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Current Cardiovascular Imaging Reports

Erschienen in:

01.01.2018 | Cardiac Nuclear Imaging (A Cuocolo and M Petretta, Section Editors)

New Trends in Quantitative Nuclear Cardiology Methods

verfasst von: Javier Gomez, Rami Doukky, Guido Germano, Piotr Slomka

Erschienen in: Current Cardiovascular Imaging Reports | Ausgabe 1/2018

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Abstract

Purpose of Review

The use of quantitative analysis in single photon emission computed tomography (SPECT) and positron emission tomography (PET) has become an integral part of current clinical practice and plays a crucial role in the detection and risk stratification of coronary artery disease. Emerging technologies, new protocols, and new quantification methods have had a significant impact on the diagnostic performance and prognostic value of nuclear cardiology imaging while reducing the need for clinician oversight. In this review, we aim to describe recent advances in automation and quantitative analysis in nuclear cardiology.

Recent Findings

Recent publications have shown that fully automatic processing is feasible, limiting human input to specific cases where aberrancies are detected by the quality control software. Furthermore, there is evidence indicating that fully quantitative analysis of myocardial perfusion imaging is feasible and can achieve at least similar diagnostic accuracy as visual interpretation by an expert clinician. In addition, the use of fully automated quantification in combination with machine learning algorithms can provide incremental diagnostic and prognostic value over the traditional method of expert visual interpretation.

Summary

Emerging technologies in nuclear cardiology focus on automation and the use of artificial intelligence as part of the interpretation process. This review highlights the benefits and limitations of these applications and outlines future directions in the field.

Salerno M, Beller GA. Noninvasive assessment of myocardial perfusion. Circ Cardiovasc Imaging. 2009;2(5):412–24. https://doi.org/10.1161/CIRCIMAGING.109.854893.PubMedCrossRef

Shaw LJ, Iskandrian AE. Prognostic value of gated myocardial perfusion SPECT. J Nucl Cardiol. 2004;11(2):171–85. https://doi.org/10.1016/j.nuclcard.2003.12.004.PubMedCrossRef

Dorbala S, Vangala D, Sampson U, Limaye A, Kwong R, Di Carli MF. Value of vasodilator left ventricular ejection fraction reserve in evaluating the magnitude of myocardium at risk and the extent of angiographic coronary artery disease: a 82Rb PET/CT study. J Nucl Med. 2007;48(3):349–58.PubMed

Yoshinaga K, Chow BJ, Williams K, Chen L, deKemp RA, Garrard L, et al. What is the prognostic value of myocardial perfusion imaging using rubidium-82 positron emission tomography? J Am Coll Cardiol. 2006;48(5):1029–39. https://doi.org/10.1016/j.jacc.2006.06.025.PubMedCrossRef

Berman DS, Kang X, Gransar H, Gerlach J, Friedman JD, Hayes SW, et al. Quantitative assessment of myocardial perfusion abnormality on SPECT myocardial perfusion imaging is more reproducible than expert visual analysis. J Nucl Cardiol. 2009;16(1):45–53. https://doi.org/10.1007/s12350-008-9018-0.PubMedPubMedCentralCrossRef

Iskandrian AE, Garcia EV, Faber T, Mahmarian JJ. Automated assessment of serial SPECT myocardial perfusion images. J Nucl Cardiol. 2009;16(1):6–9. https://doi.org/10.1007/s12350-008-9020-6.PubMedCrossRef

Arsanjani R, Xu Y, Hayes SW, Fish M, Lemley M Jr, Gerlach J, et al. Comparison of fully automated computer analysis and visual scoring for detection of coronary artery disease from myocardial perfusion SPECT in a large population. J Nucl Med. 2013;54(2):221–8. https://doi.org/10.2967/jnumed.112.108969.PubMedPubMedCentralCrossRef

Xu Y, Hayes S, Ali I, Ruddy TD, Wells RG, Berman DS, et al. Automatic and visual reproducibility of perfusion and function measures for myocardial perfusion SPECT. J Nucl Cardiol. 2010;17(6):1050–7. https://doi.org/10.1007/s12350-010-9297-0.PubMedPubMedCentralCrossRef

Slomka P, Xu Y, Berman D, Germano G. Quantitative analysis of perfusion studies: strengths and pitfalls. J Nucl Cardiol. 2012;19(2):338–46. https://doi.org/10.1007/s12350-011-9509-2.PubMedPubMedCentralCrossRef

10.

