Precision and accuracy of clinical quantification of myocardial blood flow by dynamic PET: A technical perspective

Abstract

A number of exciting advances in PET/CT technology and improvements in methodology have recently converged to enhance the feasibility of routine clinical quantification of myocardial blood flow and flow reserve. Recent promising clinical results are pointing toward an important role for myocardial blood flow in the care of patients. Absolute blood flow quantification can be a powerful clinical tool, but its utility will depend on maintaining precision and accuracy in the face of numerous potential sources of methodological errors. Here we review recent data and highlight the impact of PET instrumentation, image reconstruction, and quantification methods, and we emphasize 82Rb cardiac PET which currently has the widest clinical application. It will be apparent that more data are needed, particularly in relation to newer PET technologies, as well as clinical standardization of PET protocols and methods. We provide recommendations for the methodological factors considered here. At present, myocardial flow reserve appears to be remarkably robust to various methodological errors; however, with greater attention to and more detailed understanding of these sources of error, the clinical benefits of stress-only blood flow measurement may eventually be more fully realized.

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Abbreviations

MPI:

Myocardial Perfusion Imaging

MBF:

Myocardial Blood Flow

MFR:

Myocardial Flow Reserve

TOF:

Time of Flight

RPC:

Repeatability Coefficient

FBP:

Filtered Back Projection

3DRP:

3D Reprojection (i.e., 3D FBP)

OSEM:

Ordered Subsets Expectation Maximization

PSF:

Point Spread Function

LV:

Left Ventricle

RV:

Right Ventricle

References

  1. 1.

    Tio RA, Dabeshlim A, Siebelink H-MJ, Sutter J de, Hillege HL, Zeebregts CJ, et al. Comparison between the prognostic value of left ventricular function and myocardial perfusion reserve in patients with ischemic heart disease. J Nucl Med 2009;50:214-9. doi:10.2967/jnumed.108.054395.

    PubMed  Article  Google Scholar 

  2. 2.

    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;54:150-6. doi:10.1016/j.jacc.2009.02.069.

    PubMed  Article  Google Scholar 

  3. 3.

    Ziadi MC, deKemp RA, Williams KA, Guo A, Chow BJW, 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;58:740-8. doi:10.1016/j.jacc.2011.01.065.

    PubMed  Article  Google Scholar 

  4. 4.

    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;124:2215-24. doi:10.1161/CIRCULATIONAHA.111.050427.

    PubMed Central  PubMed  Article  Google Scholar 

  5. 5.

    Murthy VL, Naya M, Foster CR, Gaber M, Hainer J, Klein J, et al. Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus. Circulation 2012;126:1858-68. doi:10.1161/CIRCULATIONAHA.112.120402.

    PubMed Central  CAS  PubMed  Article  Google Scholar 

  6. 6.

    Klein R, Beanlands R, deKemp R. Quantification of myocardial blood flow and flow reserve: Technical aspects. J Nucl Cardiol 2010;17:555-70.

    PubMed  Article  Google Scholar 

  7. 7.

    Lewellen TK. The challenge of detector designs for PET. Am J Roentgenol 2010;195:301-9. doi:10.2214/AJR.10.4741.

    Article  Google Scholar 

  8. 8.

    Budinger TF, Derenzo SE, Huesman RH, Cahoon JL. Medical criteria for the design of a dynamic positron tomograph for heart studies. IEEE Trans Nucl Sci 1982;29:488.

    Article  Google Scholar 

  9. 9.

    Mullani NA, Gaeta J, Yerian K, Wong W-H, Hartz RK, Philippe EA, et al. Dynamic imaging with high resolution time-of-flight PET camera—TOFPET I. IEEE Trans Nucl Sci 1984;31:609-13. doi:10.1109/TNS.1984.4333329.

    Article  Google Scholar 

  10. 10.

    Ter-Pogossian MM, Ficke DC, Beecher DE, Hoffman GR, Bergmann SR. The super PET 3000-E: A PET scanner designed for high count rate cardiac applications. J Comput Assist Tomogr 1994;18:661-9.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Strother SC, Casey ME, Hoffman EJ. Measuring PET scanner sensitivity: Relating countrates to image signal-to-noise ratios using noise equivalents counts. IEEE Trans Nucl Sci 1990;37:783-8. doi:10.1109/23.106715.

    Article  Google Scholar 

  12. 12.

    Germano G, Hoffman EJ. Investigation of count rate and deadtime characteristics of a high resolution PET system. J Comput Assist Tomogr 1988;12:836-46.

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Germano G, Hoffman EJ. A study of data loss and mispositioning due to pileup in 2-D detectors in PET. IEEE Trans Nucl Sci 1990;37:671-5. doi:10.1109/23.106696.

    Article  Google Scholar 

  14. 14.

    Jakoby BW, Bercier Y, Conti M, Casey ME, Bendriem B, Townsend DW. Physical and clinical performance of the mCT time-of-flight PET/CT scanner. Phys Med Biol 2011;56:2375. doi:10.1088/0031-9155/56/8/004.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi MC. Physical performance of the new hybrid PET/CT discovery-690. Med Phys 2011;38:5394-411. doi:10.1118/1.3635220.

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Kolthammer JA, Su K-H, Grover A, Narayanan M, Jordan DW, Muzic RF. Performance evaluation of the Ingenuity TF PET/CT scanner with a focus on high count-rate conditions. Phys Med Biol 2014;59:3843. doi:10.1088/0031-9155/59/14/3843.

