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19. Comparison of myocardial blood flow estimates from dynamic contrast-enhanced used in Cardiac Magnetic Resonance Imaging
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Abstracts / Physica Medica 56 (2018) 59–132
3. Tofts P, Quantitative MRI of the brain: measuring changes caused by disease. John Wiley and Sons; 2004. https://doi.org/10.1016/j.ejmp.2018.04.028
19. Comparison of myocardial blood flow estimates from dynamic contrast-enhanced used in Cardiac Magnetic Resonance Imaging M. Carnı` a, C. Borrazzo b,c, N. Galea c, F. Vullo c, M. Francone c, C. Catalano c, I. Carbone c, M. Pacilio a a
Azienda Ospedaliera Universitaria Policlinico Umberto I, Medical Physics Division, Rome, Italy b Sapienza University of Rome, Department of Radiological, Oncological and Pathological Science, Rome, Italy c Sapienza University of Rome, Department of Public Health and Infectious Disease, Rome, Italy Purpose. Dynamic Contrast-Enhanced Cardiovascuar Magnetic Resonance Imaging (DCE-CMRI) may quantitatively assess the Myocardial Blood Flow (MBF), recovering the tissue impulseresponse function to transit of gadolinium bolus through myocardium. Several deconvolution techniques are available, using various models for the impulse-response [1]. The method choice may influence the results, producing differences that have not been deeply investigated yet. Methods. Three methods for quantifying myocardial perfusion were compared: Fermi Function Modeling, Tofts Model, and Gamma Function model, the latter traditionally used in brain perfusion. Thirty human subjects were studied at rest, and Cold Pressor Test stress, injecting a single-bolus of gadolinium of 0.1 mmol/kg. Perfusion estimate differences among methods were analysed by paired comparisons with Student’s t-test, linear regression analysis, and Bland-Altman plots, and using also the two-way ANOVA, considering the MBF values of all patients grouped according to two categories: calculation method and rest/stress conditions. Results. Perfusion estimates obtained by various methods in both rest and stress conditions were not significantly different, and in agreement with the literature. Results obtained during the firstpass transit time (20 s) yielded p-values in the range 0.20–0.28 for the Student’s t-test, slopes from the linear regression analysis between 0.98–1.03, and R values between 0.92–1.01. With the two-way ANOVA, the results were p = 0.20 for the method effect (not significant), p < 0.0001 for the rest/stress condition effect, whereas no interaction resulted between rest/stress condition and
method (p = 0.70, not significant). Considering a wider period (60 s), the estimates for both rest and stress conditions were 25–30% higher than those obtained in the 20 s period. Conclusion. MBF estimates obtained by various methods at rest/ stress condition were not significantly different in the first-pass transit time, encouraging quantitative perfusion estimates in DCECMRI with the used methods.
Reference 1. Borrazzo C et al.. Myocardial blood flow estimates from dynamic contrast-enhanced magnetic resonance imaging: three quantitative methods. Phys Med Biol 2017;63(3). https://doi.org/10.1016/j.ejmp.2018.04.029
20. Diffusion MRI and ADC accuracy at the isocenter: An AIFM multisite comparison study A. Coniglio a, L. Fedeli d, G. Belli b, A. Ciccarone c, M. Esposito e, M. Giannelli f, G. Gobbi ae, C. Gori d, L.N. Mazzoni g, L. Nocetti h, R. Sghedoni i, R. Tarducci j, L. Altabella k, E. Belligotti l, M. Benelli g, M. Betti m, R. Caivano n, M. Carnì o, A. Chiappiniello af, S. Cimolai p, F. Cretti q, C. Fulcheri j, C. Gasperi r, M. Giacometti s, F. Levrero t, D. Lizio u, C. Luchinat v, M. Maieron w, S. Marzi x, L. Mascaro y, S. Mazzocchi e, G. Meliadò z, S. Morzenti aa, L. Noferini ab, N. Oberhofer ac, M.G. Quattrocchi ab, A. Ricci ad, A. Taddeucci b, L. Tenori v, A. Torresin ae, S. Busoni b a
A.O. Fatebenefratelli, Roma, Italy A.O.U. Careggi, U.O.C. Fisica Sanitaria, Firenze, Italy c A.O.U. Mayer, Firenze, Italy d University of Firenze, Dept. ‘‘Fisica e Astronomia”, Firenze, Italy e A.U.S.L. Toscana Centro, Firenze, Italy f A.O.U. Pisana, Pisa, Italy g A.U.S.L. Toscana Centro, Pistoia, Italy h A.O.U. Policlinico di Modena, Italy i A.U.S.L. di Reggio Emilia, Reggio Emilia, Italy j A.O.U Perugia, Perugia, Italy k Ospedale S. Raffaele, Milano, Italy l OspedaliRiuniti Marche Nord, Pesaro, Italy m A.U.S.L. Toscana Centro, Prato, Italy b
ADCref/ADCC-1
ADCref/ADCC-1
10%
5% 0% -5%
1.5 T 3T mean
-10% 0
5
10
15
20
25
30
Scanner Percentage difference between ADCref and ADCcert: blue (red) marks refer to scanner with B0 of 1.5T (3T). Green line is the mean value from 26 considered scanner (dotted lines are ±SD). Result from scanner no. 17 are outside the plot.