Xu Y, Kavanagh P, Fish M, Gerlach J, Ramesh A, Lemley M, et al. Automated quality control for segmentation of myocardial perfusion SPECT. J Nucl Med. 2009;50(9):1418–26. https://doi.org/10.2967/jnumed.108.061333.PubMedPubMedCentralCrossRef

11.

• Germano G, Kavanagh PB, Fish MB, Lemley MH, Xu Y, Berman DS, et al. “Same-patient processing” for multiple cardiac SPECT studies. 1. Improving LV segmentation accuracy. J Nucl Cardiol. 2016;23(6):1435–41. https://doi.org/10.1007/s12350-016-0673-2. This study demonstrated that, in patients with multiple MPI studies, contour detection is improved using "same-patient processing" by avoiding inter-study inconsistencies.

12.

• Betancur J, Rubeaux M, Fuchs TA, Otaki Y, Arnson Y, Slipczuk L, et al. Automatic valve plane localization in myocardial perfusion SPECT/CT by machine learning: anatomic and clinical validation. J Nucl Med. 2017;58(6):961–7. https://doi.org/10.2967/jnumed.116.179911. This study demonstrated that machine learning algorithms for valve plane localization and segmentation are as effective as expert operators and yielded equivalent diagnostic accuracy.

13.

Taillefer R, DePuey EG, Udelson JE, Beller GA, Benjamin C, Gagnon A. Comparison between the end-diastolic images and the summed images of gated 99mTc-sestamibi SPECT perfusion study in detection of coronary artery disease in women. J Nucl Cardiol. 1999;6(2):169–76. https://doi.org/10.1016/S1071-3581(99)90077-6.PubMedCrossRef

14.

Motwani M, Berman DS, Germano G, Slomka P. Automated quantitative nuclear cardiology methods. Cardiol Clin. 2016;34(1):47–57. https://doi.org/10.1016/j.ccl.2015.08.003.PubMedCrossRef

15.

Slomka PJ, Nishina H, Berman DS, Kang X, Akincioglu C, Friedman JD, et al. “Motion-frozen” display and quantification of myocardial perfusion. J Nucl Med. 2004;45(7):1128–34.PubMed

16.

Qi W, Yang Y, Wernick MN, Pretorius PH, King MA. Limited-angle effect compensation for respiratory binned cardiac SPECT. Med Phys. 2016;43(1):443. https://doi.org/10.1118/1.4938579.PubMedCrossRef

17.

• Daou D, Sabbah R, Coaguila C, Boulahdour H. Applicability of data-driven respiratory motion correction to CZT SPECT myocardial perfusion imaging in the clinical setting: the birth of an old wish. J Nucl Cardiol. 2017;24(4):1451–3. https://doi.org/10.1007/s12350-016-0633-x. This study demonstrated the feasibility and applicability of respiratory motion correction with CZT SPECT systems.

18.

Daou D, Sabbah R, Coaguila C, Boulahdour H. Feasibility of data-driven cardiac respiratory motion correction of myocardial perfusion CZT SPECT: a pilot study. J Nucl Cardiol. 2016.

19.

• Slomka PJ, Rubeaux M, Le Meunier L, Dey D, Lazewatsky JL, Pan T, et al. Dual-gated motion-frozen cardiac PET with flurpiridaz F 18. J Nucl Med. 2015;56(12):1876–81. https://doi.org/10.2967/jnumed.115.164285. This study demonstrated the feasibility of dual (respiratory and cardiac) motion correction in PET imaging using the novel radiotracer F-18 flurpiridaz.

20.

Liu YH. Quantification of nuclear cardiac images: the Yale approach. J Nucl Cardiol. 2007;14(4):483–91. https://doi.org/10.1016/j.nuclcard.2007.06.005.PubMedCrossRef

21.

Garcia EV, Faber TL, Cooke CD, Folks RD, Chen J, Santana C. The increasing role of quantification in clinical nuclear cardiology: the Emory approach. J Nucl Cardiol. 2007;14(4):420–32. https://doi.org/10.1016/j.nuclcard.2007.06.009.PubMedCrossRef

22.

Ficaro EP, Lee BC, Kritzman JN, Corbett JR. Corridor4DM: the Michigan method for quantitative nuclear cardiology. J Nucl Cardiol. 2007;14(4):455–65. https://doi.org/10.1016/j.nuclcard.2007.06.006.PubMedCrossRef

23.