    PubMed Central  PubMed  Article  Google Scholar 

  17. 17.

    Conti M, Bendriem B, Casey M, Chen M, Kehren F, Michel C, et al. First experimental results of time-of-flight reconstruction on an LSO PET scanner. Phys Med Biol 2005;50:4507. doi:10.1088/0031-9155/50/19/006.

    PubMed  Article  Google Scholar 

  18. 18.

    Surti S, Kuhn A, Werner ME, Perkins AE, Kolthammer J, Karp JS. Performance of Philips Gemini TF PET/CT scanner with special consideration for its time-of-flight imaging capabilities. J Nucl Med 2007;48:471-80.

    PubMed  Google Scholar 

  19. 19.

    Seo Y, Teo B-K, Hadi M, Schreck C, Bacharach SL, Hasegawa BH. Quantitative accuracy of PET/CT for image-based kinetic analysis. Med Phys 2008;35:3086-9. doi:10.1118/1.2937439.

    PubMed Central  PubMed  Article  Google Scholar 

  20. 20.

    Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Carson RE. Tracer kinetic modeling in PET. In: Bailey DL, Townsend DW, Valk PE, Maisey MN, editors. Positron emission tomography. London: Springer; 2005. p. 127-59.

    Google Scholar 

  22. 22.

    Raylman RR, Caraher JM, Hutchins GD. Sampling requirements for dynamic cardiac PET studies using image-derived input functions. J Nucl Med 1993;34:440-7.

    CAS  PubMed  Google Scholar 

  23. 23.

    Di Carli MF, Dorbala S, Meserve J, El Fakhri G, Sitek A, Moore SC. Clinical myocardial perfusion PET/CT. J Nucl Med 2007;48:783-93.

    PubMed  Article  Google Scholar 

  24. 24.

    Schelbert HR. Positron emission tomography of the heart: Methodology, findings in the normal and the diseased heart, and clinical applications. In: Phelps ME, editor. PET: Molecular imaging and its biological applications. 1st ed. New York: Springer; 2004. p. 389-508.

    Google Scholar 

  25. 25.

    Kolthammer JA, Muzic RF. Optimized dynamic framing for PET-based myocardial blood flow estimation. Phys Med Biol 2013;58:5783. doi:10.1088/0031-9155/58/16/5783.

    PubMed  Article  Google Scholar 

  26. 26.

    Lee B, Moody J, Murthy V, Corbett J, Ficaro E. Effects of temporal sampling on PET myocardial blood flow estimates. Soc Nucl Med Annu Meet Abstr 2014;55:1775.

    Google Scholar 

  27. 27.

    Klein R, Adler A, Beanlands RS, deKemp RA. Precision-controlled elution of a 82Sr/82Rb generator for cardiac perfusion imaging with positron emission tomography. Phys Med Biol 2007;52:659-73. doi:10.1088/0031-9155/52/3/009.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    deKemp RA, Yoshinaga K, Beanlands RSB. Will 3-dimensional PET-CT enable the routine quantification of myocardial blood flow? J Nucl Cardiol 2007;14:380-97.

    PubMed  Article  Google Scholar 

  29. 29.

    Tout D, Tonge C, Muthu S, Arumugam P. Assessment of a protocol for routine simultaneous myocardial blood flow measurement and standard myocardial perfusion imaging with rubidium-82 on a high count rate positron emission tomography system. Nucl Med Commun 2012;33:1202-11. doi:10.1097/MNM.0b013e3283567554.

    PubMed  Article  Google Scholar 

  30. 30.

    Cheng J-C, Blinder S, Rahmim A, Sossi V. A Scatter calibration technique for dynamic brain imaging in high resolution PET. IEEE Trans Nucl Sci 2010;57:225-33. doi:10.1109/TNS.2009.2031643.

    Article  Google Scholar 

  31. 31.

    Watson CC. Extension of single scatter simulation to scatter correction of time-of-flight PET. IEEE Trans Nucl Sci 2007;54:1679-86. doi:10.1109/TNS.2007.901227.

    Article  Google Scholar 

  32. 32.

    Walker MD, Sossi V. Commentary: An eye on PET quantification. Mol Imaging Biol 2014;17:1-3. doi:10.1007/s11307-014-0791-7.

    Article  Google Scholar 

  33. 33.

    Rajaram M, Tahari AK, Lee AH, Lodge MA, Tsui B, Nekolla S, et al. Cardiac PET/CT misregistration causes significant changes in estimated myocardial blood flow. J Nucl Med 2013;54:50-4. doi:10.2967/jnumed.112.108183.

    PubMed  Article  Google Scholar 

  34. 34.

    Martin CC, Christian BT, Satter MR, Nickerson LDH, Nickles RJ. Quantitative PET with positron emitters that emit prompt gamma rays. IEEE Trans Med Imaging 1995;14:681-7. doi:10.1109/42.476109.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Cherry SR, Sorenson J, Phelps M. Physics in nuclear medicine, vol. 3. Philadelphia: Saunders; 2003.

    Google Scholar 

  36. 36.

    Esteves FP, Nye JA, Khan A, Folks RD, Halkar RK, Garcia EV, et al. Prompt-gamma compensation in Rb-82 myocardial perfusion 3D PET/CT. J Nucl Cardiol 2010;17:247-53.

    PubMed  Article  Google Scholar 

  37. 37.

