Venkatesh L Murthy1, Benjamin C Lee2, Arkadiusz Sitek3, Masanao Naya4, Jonathan Moody2, Vivek Polavarapu5, Edward P Ficaro6, Marcelo F Di Carli4. 1. Noninvasive Cardiovascular Imaging Program, Departments of Radiology and Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan vlmurthy@med.umich.edu. 2. INVIA Medical Imaging Solutions, Ann Arbor, Michigan; and. 3. Division of Nuclear Medicine, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts. 4. Noninvasive Cardiovascular Imaging Program, Departments of Radiology and Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 5. Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan. 6. Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan INVIA Medical Imaging Solutions, Ann Arbor, Michigan; and.
Abstract
UNLABELLED: The quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR) using PET with (82)Rb in patients with known or suspected coronary artery disease has been demonstrated to have substantial prognostic and diagnostic value. However, multiple methods for estimation of an image-derived input function and several models for the nonlinear first-pass extraction of (82)Rb by myocardium have been used. We sought to compare the differences in these methods and models and their impact on prognostic assessment in a large clinical dataset. METHODS: Consecutive patients (n = 2,783) underwent clinically indicated rest-stress myocardial perfusion PET with (82)Rb. The input function was derived using a region of interest (ROI) semiautomatically placed in the region of the mitral valve, factor analysis, and a hybrid method that creates an ROI from factor analysis. We used 5 commonly used extraction models for (82)Rb to estimate MBF and MFR. Pearson correlations, bias, and Cohen κ were computed for the various measures. The relationship between MFR/stress MBF and annual rate of cardiac mortality was estimated with spline fits using Poisson regression. Finally, incremental value was assessed with the net reclassification improvement using Cox proportional hazards regression. RESULTS: Correlations between MFR or stress MBF measures made with the same input function derivation method were generally high, regardless of extraction model used (Pearson r > 0.90). However, correlations between measures derived with the ROI method and other methods were only moderate (Pearson r = 0.42-0.62). Importantly, substantial biases were seen for most combinations. We saw that the relationship between cardiac mortality and stress MBF was variable depending on the input function method and extraction model, whereas the relationship between MFR and risk was highly consistent. Net reclassification improvement was comparable for most methods and models for MFR but was highly variable for stress MBF. CONCLUSION: Although both stress MBF and MFR can improve prognostic assessment, MFR is substantially more consistent, regardless of choice of input function derivation method and extraction model used.
UNLABELLED: The quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR) using PET with (82)Rb in patients with known or suspected coronary artery disease has been demonstrated to have substantial prognostic and diagnostic value. However, multiple methods for estimation of an image-derived input function and several models for the nonlinear first-pass extraction of (82)Rb by myocardium have been used. We sought to compare the differences in these methods and models and their impact on prognostic assessment in a large clinical dataset. METHODS: Consecutive patients (n = 2,783) underwent clinically indicated rest-stress myocardial perfusion PET with (82)Rb. The input function was derived using a region of interest (ROI) semiautomatically placed in the region of the mitral valve, factor analysis, and a hybrid method that creates an ROI from factor analysis. We used 5 commonly used extraction models for (82)Rb to estimate MBF and MFR. Pearson correlations, bias, and Cohen κ were computed for the various measures. The relationship between MFR/stress MBF and annual rate of cardiac mortality was estimated with spline fits using Poisson regression. Finally, incremental value was assessed with the net reclassification improvement using Cox proportional hazards regression. RESULTS: Correlations between MFR or stress MBF measures made with the same input function derivation method were generally high, regardless of extraction model used (Pearson r > 0.90). However, correlations between measures derived with the ROI method and other methods were only moderate (Pearson r = 0.42-0.62). Importantly, substantial biases were seen for most combinations. We saw that the relationship between cardiac mortality and stress MBF was variable depending on the input function method and extraction model, whereas the relationship between MFR and risk was highly consistent. Net reclassification improvement was comparable for most methods and models for MFR but was highly variable for stress MBF. CONCLUSION: Although both stress MBF and MFR can improve prognostic assessment, MFR is substantially more consistent, regardless of choice of input function derivation method and extraction model used.
Authors: Lauren A Baldassarre; Subha V Raman; James K Min; Jennifer H Mieres; Martha Gulati; Nanette K Wenger; Thomas H Marwick; Chiara Bucciarelli-Ducci; C Noel Bairey Merz; Dipti Itchhaporia; Keith C Ferdinand; Carl J Pepine; Mary Norine Walsh; Jagat Narula; Leslee J Shaw Journal: JACC Cardiovasc Imaging Date: 2016-04
Authors: Jonathan B Moody; Benjamin C Lee; James R Corbett; Edward P Ficaro; Venkatesh L Murthy Journal: J Nucl Cardiol Date: 2015-04-14 Impact factor: 5.952
Authors: Mark A Marinescu; Adrián I Löffler; Michelle Ouellette; Lavone Smith; Christopher M Kramer; Jamieson M Bourque Journal: JACC Cardiovasc Imaging Date: 2015-02