Germano G, Kavanagh PB, Slomka PJ, Van Kriekinge SD, Pollard G, Berman DS. Quantitation in gated perfusion SPECT imaging: the Cedars-Sinai approach. J Nucl Cardiol. 2007;14(4):433–54. https://doi.org/10.1016/j.nuclcard.2007.06.008.PubMedCrossRef

24.

Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation. 2002;105(4):539–42. https://doi.org/10.1161/hc0402.102975.PubMedCrossRef

25.

Hendel RC, Budoff MJ, Cardella JF, Chambers CE, Dent JM, Fitzgerald DM, et al. ACC/AHA/ACR/ASE/ASNC/HRS/NASCI/RSNA/SAIP/SCAI/SCCT/SCMR/SIR 2008 key data elements and definitions for cardiac imaging a report of the American College of Cardiology/American Heart Association Task Force on clinical data standards (Writing Committee to Develop Clinical Data Standards for Cardiac Imaging). J Am Coll Cardiol. 2009;53(1):91–124. https://doi.org/10.1016/j.jacc.2008.09.006.PubMedCrossRef

26.

Slomka PJ, Nishina H, Berman DS, Akincioglu C, Abidov A, Friedman JD, et al. Automated quantification of myocardial perfusion SPECT using simplified normal limits. J Nucl Cardiol. 2005;12(1):66–77. https://doi.org/10.1016/j.nuclcard.2004.10.006.PubMedCrossRef

27.

• Arsanjani R, Dey D, Khachatryan T, Shalev A, Hayes SW, Fish M, et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015;22(5):877–84. https://doi.org/10.1007/s12350-014-0027-x. This study demonstrated that integration of perfusion analysis with clinical variables through machine learning algorithms was comparable or superior to expert interpretation in predicting coronary revascularization.

28.

• Motwani M, Leslie WD, Goertzen AL, Otaki Y, Germano G, Berman DS, et al. Fully automated analysis of attenuation-corrected SPECT for the long-term prediction of acute myocardial infarction. J Nucl Cardiol. 2017; https://doi.org/10.1007/s12350-017-0840-0. This study demonstrated that in automatically processed and interpreted MPI studies, stress TPD was an independent predictor of future myocardial infarction.

29.

Sanghani RM, Doukky R. Fully automated analysis of perfusion data: the rise of the machines. J Nucl Cardiol. 2017; https://doi.org/10.1007/s12350-017-0884-1.

30.

Slomka PJ, Nishina H, Berman DS, Kang X, Friedman JD, Hayes SW, et al. Automatic quantification of myocardial perfusion stress-rest change: a new measure of ischemia. J Nucl Med. 2004;45(2):183–91.PubMed

31.

Slomka P, Hung GU, Germano G, Berman DS. Novel SPECT technologies and approaches in cardiac imaging. Cardiovasc Innov Appl. 2016;2(1):31–46. https://doi.org/10.15212/CVIA.2016.0052.PubMedPubMedCentralCrossRef

32.

Shaw LJ, Berman DS, Maron DJ, Mancini GB, Hayes SW, Hartigan PM, et al. Optimal medical therapy with or without percutaneous coronary intervention to reduce ischemic burden: results from the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial nuclear substudy. Circulation. 2008;117(10):1283–91. https://doi.org/10.1161/CIRCULATIONAHA.107.743963.PubMedCrossRef

33.

Fiechter M, Gebhard C, Ghadri JR, Fuchs TA, Pazhenkottil AP, Nkoulou RN, et al. Myocardial perfusion imaging with 13N-ammonia PET is a strong predictor for outcome. Int J Cardiol. 2013;167(3):1023–6. https://doi.org/10.1016/j.ijcard.2012.03.076.PubMedCrossRef

34.

Hajjiri MM, Leavitt MB, Zheng H, Spooner AE, Fischman AJ, Gewirtz H. Comparison of positron emission tomography measurement of adenosine-stimulated absolute myocardial blood flow versus relative myocardial tracer content for physiological assessment of coronary artery stenosis severity and location. JACC Cardiovasc Imaging. 2009;2(6):751–8. https://doi.org/10.1016/j.jcmg.2009.04.004.PubMedCrossRef

35.

Ziadi MC, Dekemp RA, Williams KA, Guo A, Chow BJ, Renaud JM, et al. Impaired myocardial flow reserve on rubidium-82 positron emission tomography imaging predicts adverse outcomes in patients assessed for myocardial ischemia. J Am Coll Cardiol. 2011;58(7):740–8. https://doi.org/10.1016/j.jacc.2011.01.065.PubMedCrossRef

36.