    Renaud JM, Mylonas I, McArdle B, Dowsley T, Yip K, Turcotte E, et al. Clinical interpretation standards and quality assurance for the multicenter PET/CT Trial: 82Rb as an alternative radiopharmaceutical for myocardial imaging. J Nucl Med 2014. doi:10.2967/jnumed.112.117515.

    Google Scholar 

  38. 38.

    Watson C, Hayden C, Casey M, Hamill J, Bendriem B. Prompt gamma correction for improved quantification in 82Rb PET. Soc Nucl Med Annu Meet Abstr 2008;49:64P.

    Google Scholar 

  39. 39.

    Alpert NM, Chesler D, Correia J, Ackerman RH, Chang JY, Finklestein S, et al. Estimation of the local statistical noise in emission computed tomography. IEEE Trans Med Imaging 1982;1:142-6. doi:10.1109/TMI.1982.4307561.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Huesman RH. A new fast algorithm for the evaluation of regions of interest and statistical uncertainty in computed tomography. Phys Med Biol 1984;29:543-52.

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Carson RE, Yan Y, Daube-Witherspoon ME, Freedman N, Bacharach SL, Herscovitch P. An approximation formula for the variance of PET region-of-interest values. IEEE Trans Med Imaging 1993;12:240-50.

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Van Velden FHP, Kloet RW, van Berckel BNM, Lammertsma AA, Boellaard R. Accuracy of 3-dimensional reconstruction algorithms for the high-resolution research tomograph. J Nucl Med 2009;50:72-80. doi:10.2967/jnumed.108.052985.

    PubMed  Article  Google Scholar 

  43. 43.

    Chen GP, Branch KR, Alessio AM, Pham P, Tabibiazar R, Kinahan P, et al. Effect of reconstruction algorithms on myocardial blood flow measurement with 13N-ammonia PET. J Nucl Med 2007;48:1259-65. doi:10.2967/jnumed.106.038232.

    PubMed Central  PubMed  Article  Google Scholar 

  44. 44.

    Søndergaard HM, Madsen MM, Boisen K, Bøttcher M, Schmitz O, Nielsen TT, et al. Evaluation of iterative reconstruction (OSEM) versus filtered back-projection for the assessment of myocardial glucose uptake and myocardial perfusion using dynamic PET. Eur J Nucl Med Mol Imaging 2007;34:320-9. doi:10.1007/s00259-006-0198-z.

    PubMed  Article  Google Scholar 

  45. 45.

    Kobayashi M, Mori T, Kiyono Y, Tsujikawa T, Maruyama R, Higaki Y, et al. Appropriate parameters of the ordered-subset expectation maximization algorithm on measurement of myocardial blood flow and oxygen consumption with 11C-acetate PET. Nucl Med Commun 2012;33:130-8. doi:10.1097/MNM.0b013e32834e7f5c.

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Boellaard R, van Lingen A, Lammertsma AA. Experimental and clinical evaluation of iterative reconstruction (OSEM) in dynamic PET: Quantitative characteristics and effects on kinetic modeling. J Nucl Med 2001;42:808-17.

    CAS  PubMed  Google Scholar 

  47. 47.

    Reilhac A, Tomeï S, Buvat I, Michel C, Keheren F, Costes N. Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies. NeuroImage 2008;39:359-68. doi:10.1016/j.neuroimage.2007.07.038.

    PubMed  Article  Google Scholar 

  48. 48.

    Walker MD, Asselin M-C, Julyan PJ, Feldmann M, Talbot PS, Jones T, et al. Bias in iterative reconstruction of low-statistics PET data: Benefits of a resolution model. Phys Med Biol 2011;56:931. doi:10.1088/0031-9155/56/4/004.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Jian Y, Carson RE. Effect of subsets on bias and variance in low-count iterative PET reconstruction. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2013. p. 1-4. doi:10.1109/NSSMIC.2013.6829220.

  50. 50.

    Presotto L, Gianolli L, Gilardi MC, Bettinardi V. Evaluation of image reconstruction algorithms encompassing time-of-flight and point spread function modelling for quantitative cardiac PET: Phantom studies. J Nucl Cardiol 2014. doi:10.1007/s12350-014-0023-1.

    PubMed  Google Scholar 

  51. 51.

    Henze E, Huang S-C, Ratib O, Hoffman E, Phelps ME, Schelbert HR. Measurements of regional tissue and blood-pool radiotracer concentrations from serial tomographic images of the heart. J Nucl Med 1983;24:987-96.

    CAS  PubMed  Google Scholar 

  52. 52.

    Rahmim A, Qi J, Sossi V. Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls. Med Phys 2013;40:064301. doi:10.1118/1.4800806.

    PubMed Central  PubMed  Article  Google Scholar 

  53. 53.

    Jakoby BW, Bercier Y, Watson CC, Bendriem B, Townsend DW. Performance characteristics of a New LSO PET/CT scanner with extended axial field-of-view and PSF reconstruction. IEEE Trans Nucl Sci 2009;56:633-9. doi:10.1109/TNS.2009.2015764.

    Article  Google Scholar 

  54. 54.

    Tong S, Alessio AM, Thielemans K, Stearns C, Ross S, Kinahan PE. Properties and mitigation of edge artifacts in PSF-based PET reconstruction. IEEE Trans Nucl Sci 2011;58:2264-75. doi:10.1109/TNS.2011.2164579.

    Article  Google Scholar 

  55. 55.

