UNLABELLED: We have addressed 2 major challenges of (82)Rb cardiac PET, noninvasive estimation of an accurate input function and absolute quantitation of myocardial perfusion, using a generalized form of least-squares factor analysis of dynamic sequences (GFADS) and a novel compartment analysis approach. METHODS: Left and right ventricular (LV + RV) time-activity curves (TACs) were generated from 10 rest/stress studies, and 30 myocardial TACs were modeled to cover a range of clinical values. Two-dimensional PET Monte Carlo simulations of the LV, RV, myocardium, and other organs were generated separately and combined using the above TACs to form 30 realistic dynamic (82)Rb studies. LV and RV TACs were estimated by GFADS and used as input to a 2-compartment kinetic analysis that estimates parametric maps of myocardial tissue extraction (k(1)) and egress (k(2)), as well as LV + RV contributions (f(v), r(v)), by orthogonal voxel grouping. In addition, 13 patients were injected with 2.22 +/- 0.19 GBq (60 +/- 5 mCi) of (82)Rb and imaged dynamically for 6 min at rest and during dipyridamole stress. RESULTS: In Monte Carlo simulations, GFADS yielded estimates of the 3 factors and corresponding factor images, with average errors of -4.2% +/- 6.3%, 3.5% +/- 4.3%, and 2.0% +/- 5.5% in the LV, RV, and myocardial factor estimates, respectively. The estimates were significantly more accurate and robust to noise than those obtained using TACs based on manually drawn volumes of interest (P < 0.01). The 2-compartment approach yielded accurate k(1), k(2), f(v), and r(v) parametric maps; the average error of estimates of k(1) was 6.8% +/- 3.6%. In all patient studies, our approach yielded robust estimates of k(1), k(2), f(v), and r(v), which correlated very well with the status of the subject and the catheterization results. CONCLUSION: Quantitative dynamic (82)Rb PET using generalized factor analysis of dynamic sequences and compartmental modeling yields estimates of parameters of absolute myocardial perfusion and kinetics with errors of <9%.
UNLABELLED: We have addressed 2 major challenges of (82)Rb cardiac PET, noninvasive estimation of an accurate input function and absolute quantitation of myocardial perfusion, using a generalized form of least-squares factor analysis of dynamic sequences (GFADS) and a novel compartment analysis approach. METHODS: Left and right ventricular (LV + RV) time-activity curves (TACs) were generated from 10 rest/stress studies, and 30 myocardial TACs were modeled to cover a range of clinical values. Two-dimensional PET Monte Carlo simulations of the LV, RV, myocardium, and other organs were generated separately and combined using the above TACs to form 30 realistic dynamic (82)Rb studies. LV and RV TACs were estimated by GFADS and used as input to a 2-compartment kinetic analysis that estimates parametric maps of myocardial tissue extraction (k(1)) and egress (k(2)), as well as LV + RV contributions (f(v), r(v)), by orthogonal voxel grouping. In addition, 13 patients were injected with 2.22 +/- 0.19 GBq (60 +/- 5 mCi) of (82)Rb and imaged dynamically for 6 min at rest and during dipyridamole stress. RESULTS: In Monte Carlo simulations, GFADS yielded estimates of the 3 factors and corresponding factor images, with average errors of -4.2% +/- 6.3%, 3.5% +/- 4.3%, and 2.0% +/- 5.5% in the LV, RV, and myocardial factor estimates, respectively. The estimates were significantly more accurate and robust to noise than those obtained using TACs based on manually drawn volumes of interest (P < 0.01). The 2-compartment approach yielded accurate k(1), k(2), f(v), and r(v) parametric maps; the average error of estimates of k(1) was 6.8% +/- 3.6%. In all patient studies, our approach yielded robust estimates of k(1), k(2), f(v), and r(v), which correlated very well with the status of the subject and the catheterization results. CONCLUSION: Quantitative dynamic (82)Rb PET using generalized factor analysis of dynamic sequences and compartmental modeling yields estimates of parameters of absolute myocardial perfusion and kinetics with errors of <9%.
Authors: Hendrik J Harms; Paul Knaapen; Stefan de Haan; Rick Halbmeijer; Adriaan A Lammertsma; Mark Lubberink Journal: Eur J Nucl Med Mol Imaging Date: 2011-01-27 Impact factor: 9.236
Authors: Silvia C Eufinger; John Votaw; Tracy Faber; Thomas R Ziegler; Jack Goldberg; J Douglas Bremner; Viola Vaccarino Journal: Am J Clin Nutr Date: 2012-01-18 Impact factor: 7.045
Authors: Venkatesh L Murthy; Masanao Naya; Courtney R Foster; Jon Hainer; Mariya Gaber; Gilda Di Carli; Ron Blankstein; Sharmila Dorbala; Arkadiusz Sitek; Michael J Pencina; Marcelo F Di Carli Journal: Circulation Date: 2011-10-17 Impact factor: 29.690
Authors: Ashley M Groves; Marie-Elsya Speechly-Dick; John C Dickson; Irfan Kayani; Raymondo Endozo; Patty Blanchard; Manu Shastry; Elizabeth Prvulovich; Wendy A Waddington; Simona Ben-Haim; Jamshed B Bomanji; Jean R McEwan; Peter J Ell Journal: Eur J Nucl Med Mol Imaging Date: 2007-08-31 Impact factor: 9.236
Authors: Ankur Gupta; Viviany R Taqueti; Tim P van de Hoef; Navkaranbir S Bajaj; Paco E Bravo; Venkatesh L Murthy; Michael T Osborne; Sara B Seidelmann; Tomas Vita; Courtney F Bibbo; Meagan Harrington; Jon Hainer; Ornella Rimoldi; Sharmila Dorbala; Deepak L Bhatt; Ron Blankstein; Paolo G Camici; Marcelo F Di Carli Journal: Circulation Date: 2017-09-01 Impact factor: 29.690
Authors: Andrew Van Tosh; John R Votaw; Nathaniel Reichek; Christopher J Palestro; Kenneth J Nichols Journal: J Nucl Cardiol Date: 2013-10-04 Impact factor: 5.952