8,429X-15-8 . doi:10.1186/1532-429X-15-8
27 Almutairi HM, Boubertakh R, Miquel ME. et al. Myocardial deformation assessment using cardiovascular magnetic resonance-feature tracking technique. Br J Radiol 2017; 90: 20170072 . doi:10.1259/bjr.20170072
28 Kupfahl C, Honold M, Meinhardt G. et al. Evaluation of aortic stenosis by cardiovascular magnetic resonance imaging: comparison with established routine clinical techniques. Heart 2004; 90: 893-901 . doi:10.1136/hrt.2003.022376
29 Schlosser T, Malyar N, Jochims M. et al. Quantification of aortic valve stenosis in MRI-comparison of steady-state free precession and fast low-angle shot sequences. Eur Radiol 2007; 17: 1284-1290 . doi:10.1007/s00330-006-0437-5
30 Gatehouse PD, Keegan J, Crowe LA. et al. Applications of phase-contrast flow and velocity imaging in cardiovascular MRI. Eur Radiol 2005; 15: 2172-2184 . doi:10.1007/s00330-005-2829-3
31 Nayak KS, Nielsen JF, Bernstein MA. et al. Cardiovascular magnetic resonance phase contrast imaging. J Cardiovasc Magn Reson 2015; 17: 71,015-0172-7 . doi:10.1186/s12968-015-0172-7
32 Dyverfeldt P, Bissell M, Barker AJ. et al. 4D flow cardiovascular magnetic resonance consensus statement. J Cardiovasc Magn Reson 2015; 17: 72,015-0174-5 . doi:10.1186/s12968-015-0174-5
33 Gatehouse PD, Rolf MP, Graves MJ. et al. Flow measurement by cardiovascular magnetic resonance: a multi-centre multi-vendor study of background phase offset errors that can compromise the accuracy of derived regurgitant or shunt flow measurements. J Cardiovasc Magn Reson 2010; 12: 5,429X-12-5 . doi:10.1186/1532-429X-12-5
34 Aquaro GD, Barison A, Todiere G. et al. Cardiac magnetic resonance “virtual catheterization” for the quantification of valvular regurgitations and cardiac shunt. J Cardiovasc Med (Hagerstown) 2015; 16: 663-670 . doi:10.2459/JCM.0000000000000245
35 Krieger EV, Lee J, Branch KR. et al. Quantitation of mitral regurgitation with cardiac magnetic resonance imaging: a systematic review. Heart 2016; 102: 1864-1870 . doi:10.1136/heartjnl-2015-309054
36 Defrance C, Bollache E, Kachenoura N. et al. Evaluation of aortic valve stenosis using cardiovascular magnetic resonance: comparison of an original semiautomated analysis of phase-contrast cardiovascular magnetic resonance with Doppler echocardiography. Circ Cardiovasc Imaging 2012; 5: 604-612 . doi:10.1161/CIRCIMAGING.111.971218
37 Bollache E, Redheuil A, Clement-Guinaudeau S. et al. Automated left ventricular diastolic function evaluation from phase-contrast cardiovascular magnetic resonance and comparison with Doppler echocardiography. J Cardiovasc Magn Reson 2010; 12: 63,429X-12-63 . doi:10.1186/1532-429X-12-63
38 Ashrafpoor G, Bollache E, Redheuil A. et al. Age-specific changes in left ventricular diastolic function: a velocity-encoded magnetic resonance imaging study. Eur Radiol 2015; 25: 1077-1086 . doi:10.1007/s00330-014-3488-z
39 Buss SJ, Krautz B, Schnackenburg B. et al. Classification of diastolic function with phase-contrast cardiac magnetic resonance imaging: validation with echocardiography and age-related reference values. Clin Res Cardiol 2014; 103: 441-450 . doi:10.1007/s00392-014-0669-3
40 Jerosch-Herold M. Quantification of myocardial perfusion by cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2010; 12: 57 . doi:10.1186/1532-429X-12-57
41 Klem I, Heitner JF, Shah DJ. et al. Improved detection of coronary artery disease by stress perfusion cardiovascular magnetic resonance with the use of delayed enhancement infarction imaging. J Am Coll Cardiol 2006; 47: 1630-1638 . doi:10.1016/j.jacc.2005.10.074
42 Thomson LE, Fieno DS, Abidov A. et al. Added value of rest to stress study for recognition of artifacts in perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2007; 9: 733-740 . doi:10.1080/10976640701544415