    Tong S, Alessio AM, Kinahan PE. Noise and signal properties in PSF-based fully 3D PET image reconstruction: An experimental evaluation. Phys Med Biol 2010;55:1453. doi:10.1088/0031-9155/55/5/013.

    PubMed Central  CAS  PubMed  Article  Google Scholar 

  56. 56.

    Snyder DL, Thomas LJ, Ter-Pogossian MM. A mathematical model for positron-emission tomography systems having time-of-flight measurements. IEEE Trans Nucl Sci 1981;28:3575-83. doi:10.1109/TNS.1981.4332168.

    Article  Google Scholar 

  57. 57.

    Budinger TF. Time-of-flight positron emission tomography: Status relative to conventional PET. J Nucl Med 1983;24:73-8.

    CAS  PubMed  Google Scholar 

  58. 58.

    Karp JS, Surti S, Daube-Witherspoon ME, Muehllehner G. Benefit of time-of-flight in PET: Experimental and clinical results. J Nucl Med Off Publ Soc Nucl Med 2008;49:462-70. doi:10.2967/jnumed.107.044834.

    Google Scholar 

  59. 59.

    Daube-Witherspoon ME, Surti S, Perkins AE, Karp JS. Determination of accuracy and precision of lesion uptake measurements in human subjects with time-of-flight PET. J Nucl Med 2014;55:602-7. doi:10.2967/jnumed.113.127035.

    PubMed Central  PubMed  Article  Google Scholar 

  60. 60.

    Mettivier G, Tabacchini V, Conti M, Russo P. Signal-to-noise gain at variable randoms ratio in TOF PET. IEEE Trans Nucl Sci 2012;59:1948-57. doi:10.1109/TNS.2012.2198833.

    Article  Google Scholar 

  61. 61.

    Westerwoudt V, Conti M, Eriksson L. Advantages of improved time resolution for TOF PET at very low statistics. IEEE Trans Nucl Sci 2014;61:126-33. doi:10.1109/TNS.2013.2287175.

    Article  Google Scholar 

  62. 62.

    Surti S, Karp JS, Popescu LM, Daube-Witherspoon ME, Werner M. Investigation of time-of-flight benefit for fully 3-D PET. IEEE Trans Med Imaging 2006;25:529-38. doi:10.1109/TMI.2006.871419.

    PubMed  Article  Google Scholar 

  63. 63.

    Conti M. Why is TOF PET reconstruction a more robust method in the presence of inconsistent data? Phys Med Biol 2011;56:155. doi:10.1088/0031-9155/56/1/010.

    PubMed  Article  Google Scholar 

  64. 64.

    Degenhardt C, Rodrigues P, Trindade A, Zwaans B, Mulhens O, Dorscheid R, et al. Performance evaluation of a prototype Positron Emission Tomography scanner using Digital Photon Counters (DPC). In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:2820-4. doi:10.1109/NSSMIC.2012.6551643.

  65. 65.

    Presotto L, Busnardo E, Bettinardi V, Landoni C, Todeschini P, Rimoldi O, et al. Evaluation of time of flight (TOF) and point spread function (PSF) reconstructions in the quantification of myocardial blood flow with 13N ammonia and PET: Comparison among reconstructions (reprojection, OSEM), software (PMOD and CARIMAS) and operators. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:3979-82. doi:10.1109/NSSMIC.2012.6551912.

  66. 66.

    Armstrong IS, Tonge CM, Arumugam P. Impact of point spread function modeling and time-of-flight on myocardial blood flow and myocardial flow reserve measurements for rubidium-82 cardiac PET. J Nucl Cardiol 2014;21:467-74. doi:10.1007/s12350-014-9858-8.

    PubMed  Article  Google Scholar 

  67. 67.

    Tomiyama T, Ishihara K, Suda M, Kanaya K, Sakurai M, Takahashi N, et al. Impact of time-of-flight on qualitative and quantitative analyses of myocardial perfusion PET studies using 13N-ammonia. J Nucl Cardiol 2014. doi:10.1007/s12350-014-0037-8.

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Lortie M, Beanlands RSB, Yoshinaga K, Klein R, Dasilva JN, DeKemp RA. Quantification of myocardial blood flow with 82Rb dynamic PET imaging. Eur J Nucl Med Mol Imaging 2007;34:1765-74.

    PubMed  Article  Google Scholar 

  69. 69.

    Pan X-B, Declerck J, Burckhardt DD. Cardiac positron emission tomography: Overview of myocardial perfusion, myocardial blood flow and myocardial flow reserve imaging. Siemens Medical Solutions; 2011:1-24.

  70. 70.

    Lee B, Moody J, Sitek A, Murthy V, Di Carli M, Corbett J, et al. Effects of filtering on Rb-82 myocardial blood flow estimates. In: Society of Nuclear Medicine Annual Meeting Abstracts. 2013;54:1659. http://jnumedmtg.snmjournals.org..

  71. 71.

    Presotto L, Bettinardi V, Petta P, Gilardi MC. A compact dynamic phantom to assess the effect of motion in cardiac PET and SPECT studies. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:2638-42. doi:10.1109/NSSMIC.2012.6551601.

  72. 72.

    Hutchins GD, Caraher JM, Raylman RR. A region of interest strategy for minimizing resolution distortions in quantitative myocardial PET studies. J Nucl Med 1992;33:1243-50.

    CAS  PubMed  Google Scholar 

  73. 73.

