Entropy-based myocardial blood flow measurements using PET: a way to improve reproducibility

Abstract

Funding Acknowledgements Type of funding sources: None. Background and purpose Myocardial blood flow (MBF) measurements using PET are increasingly used to guide the management of patients with (suspected) coronary artery disease (CAD). Day-to-day variability of these measurements is poor with a 21% standard deviation or 40% 95%-confidence interval [Reference: JACC Cardiovasc Imaging, 2017;10(5):565]. This limits clinical applicability in diagnosis, risk stratification and follow-up as these all depend on comparison of flow values with fixed cut-off values. We expect that reproducibility can be improved by combining flow measurements with the variation of flow values within the myocardium. As entropy is a measure of variability of the associated distribution, we compared the reproducibility of an entropy-based flow parameter with that of conventional myocardial flow reserve (MFR) measurements. Methods We performed a study using intra-individual comparison in 24 patients who underwent rest and regadenoson-induced stress myocardial perfusion imaging using Rubidium-82 on two different PET systems (PET1: Discovery 690, GE Healthcare, and PET2: Vereos, Philips Healthcare) within 3 weeks. MBF for both rest and stress was calculated using Lortie’s one-tissue compartment model (Corridor4DM, INVIA). MFR (ratio of MBF stress/rest) was determined for the myocardial as a whole (MFRglobal), for the three vascular territories: LAD, LCX and RCA (MFRregional) and for the 17 segments. Next, we calculated Shannon’s entropy to measure the variation of the 17 MFR segmental values. We multiplied Shannon’s entropy by the mean of the MFR segmental values resulting in an entropy-based MFR (MFRentropy). For each patient MFRglobal, MFRregional and MRFentropy were compared between both PET systems. For each of the three parameters the test-retest precision was calculated as the SD of the relative difference between measurements. Results The mean difference in MFR measurements between both cameras did not differ from zero (p > 0.05). Mean values for PET1 were MFRglobal = 2.4, MFRregional = 2.4 (LAD), 2.4 (LCX) and 2.5 (RCA), and MFRentropy = 2.4. For PET2 we found MFRglobal = 2.5, MFRregional = 2.5 (LAD), 2.4 (LCX) and 2.6 (RCA), and MFRentropy = 2.5. Test-retest precision was lower for MFRentropy with 11% compared to that of MFRglobal (21%), MFRregional LAD (22%), MFRregional LCX (23%) and MFRregional RCA (24%) (p < 0.01). Conclusion The reproducibility of myocardial flow reserve measurements using Rubidium-82 PET improved by a factor of 2 when an entropy-based flow parameter instead of global or regional MFR parameters is used. This entropy-based flow-parameter may be used to better discriminate ischemia from non-ischemia and may therefore improve CAD management.

Machine learning based model to diagnose obstructive coronary artery disease using calcium scoring, PET imaging, and clinical data

“…MPE can be interpreted as the amount of disorder between the 17segmental MBF values and can possibly better discriminate ischemic from non-ischemic areas as compared to global or regional MBF values. 12 MPE was calculated using Shannon's equation for entropy 13 with the 17segmental MFR values as input. 12…”

“…12 MPE was calculated using Shannon's equation for entropy 13 with the 17segmental MFR values as input. 12…”