Herzog BA, Husmann L, Valenta I, Gaemperli O, Siegrist PT, Tay FM, et al. Long-term prognostic value of 13N-ammonia myocardial perfusion positron emission tomography added value of coronary flow reserve. J Am Coll Cardiol. 2009;54(2):150–6. https://doi.org/10.1016/j.jacc.2009.02.069.PubMedCrossRef

37.

Fukushima K, Javadi MS, Higuchi T, Lautamaki R, Merrill J, Nekolla SG, et al. Prediction of short-term cardiovascular events using quantification of global myocardial flow reserve in patients referred for clinical 82Rb PET perfusion imaging. J Nucl Med. 2011;52(5):726–32. https://doi.org/10.2967/jnumed.110.081828.PubMedCrossRef

38.

Klein R, Hung GU, Wu TC, Huang WS, Li D, deKemp RA, et al. Feasibility and operator variability of myocardial blood flow and reserve measurements with (9)(9)mTc-sestamibi quantitative dynamic SPECT/CT imaging. J Nucl Cardiol. 2014;21(6):1075–88. https://doi.org/10.1007/s12350-014-9971-8.PubMedCrossRef

39.

• Hsu B, Hu LH, Yang BH, Chen LC, Chen YK, Ting CH, et al. SPECT myocardial blood flow quantitation toward clinical use: a comparative study with 13N-ammonia PET myocardial blood flow quantitation. Eur J Nucl Med Mol Imaging. 2017;44(1):117–28. https://doi.org/10.1007/s00259-016-3491-5. This study suggested that the accuracy of myocardial blood flow quantification by PET and SPECT may be comparable.

40.

Slomka PJ, Berman DS, Germano G. Absolute myocardial blood flow quantification with SPECT/CT: is it possible? J Nucl Cardiol. 2014;21(6):1092–5. https://doi.org/10.1007/s12350-014-0002-6.PubMedCrossRef

41.

Murthy VL, Naya M, Foster CR, Hainer J, Gaber M, Di Carli G, et al. Improved cardiac risk assessment with noninvasive measures of coronary flow reserve. Circulation. 2011;124(20):2215–24. https://doi.org/10.1161/CIRCULATIONAHA.111.050427.PubMedPubMedCentralCrossRef

42.

Brodov Y, Fish M, Rubeaux M, Otaki Y, Gransar H, Lemley M, et al. Quantitation of left ventricular ejection fraction reserve from early gated regadenoson stress Tc-99m high-efficiency SPECT. J Nucl Cardiol. 2016;23(6):1251–61. https://doi.org/10.1007/s12350-016-0519-y.PubMedPubMedCentralCrossRef

43.

Atchley AE, Kitzman DW, Whellan DJ, Iskandrian AE, Ellis SJ, Pagnanelli RA, et al. Myocardial perfusion, function, and dyssynchrony in patients with heart failure: baseline results from the single-photon emission computed tomography imaging ancillary study of the heart failure and a controlled trial investigating outcomes of exercise TraiNing (HF-ACTION) trial. Am Heart J. 2009;158(4 Suppl):S53–63. https://doi.org/10.1016/j.ahj.2009.07.009.PubMedPubMedCentralCrossRef

44.

Chen J, Garcia EV, Folks RD, Cooke CD, Faber TL, Tauxe EL, et al. Onset of left ventricular mechanical contraction as determined by phase analysis of ECG-gated myocardial perfusion SPECT imaging: development of a diagnostic tool for assessment of cardiac mechanical dyssynchrony. J Nucl Cardiol. 2005;12(6):687–95. https://doi.org/10.1016/j.nuclcard.2005.06.088.PubMedCrossRef

45.

Chen J, Garcia EV, Bax JJ, Iskandrian AE, Borges-Neto S, Soman P. SPECT myocardial perfusion imaging for the assessment of left ventricular mechanical dyssynchrony. J Nucl Cardiol. 2011;18(4):685–94. https://doi.org/10.1007/s12350-011-9392-x.PubMedPubMedCentralCrossRef

46.

Trimble MA, Velazquez EJ, Adams GL, Honeycutt EF, Pagnanelli RA, Barnhart HX, et al. Repeatability and reproducibility of phase analysis of gated single-photon emission computed tomography myocardial perfusion imaging used to quantify cardiac dyssynchrony. Nucl Med Commun. 2008;29(4):374–81. https://doi.org/10.1097/MNM.0b013e3282f81380.PubMedPubMedCentralCrossRef

47.