    Schäfers KP, Stegger L. Combined imaging of molecular function and morphology with PET/CT and SPECT/CT: Image fusion and motion correction. Basic Res Cardiol 2008;103:191-9. doi:10.1007/s00395-008-0717-0.

    PubMed  Article  Google Scholar 

  74. 74.

    Mohy-ud-Din H, Karakatsanis NA, Goddard JS, Baba J, Wills W, Tahari AK, et al. Generalized dynamic PET inter-frame and intra-frame motion correction: Phantom and human validation studies. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:3067-78. doi:10.1109/NSSMIC.2012.6551701.

  75. 75.

    Pourmoghaddas A, Klein R, deKemp RA, Wells RG. Respiratory phase alignment improves blood-flow quantification in Rb82 PET myocardial perfusion imaging. Med Phys 2013;40:022503. doi:10.1118/1.4788669.

    PubMed  Article  Google Scholar 

  76. 76.

    Schleyer PJ, Thielemans K, Marsden PK. Extracting a respiratory signal from raw dynamic PET data that contain tracer kinetics. Phys Med Biol 2014;59:4345. doi:10.1088/0031-9155/59/15/4345.

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Defrise M, Rezaei A, Nuyts J. Time-of-flight PET data determine the attenuation sinogram up to a constant. Phys Med Biol 2012;57:885. doi:10.1088/0031-9155/57/4/885.

    PubMed  Article  Google Scholar 

  78. 78.

    Rezaei A, Nuyts J. Simultaneous reconstruction of the activity image and registration of the CT image in TOF-PET. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2013.

  79. 79.

    Garcia EV, Van Train K, Maddahi J, Prigent F, Friedman J, Areeda J, et al. Quantification of rotational thallium-201 myocardial tomography. J Nucl Med 1985;26:17-26.

    CAS  PubMed  Google Scholar 

  80. 80.

    Weinberg IN, Huang SC, Hoffman EJ, Araujo L, Nienaber C, McKay MG, et al. Validation of PET-acquired input functions for cardiac studies. J Nucl Med 1988;29:241-7.

    CAS  PubMed  Google Scholar 

  81. 81.

    Klein R, Renaud JM, Ziadi MC, Thorn SL, Adler A, Beanlands RS, et al. Intra- and inter-operator repeatability of myocardial blood flow and myocardial flow reserve measurements using rubidium-82 PET and a highly automated analysis program. J Nucl Cardiol 2010;17:600-16. doi:10.1007/s12350-010-9225-3.

    PubMed  Article  Google Scholar 

  82. 82.

    Hoffman EJ, Huang SC, Phelps ME. Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr 1979;3:299-308. doi:10.1097/00004728-197906000-00001

    CAS  PubMed  Article  Google Scholar 

  83. 83.

    Nuyts J, Maes A, Vrolix M, Schiepers C, Schelbert H, Kuhle W, et al. Three-dimensional correction for spillover and recovery of myocardial PET images. J Nucl Med 1996;37:767-74.

    CAS  PubMed  Google Scholar 

  84. 84.

    Coxson PG, Brennan KM, Huesman RH, Lim S, Budinger TF. Variability and reproducibility of rubidium-82 kinetic parameters in the myocardium of the anesthetized canine. J Nucl Med 1995;36:287-96.

    CAS  PubMed  Google Scholar 

  85. 85.

    deKemp RA, Declerck J, Klein R, Pan X-B, Nakazato R, Tonge C, et al. Multisoftware reproducibility study of stress and rest myocardial blood flow assessed with 3D dynamic PET/CT and a 1-tissue-compartment model of 82Rb kinetics. J Nucl Med 2013;54:571-7.

    CAS  PubMed  Article  Google Scholar 

  86. 86.

    Sitek A, Gullberg GT, Huesman RH. Correction for ambiguous solutions in factor analysis using a penalized least squares objective. IEEE Trans Med Imaging 2002;21:216-25.

    PubMed  Article  Google Scholar 

  87. 87.

    El Fakhri G, Sitek A, Guerin B, Kijewski MF, Di Carli MF, Moore SC. Quantitative dynamic cardiac 82Rb PET using generalized factor and compartment analyses. J Nucl Med 2005;46:1264-71.

    PubMed  Google Scholar 

  88. 88.

    El Fakhri G, Kardan A, Sitek A, Dorbala S, Abi-Hatem N, Lahoud Y, et al. Reproducibility and accuracy of quantitative myocardial blood flow assessment with 82Rb PET: Comparison with 13N-ammonia PET. J Nucl Med 2009;50:1062-71. doi:10.2967/jnumed.104.007831.

    PubMed Central  PubMed  Article  CAS  Google Scholar 

  89. 89.

    Klein R, Beanlands RS, Wassenaar RW, Thorn SL, Lamoureux M, DaSilva JN, et al. Kinetic model-based factor analysis of dynamic sequences for 82-rubidium cardiac positron emission tomography. Med Phys 2010;37:3995-4010.

    CAS  PubMed  Article  Google Scholar 

  90. 90.

    Murthy VL, Lee BC, Sitek A, Naya M, Moody J, Polavarapu V, et al. Comparison and prognostic validation of multiple methods of quantification of myocardial blood flow with 82Rb PET. J Nucl Med 2014;55:1952-8. doi:10.2967/jnumed.114.145342.

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    Germino M, Ropchan J, Mulnix T, Najafzadeh S, Ackah E, Feringa H, et al. Generation of parametric images from time-of-flight 82Rb and 15O-water cardiac PET with ICA-derived input functions. Soc Nucl Med Annu Meet Abstr 2014;55:46.