43 Zorach B, Shaw PW, Bourque J. et al. Quantitative cardiovascular magnetic resonance perfusion imaging identifies reduced flow reserve in microvascular coronary artery disease. J Cardiovasc Magn Reson 2018; 20: 14,018-0435-1 . doi:10.1186/s12968-018-0435-1
44 Kellman P, Hansen MS, Nielles-Vallespin S. et al. Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification. J Cardiovasc Magn Reson 2017; 19: 43,017-0355-5 . doi:10.1186/s12968-017-0355-5
45 Watkins S, McGeoch R, Lyne J. et al. Validation of magnetic resonance myocardial perfusion imaging with fractional flow reserve for the detection of significant coronary heart disease. Circulation 2009; 120: 2207-2213 . doi:10.1161/CIRCULATIONAHA.109.872358
46 Mordini FE, Haddad T, Hsu LY. et al. Diagnostic accuracy of stress perfusion CMR in comparison with quantitative coronary angiography: fully quantitative, semiquantitative, and qualitative assessment. JACC Cardiovasc Imaging 2014; 7: 14-22 . doi:10.1016/j.jcmg.2013.08.014
47 Handayani A, Sijens PE, Lubbers DD. et al. Influence of the choice of software package on the outcome of semiquantitative MR myocardial perfusion analysis. Radiology 2013; 266: 759-765 . doi:10.1148/radiol.12120626
48 van Dijk R, van Assen M, Vliegenthart R. et al. Diagnostic performance of semi-quantitative and quantitative stress CMR perfusion analysis: a meta-analysis. J Cardiovasc Magn Reson 2017; 19: 92 . doi:10.1186/s12968-017-0393-z
49 Kellman P, Hansen MS. T1-mapping in the heart: accuracy and precision. J Cardiovasc Magn Reson 2014; 16: 2,429X-16-2 . doi:10.1186/s12968-015-0136-y
50 Reiter G, Reiter C, Krauter C. et al. Cardiac magnetic resonance T1 mapping. Part 1: Aspects of acquisition and evaluation. Eur J Radiol 2018; 109: 223-234 . doi:10.1016/j.ejrad.2018.10.011
51 Kim PK, Hong YJ, Im DJ. et al. Myocardial T1 and T2 Mapping: Techniques and Clinical Applications. Korean J Radiol 2017; 18: 113-131 . doi:10.3348/kjr.2017.18.1.113
52 Lota AS, Gatehouse PD, Mohiaddin RH. T2 mapping and T2* imaging in heart failure. Heart Fail Rev 2017; 22: 431-440 . doi:10.1007/s10741-017-9616-5
53 Reiter U, Reiter C, Krauter C. et al. Cardiac magnetic resonance T1 mapping. Part 2: Diagnostic potential and applications. Eur J Radiol 2018; 109: 235-247 . doi:10.1016/j.ejrad.2018.10.013
54 Kellman P, Wilson JR, Xue H. et al. Extracellular volume fraction mapping in the myocardium, part 1: evaluation of an automated method. J Cardiovasc Magn Reson 2012; 14: 63,429X-14-63 . doi:10.1186/1532-429X-14-63
55 Ferreira VM, Schulz-Menger J, Holmvang G. et al. Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation: Expert Recommendations. J Am Coll Cardiol 2018; 72: 3158-3176 . doi:10.1016/j.jacc.2018.09.072
56 Diao KY, Yang ZG, Xu HY. et al. Histologic validation of myocardial fibrosis measured by T1 mapping: a systematic review and meta-analysis. J Cardiovasc Magn Reson 2016; 18: 92,016-0313-7 . doi:10.1186/s12968-016-0313-7
57 Piechnik SK, Ferreira VM, Lewandowski AJ. et al. Normal variation of magnetic resonance T1 relaxation times in the human population at 1.5 T using ShMOLLI. J Cardiovasc Magn Reson 2013; 15: 13 . doi:10.1186/1532-429X-15-13
58 Kellman P, Bandettini WP, Mancini C. et al. Characterization of myocardial T1-mapping bias caused by intramyocardial fat in inversion recovery and saturation recovery techniques. J Cardiovasc Magn Reson 2015; 17: 33 . doi:10.1186/s12968-015-0136-y
59 Wood JC. Cardiac iron across different transfusion-dependent diseases. Blood Rev 2008; 22 (Suppl. 02) S14-S21 . doi:10.1016/S0268-960X(08)70004-3
60 Carpenter JP, He T, Kirk P. et al. Calibration of myocardial T2 and T1 against iron concentration. J Cardiovasc Magn Reson 2014; 16: 62,014-0062-4 . doi:10.1186/s12968-014-0062-4
61 Kritsaneepaiboon S, Ina N, Chotsampancharoen T. et al. The relationship between myocardial and hepatic T2 and T2* at 1.5T and 3T MRI in normal and iron-overloaded patients. Acta Radiol 2018; 59: 355-362 . doi:10.1177/0284185117715285