Aggarwal H, AlJaroudi WA, Mehta S, Mannon R, Heo J, Iskandrian AE, et al. The prognostic value of left ventricular mechanical dyssynchrony using gated myocardial perfusion imaging in patients with end-stage renal disease. J Nucl Cardiol. 2014;21(4):739–46. https://doi.org/10.1007/s12350-014-9886-4.PubMedCrossRef

48.

Pazhenkottil AP, Buechel RR, Husmann L, Nkoulou RN, Wolfrum M, Ghadri JR, et al. Long-term prognostic value of left ventricular dyssynchrony assessment by phase analysis from myocardial perfusion imaging. Heart. 2011;97(1):33–7. https://doi.org/10.1136/hrt.2010.201566.PubMedCrossRef

49.

Brodov Y, Gransar H, Dey D, Shalev A, Germano G, Friedman JD, et al. Combined quantitative assessment of myocardial perfusion and coronary artery calcium score by hybrid 82Rb PET/CT improves detection of coronary artery disease. J Nucl Med. 2015;56(9):1345–50. https://doi.org/10.2967/jnumed.114.153429.PubMedCrossRef

50.

Engbers EM, Timmer JR, Mouden M, Jager PL, Knollema S, Oostdijk AH, et al. Visual estimation of coronary calcium on computed tomography for attenuation correction. J Cardiovasc Comput Tomogr. 2016;10(4):327–9. https://doi.org/10.1016/j.jcct.2016.04.002.PubMedCrossRef

51.

Mylonas I, Kazmi M, Fuller L, deKemp RA, Yam Y, Chen L, et al. Measuring coronary artery calcification using positron emission tomography-computed tomography attenuation correction images. Eur Heart J Cardiovasc Imaging. 2012;13(9):786–92. https://doi.org/10.1093/ehjci/jes079.PubMedCrossRef

52.

Einstein AJ, Johnson LL, Bokhari S, Son J, Thompson RC, Bateman TM, et al. Agreement of visual estimation of coronary artery calcium from low-dose CT attenuation correction scans in hybrid PET/CT and SPECT/CT with standard Agatston score. J Am Coll Cardiol. 2010;56(23):1914–21. https://doi.org/10.1016/j.jacc.2010.05.057.PubMedPubMedCentralCrossRef

53.

• Isgum I, de Vos BD, Wolterink JM, Dey D, Berman DS, Rubeaux M, et al. Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT. J Nucl Cardiol. 2017. This study demonstrated that CT attenuation correction scan in PET imaging may be used for calcium score determination.

54.

• Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J. 2017;38(7):500–7. https://doi.org/10.1093/eurheartj/ehw188. This study demonstrated that machine learning algorithms, combining clinical and CCTA parameters, predict all cause death better than CCTA alone.

55.

Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging (Bellingham). 2015;2(1):014003. https://doi.org/10.1117/1.JMI.2.1.014003.CrossRef

56.

• Betancur J, Otaki Y, Motwani M, Fish M, Lemley M, Dey D, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. JACC Cardiovasc Imaging. 2017; https://doi.org/10.1016/j.jcmg.2017.07.024. This study shows that, compared to visual assessment, automatic interpretation using machine learning was superior in predicting 3-year MACE risk.

57.

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2(3):841–60. https://doi.org/10.1214/08-AOAS169.CrossRef

58.

Chen Y, Jia Z, Mercola D, Xie X. A gradient boosting algorithm for survival analysis via direct optimization of concordance index. Comput Math Methods Med. 2013;2013:873595.PubMedPubMedCentral

59.

• Betancur J, Commandeur T, Sharir T, Fish M, Ruddy TD, Kaufmann PA, et al. Analysis of raw polar maps from myocardial perfusion SPECT by gender-adjusted deep learning improves automatic prediction of obstructive coronary disease. J Nucl Cardiol. 2017;24(4):1492–3. [Abstract 330–05]. This study demonstrated that deep machine learning algorithms improved the prediction of obstructive coronary artery disease.

Titel: New Trends in Quantitative Nuclear Cardiology Methods
verfasst von: Javier Gomez
Rami Doukky
Guido Germano
Piotr Slomka
Publikationsdatum: 01.01.2018
Verlag: Springer US
Erschienen in: Current Cardiovascular Imaging Reports / Ausgabe 1/2018
Print ISSN: 1941-9066
Elektronische ISSN: 1941-9074
DOI: https://doi.org/10.1007/s12410-018-9443-7

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New Trends in Quantitative Nuclear Cardiology Methods

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