    Google Scholar 

  92. 92.

    Coxson PG, Huesman RH, Borland L. Consequences of using a simplified kinetic model for dynamic PET data. J Nucl Med 1997;38:660-7.

    CAS  PubMed  Google Scholar 

  93. 93.

    Van den Hoff J, Burchert W, Borner A-R, Fricke H, Kuhnel G, Meyer GJ, et al. [1-11C]Acetate as a quantitative perfusion tracer in myocardial PET. J Nucl Med 2001;42:1174-82.

    PubMed  Google Scholar 

  94. 94.

    Prior JO, Allenbach G, Valenta I, Kosinski M, Burger C, Verdun FR, et al. Quantification of myocardial blood flow with 82Rb positron emission tomography: Clinical validation with 15O-water. Eur J Nucl Med Mol Imaging 2012;39:1037-47.

    PubMed Central  PubMed  Article  Google Scholar 

  95. 95.

    Goldstein RA, Mullani NA, Marani SK, Fisher DJ, Gould KL, O’Brien HA. Myocardial perfusion with rubidium-82. II. Effects of metabolic and pharmacologic interventions. J Nucl Med 1983;24:907-15.

    CAS  PubMed  Google Scholar 

  96. 96.

    Mullani NA, Goldstein RA, Gould KL, Marani SK, Fisher DJ, O’Brien HA, et al. Myocardial perfusion with rubidium-82. I. Measurement of extraction fraction and flow with external detectors. J Nucl Med 1983;24:898-906.

    CAS  PubMed  Google Scholar 

  97. 97.

    Herrero P, Markham J, Shelton ME, Weinheimer CJ, Bergmann SR. Noninvasive quantification of regional myocardial perfusion with rubidium-82 and positron emission tomography. Exploration of a mathematical model. Circulation 1990;82:1377-86.

    CAS  PubMed  Article  Google Scholar 

  98. 98.

    Herrero P, Markham J, Shelton M, Bergmann S. Implementation and evaluation of a two-compartment model for quantification of myocardial perfusion with rubidium-82 and positron emission tomography. Circ Res 1992;70:496-507.

    CAS  PubMed  Article  Google Scholar 

  99. 99.

    Yoshida K, Mullani N, Gould KL. Coronary flow and flow reserve by PET simplified for clinical applications using rubidium-82 or nitrogen-13-ammonia. J Nucl Med 1996;37:1701-12.

    CAS  PubMed  Google Scholar 

  100. 100.

    Lammertsma AA. Quantification of cerebral blood flow. In: Gründer G, editor. Molecular Imaging in the Clinical Neurosciences. Neuromethods. New York: Humana Press; 2012. p. 99-109. doi:10.1007/7657_2012_43.

    Google Scholar 

  101. 101.

    Alpert NM, Barker WC, Gelman A, Weise S, Senda M, Correia JA. The precision of positron emission tomography: Theory and measurement. J Cereb Blood Flow Metab 1991;11:A26-30. doi:10.1038/jcbfm.1991.33.

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Fessler JA. Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography. IEEE Trans Image Process 1996;5:493-506.

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Qi J. A unified noise analysis for iterative image estimation. Phys Med Biol 2003;48:3505-19. doi:10.1088/0031-9155/48/21/004.

    PubMed  Article  Google Scholar 

  104. 104.

    Qi J, Huesman H. Theoretical study of penalized-likelihood image reconstruction for region of interest quantification. IEEE Trans Med Imaging 2006;25:640-8. doi:10.1109/TMI.2006.873223.

    CAS  PubMed  Article  Google Scholar 

  105. 105.

    Yaqub M, Boellaard R, Kropholler MA, Lammertsma AA. Optimization algorithms and weighting factors for analysis of dynamic PET studies. Phys Med Biol 2006;51:4217. doi:10.1088/0031-9155/51/17/007.

    PubMed  Article  Google Scholar 

  106. 106.

    Lautamäki R, George R, Kitagawa K, Higuchi T, Merrill J, Voicu C, et al. Rubidium-82 PET-CT for quantitative assessment of myocardial blood flow: Validation in a canine model of coronary artery stenosis. Eur J Nucl Med Mol Imaging 2009;36:576-86.

    PubMed  Article  CAS  Google Scholar 

  107. 107.

    Johnson NP, Gould KL. Physiological basis for angina and ST-segment change: PET-verified thresholds of quantitative stress myocardial perfusion and coronary flow reserve. JACC Cardiovasc Imaging 2011;4:990-8. doi:10.1016/j.jcmg.2011.06.015.

    PubMed  Article  Google Scholar 

  108. 108.

    Johnson NP, Gould KL. Integrating noninvasive absolute flow, coronary flow reserve, and ischemic thresholds into a comprehensive map of physiological severity. JACC Cardiovasc Imaging 2012;5:430-40. doi:10.1016/j.jcmg.2011.12.014.

    PubMed  Article  Google Scholar 

  109. 109.

    PMOD Technologies. User’s Guide: PMOD Cardiac Modeling (PCARD). http://pmod.com/technologies/pdf/doc/PCARDP.pdf. Accessed May 23, 2013.

  110. 110.

    Koeppe RA, Holden JE, Ip WR. Performance comparison of parameter estimation techniques for the quantitation of local cerebral blood flow by dynamic positron computed tomography. J Cereb Blood Flow Metab 1985;5:224-34. doi:10.1038/jcbfm.1985.29.