Abstracts / Physica Medica 56 (2018) 59–132
3. Tofts P, Quantitative MRI of the brain: measuring changes caused by disease. John Wiley and Sons; 2004. https://doi.org/10.1016/j.ejmp.2018.04.028
19. Comparison of myocardial blood flow estimates from dynamic contrast-enhanced used in Cardiac Magnetic Resonance Imaging M. Carnı` a, C. Borrazzo b,c, N. Galea c, F. Vullo c, M. Francone c, C. Catalano c, I. Carbone c, M. Pacilio a a
Azienda Ospedaliera Universitaria Policlinico Umberto I, Medical Physics Division, Rome, Italy b Sapienza University of Rome, Department of Radiological, Oncological and Pathological Science, Rome, Italy c Sapienza University of Rome, Department of Public Health and Infectious Disease, Rome, Italy Purpose. Dynamic Contrast-Enhanced Cardiovascuar Magnetic Resonance Imaging (DCE-CMRI) may quantitatively assess the Myocardial Blood Flow (MBF), recovering the tissue impulseresponse function to transit of gadolinium bolus through myocardium. Several deconvolution techniques are available, using various models for the impulse-response [1]. The method choice may influence the results, producing differences that have not been deeply investigated yet. Methods. Three methods for quantifying myocardial perfusion were compared: Fermi Function Modeling, Tofts Model, and Gamma Function model, the latter traditionally used in brain perfusion. Thirty human subjects were studied at rest, and Cold Pressor Test stress, injecting a single-bolus of gadolinium of 0.1 mmol/kg. Perfusion estimate differences among methods were analysed by paired comparisons with Student’s t-test, linear regression analysis, and Bland-Altman plots, and using also the two-way ANOVA, considering the MBF values of all patients grouped according to two categories: calculation method and rest/stress conditions. Results. Perfusion estimates obtained by various methods in both rest and stress conditions were not significantly different, and in agreement with the literature. Results obtained during the firstpass transit time (20 s) yielded p-values in the range 0.20–0.28 for the Student’s t-test, slopes from the linear regression analysis between 0.98–1.03, and R values between 0.92–1.01. With the two-way ANOVA, the results were p = 0.20 for the method effect (not significant), p < 0.0001 for the rest/stress condition effect, whereas no interaction resulted between rest/stress condition and
method (p = 0.70, not significant). Considering a wider period (60 s), the estimates for both rest and stress conditions were 25–30% higher than those obtained in the 20 s period. Conclusion. MBF estimates obtained by various methods at rest/ stress condition were not significantly different in the first-pass transit time, encouraging quantitative perfusion estimates in DCECMRI with the used methods.
Reference 1. Borrazzo C et al.. Myocardial blood flow estimates from dynamic contrast-enhanced magnetic resonance imaging: three quantitative methods. Phys Med Biol 2017;63(3). https://doi.org/10.1016/j.ejmp.2018.04.029
20. Diffusion MRI and ADC accuracy at the isocenter: An AIFM multisite comparison study A. Coniglio a, L. Fedeli d, G. Belli b, A. Ciccarone c, M. Esposito e, M. Giannelli f, G. Gobbi ae, C. Gori d, L.N. Mazzoni g, L. Nocetti h, R. Sghedoni i, R. Tarducci j, L. Altabella k, E. Belligotti l, M. Benelli g, M. Betti m, R. Caivano n, M. Carnì o, A. Chiappiniello af, S. Cimolai p, F. Cretti q, C. Fulcheri j, C. Gasperi r, M. Giacometti s, F. Levrero t, D. Lizio u, C. Luchinat v, M. Maieron w, S. Marzi x, L. Mascaro y, S. Mazzocchi e, G. Meliadò z, S. Morzenti aa, L. Noferini ab, N. Oberhofer ac, M.G. Quattrocchi ab, A. Ricci ad, A. Taddeucci b, L. Tenori v, A. Torresin ae, S. Busoni b a
A.O. Fatebenefratelli, Roma, Italy A.O.U. Careggi, U.O.C. Fisica Sanitaria, Firenze, Italy c A.O.U. Mayer, Firenze, Italy d University of Firenze, Dept. ‘‘Fisica e Astronomia”, Firenze, Italy e A.U.S.L. Toscana Centro, Firenze, Italy f A.O.U. Pisana, Pisa, Italy g A.U.S.L. Toscana Centro, Pistoia, Italy h A.O.U. Policlinico di Modena, Italy i A.U.S.L. di Reggio Emilia, Reggio Emilia, Italy j A.O.U Perugia, Perugia, Italy k Ospedale S. Raffaele, Milano, Italy l OspedaliRiuniti Marche Nord, Pesaro, Italy m A.U.S.L. Toscana Centro, Prato, Italy b
ADCref/ADCC-1
ADCref/ADCC-1
10%
5% 0% -5%
1.5 T 3T mean
-10% 0
5
10
15
20
25
30
Scanner Percentage difference between ADCref and ADCcert: blue (red) marks refer to scanner with B0 of 1.5T (3T). Green line is the mean value from 26 considered scanner (dotted lines are ±SD). Result from scanner no. 17 are outside the plot.