    CAS  PubMed  Article  Google Scholar 

  111. 111.

    Kadrmas DJ, Oktay MB. Generalized separable parameter space techniques for fitting 1K-5K serial compartment models. Med Phys 2013;40:072502. doi:10.1118/1.4810937.

    PubMed Central  PubMed  Article  Google Scholar 

  112. 112.

    Moody JB, Lee BC, Ficaro EP. Error estimation for dynamic PET myocardial blood flow. Soc Nucl Med Annu Meet Abstr 2012;53:323.

    Google Scholar 

  113. 113.

    Saraste A, Kajander S, Han C, Nesterov SV, Knuuti J. PET: Is myocardial flow quantification a clinical reality? J Nucl Cardiol 2012;19:1044-59.

    PubMed  Article  Google Scholar 

  114. 114.

    Positron Corporation. Integrating the Cardiac PET Supply Chain. 2014. http://www.positron.com/attrius. Accessed December 1, 2014.

  115. 115.

    INVIA Medical Imaging Solutions. Corridor4DM CFR. 2014. http://www.inviasolutions.com/corridor4dm. Accessed December 1, 2014.

  116. 116.

    Cardiovascular Imaging Technologies. ImagenQ: Absolute PET Quantification of cardiac PET. 2014. http://www.cvit.com/products/imagenq/. Accessed December 1, 2014.

  117. 117.

    Cedars-Sinai. Quantitative PET (QPET). 2014. http://cedars-sinai.edu/Patients/Programs-and-Services/Medicine-Department/Artificial-Intelligence-in-Medicine-AIM/Projects/Quantitative-PET-QPET.aspx. Accessed December 1, 2014.

  118. 118.

    Siemens. syngo.PET MBF (Cardiology Applications). syngo.via. 2014. http://www.healthcare.siemens.com/molecular-imaging/pet-ct/syngo-via/technical-details. Accessed December 1, 2014.

  119. 119.

    Slomka PJ, Alexanderson E, Jácome R, Jiménez M, Romero E, Meave A, et al. Comparison of clinical tools for measurements of regional stress and rest myocardial blood flow assessed with 13N-ammonia PET/CT. J Nucl Med 2012;53:171-81. doi:10.2967/jnumed.111.095398.

    CAS  PubMed  Article  Google Scholar 

  120. 120.

    Tahari AK, Lee A, Rajaram M, Fukushima K, Lodge MA, Lee BC, et al. Absolute myocardial flow quantification with 82Rb PET/CT: Comparison of different software packages and methods. Eur J Nucl Med Mol Imaging 2014;41:126-35.

    PubMed  Article  Google Scholar 

  121. 121.

    Sunderland JJ, Pan X-B, Declerck J, Menda Y. Dependency of cardiac rubidium-82 imaging quantitative measures on age, gender, vascular territory, and software in a cardiovascular normal population. J Nucl Cardiol 2015;22:72–84. doi:10.1007/s12350-014-9920-6.

    PubMed  Article  Google Scholar 

  122. 122.

    Nesterov SV, Deshayes E, Sciagrà R, Settimo L, Declerck JM, Pan X-B, et al. Quantification of myocardial blood flow in absolute terms using 82Rb PET imaging: Results of RUBY-10 study. JACC Cardiovasc Imaging 2014. doi:10.1016/j.jcmg.2014.08.003.

    PubMed  PubMed Central  Google Scholar 

  123. 123.

    Manabe O, Yoshinaga K, Katoh C, Naya M, Kemp RA, Tamaki N. Repeatability of rest and hyperemic myocardial blood flow measurements with 82Rb dynamic PET. J Nucl Med 2009;50:68-71.

    PubMed  Article  Google Scholar 

  124. 124.

    Efseaff M, Klein R, Ziadi MC, Beanlands RS, deKemp RA. Short-term repeatability of resting myocardial blood flow measurements using rubidium-82 PET imaging. J Nucl Cardiol 2012;19:997-1006.

    PubMed  Article  Google Scholar 

  125. 125.

    Fukushima K, Javadi MS, Higuchi T, Lautamäki 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;52:726-32. doi:10.2967/jnumed.110.081828.

    PubMed  Article  Google Scholar 

  126. 126.

    Muzik O, Duvernoy C, Beanlands R, Sawada S, Dayanikli F, Wolfe E, et al. Assessment of diagnostic performance of quantitative flow measurements in normal subjects and patients with angiographically documented coronary artery disease by means of nitrogen-13 ammonia and positron emission tomography. J Am Coll Cardiol 1998;31:534-40.

    CAS  PubMed  Article  Google Scholar 

  127. 127.

    Fiechter M, Ghadri JR, Gebhard C, Fuchs TA, Pazhenkottil AP, Nkoulou RN, et al. Diagnostic value of 13N-ammonia myocardial perfusion PET: Added value of myocardial flow reserve. J Nucl Med 2012;53:1230-4. doi:10.2967/jnumed.111.101840.

    CAS  PubMed  Article  Google Scholar 

  128. 128.

    Ziadi MC, deKemp RA, Williams K, Guo A, Renaud JM, Chow BJW, et al. Does quantification of myocardial flow reserve using rubidium-82 positron emission tomography facilitate detection of multivessel coronary artery disease? J Nucl Cardiol 2012;19:670-80. doi:10.1007/s12350-011-9506-5.

    PubMed  Article  Google Scholar 

  129. 129.

    Naya M, Murthy VL, Taqueti VR, Foster CR, Klein J, Garber M, et al. Preserved coronary flow reserve effectively excludes high-risk coronary artery disease on angiography. J Nucl Med 2014;55:248-55. doi:10.2967/jnumed.113.121442.

    PubMed Central  PubMed  Article  Google Scholar 

  130. 130.

    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. J Am Coll Cardiol Imaging 2009;2:751-8. doi:10.1016/j.jcmg.2009.04.004.

    Article  Google Scholar 

  131. 131.

    Kajander S, Joutsiniemi E, Saraste M, Pietilä M, Ukkonen H, Saraste A, et al. Cardiac positron emission tomography/computed tomography imaging accurately detects anatomically and functionally significant coronary artery disease. Circulation 2010;122:603-13. doi:10.1161/CIRCULATIONAHA.109.915009.

    CAS  PubMed  Article  Google Scholar 

  132. 132.

    Kaufmann PA, Gnecchi-Ruscone T, Yap JT, Rimoldi O, Camici PG. Assessment of the reproducibility of baseline and hyperemic myocardial blood flow measurements with 15O-labeled water and PET. J Nucl Med 1999;40:1848-56.

    CAS  PubMed  Google Scholar 

  133. 133.

    Wyss CA, Koepfli P, Mikolajczyk K, Burger C, von Schulthess GK, Kaufmann PA. Bicycle exercise stress in PET for assessment of coronary flow reserve: Repeatability and comparison with adenosine stress. J Nucl Med 2003;44:146-54.

    PubMed  Google Scholar 

  134. 134.

    Siegrist PT, Gaemperli O, Koepfli P, Schepis T, Namdar M, Valenta I, et al. Repeatability of cold pressor test-induced flow increase assessed with H 152 O and PET. J Nucl Med 2006;47:1420-6.

    PubMed  Google Scholar 

  135. 135.

    Schindler TH, Zhang X-L, Prior JO, Cadenas J, Dahlbom M, Sayre J, et al. Assessment of intra- and interobserver reproducibility of rest and cold pressor test-stimulated myocardial blood flow with 13N-ammonia and PET. Eur J Nucl Med Mol Imaging 2007;34:1178-88.

    PubMed  Article  Google Scholar 

  136. 136.

    Eriksson L, Wienhard K, Eriksson M, Casey ME, Knoess C, Bruckbauer T, et al. NEMA evaluation of the first and second generation of the Ecat Exact and Ecat Exact HR family of scanners. In: 2001 IEEE Nuclear Science Symposium Conference Record. vol 3, 2001:1223-26. doi:10.1109/NSSMIC.2001.1008556.

  137. 137.

    Herzog H, Tellmann L, Hocke C, Pietrzyk U, Casey ME, Kuwert T. NEMA NU2-2001 guided performance evaluation of four Siemens ECAT PET scanners. IEEE Trans Nucl Sci. 2004;51:2662-9. doi:10.1109/TNS.2004.835778.

    Article  Google Scholar 

  138. 138.

    Surti S, Karp JS. Imaging characteristics of a 3-dimensional GSO whole-body PET camera. J Nucl Med 2004;45:1040-9.

    CAS  PubMed  Google Scholar 

  139. 139.

    Bettinardi V, Danna M, Savi A, Lecchi M, Castiglioni I, Gilardi M, et al. Performance evaluation of the new whole-body PET/CT scanner: Discovery ST. Eur J Nucl Med Mol Imaging 2004;31:867-81. doi:10.1007/s00259-003-1444-2.

    PubMed  Article  Google Scholar 

  140. 140.

    Kemp BJ, Kim C, Williams JJ, Ganin A, Lowe VJ. NEMA NU 2-2001 performance measurements of an LYSO-based PET/CT system in 2D and 3D acquisition modes. J Nucl Med 2006;47:1960-7.

    PubMed  Google Scholar 

  141. 141.

    Teräs M, Tolvanen T, Johansson J, Williams J, Knuuti J. Performance of the new generation of whole-body PET/CT scanners: Discovery STE and discovery VCT. Eur J Nucl Med Mol Imaging 2007;34:1683-92. doi:10.1007/s00259-007-0493-3.

    PubMed  Article  Google Scholar 

  142. 142.

    Schelbert HR, Phelps ME, Huang SC, MacDonald NS, Hansen H, Selin C, et al. N-13 ammonia as an indicator of myocardial blood flow. Circulation 1981;63:1259-72.

    CAS  PubMed  Article  Google Scholar 

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Disclosure

J.B. Moody and B.C. Lee are employees of INVIA Medical Imaging Solutions, E.P. Ficaro and J.R. Corbett are stockholders of INVIA Medical Imaging Solutions, and V.L. Murthy has received research support from INVIA Medical Imaging Solutions, which produces a software package for myocardial blood flow estimation. V.L. Murthy has minor stock holdings in General Electric, Mallinckrodt, and Cardinal Health.

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Correspondence to Venkatesh L. Murthy MD, PhD.

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Moody, J.B., Lee, B.C., Corbett, J.R. et al. Precision and accuracy of clinical quantification of myocardial blood flow by dynamic PET: A technical perspective. J. Nucl. Cardiol. 22, 935–951 (2015). https://doi.org/10.1007/s12350-015-0100-0

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Keywords

  • Myocardial blood flow
  • Myocardial flow reserve
  • Cardiac PET/CT
  • Rubidium-82