New Radionuclides and Technological Advances in SPECT and PET Scanners

Developments throughout the history of nuclear medicine have involved improvements in both instrumentation and radionuclides, which have been intertwined. Instrumentation developments always occurred during the search to improving devices’ sensitivity and included advances in detector technology (with the introduction of cadmium zinc telluride and digital Positron Emission Tomography—PET-devices with silicon photomultipliers), design (total body PET) and configuration (ring-shaped, Single-Photon Emission Computed Tomography (SPECT), Compton camera). In the field of radionuclide development, we observed the continual changing of clinically used radionuclides, which is sometimes influenced by instrumentation technology but also driven by availability, patient safety and clinical questions. Some areas, such as tumour imaging, have faced challenges when changing radionuclides based on availability, when this produced undesirable clinical findings with the introduction of unclear focal uptakes and unspecific uptakes. On the other end of spectrum, further developments of PET technology have seen a resurgence in its use in nuclear cardiology, with rubidium-82 from strontium-82/rubidium-82 generators being the radionuclide of choice, moving away from SPECT nuclides thallium-201 and technetium-99m. These continuing improvements in both instrumentation and radionuclide development have helped the growth of nuclear medicine and its importance in the ever-evolving range of patient care options.

Comparison of the Prognostic Value of Global and Regional Myocardial Flow Capacity Radius, Myocardial Flow Reserve, and Stress Myocardial Blood Flow Using Rubidium-82 with SiPM PET/CT

Purpose The aim of this study was to assess the most reliable quantitative variable on Rubidium-82 (82Rb) cardiac PET/CT for predicting major adverse cardiovascular events (MACE), on the latest PET camera using silicon photomultipliers digital readout (SiPM) technology. Methods We prospectively enrolled 274 consecutive participants with suspected myocardial ischemia. Participants underwent 82Rb cardiac SiPM PET/CT and were followed-up for MACE over 652 days (interquartile range: 559 to 751 days). For each participant, global and regional myocardial flow reserve (MFR), stress myocardial blood flow (stress MBF) and their combination as myocardial flow capacity radius (MFC radius) were measured. Results On receiver operator curve analysis, MACE prediction was similar for global and regional MFR, stress MBF, and MFC radius (area under the curve; (i) Global: 0.70 vs. 0.71 and 0.73, and (ii) Regional: 0.71 vs. 0.71 and 0.73, respectively, p > 0.1 for all pairwise comparisons). On multivariable analysis, (i) Global: MFR < 1.98, stress MBF < 1.94 mL/g/min, and MFC radius < 3.12, as well as (ii) Regional: MFR < 1.75, stress MBF < 1.75 mL/g/min, and MFC radius < 2.7, emerged all as similar independent predictors of MACE (p < 0.001 for all). Conclusions Using the latest SiPM PET technology with 82Rb, global and regional MFR, stress MBF, and MFC radius are similar powerful predictors of cardiovascular event.

Relative Prognostic Significance of Positron Emission Tomography Myocardial Perfusion Imaging Markers in Cardiomyopathy

Background: Rubidium-82 positron emission tomography myocardial perfusion imaging provides measurements of perfusion, myocardial blood flow and reserve (MBFR), and changes in left ventricular ejection fraction (LVEF) at rest and peak stress. Although all of these variables are known to provide prognostic information, they have not been well studied in patients with heart failure due to reduced LVEF. Methods: Between 2010 and 2016, 1255 consecutive unique patients with LVEF≤40% were included in this study who underwent rubidium-82 positron emission tomography myocardial perfusion imaging and did not have subsequent revascularization within 90 days. Perfusion assessment was scored semiquantitatively, and LVEF reserve (stress-rest LVEF) and global MBFR (stress/rest MBF) were quantified using automated software. Cox proportional hazards models adjusted for 14 clinical and 7 test characteristics were used to define the independent prognostic significance of MBFR on all-cause mortality. Results: Of 1255 patients followed for a mean of 3.2 years, 454 (36.2%) died. After adjusting for clinical variables, the magnitude of fixed and reversible perfusion defects was prognostic of death ( P =0.02 and 0.01, respectively), while the rest LVEF was not ( P =0.18). The addition of LVEF reserve did not add any incremental value, while the addition of MBFR revealed incremental prognostic value (hazard ratio per 0.1 unit decrease in MBFR=1.08 [95% CI, 1.05–1.11], P <0.001) with fixed and reversible defects becoming nonsignificant ( P =0.07 and 0.29, respectively). There was no interaction between MBFR and cause of cardiomyopathy (ischemic versus nonischemic). Conclusions: In patients with a known cardiomyopathy who did not require early revascularization, reduced MBFR as obtained by positron emission tomography myocardial perfusion imaging is associated with all-cause mortality while other positron emission tomography myocardial perfusion imaging measures were not.

Value of regional myocardial flow measurements using Rubidium-82 PET

Funding Acknowledgements Type of funding sources: None. Introduction The combination of myocardial blood flow (MBF) measurements using Rubidium-82 (Rb-82) PET and visual assessment of the PET images is increasingly used due to its high diagnostic and prognostic value. Typically, flow measurements are calculated and used for the myocardium as a whole (global). However, small regional flow deficits may go unnoticed when only looking at global flow values. Purpose To compare the diagnostic value of regional and global myocardial flow measurements using Rb-82 PET in the detection of obstructive CAD. Methods We retrospectively included 1034 patients with no history of coronary artery disease (CAD) referred for rest and regadenoson-induced stress Rb-82 PET/CT. MBFs were calculated using Lortie’s one-tissue compartment model. Myocardial flow reserve (MFR) was calculated as the ratio of MBF during stress and rest. Regional flow was determined per vessel and per segment. Vessel MFR was defined as the lowest flow reserve of LAD, LCX and RCA territories and segmental MFR as the lowest flow reserve in all 17 segments. Follow-up data were obtained from medical records. Patients were classified to have obstructive CAD if follow-up included a positive invasive coronary angiography (ICA), percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG) or all cause death. Receiver-operating characteristic (ROC) analyses were constructed to compare the diagnostic value of global and regional flow values. Results Follow-up was obtained in all 1034 patients and the median follow-up time was 2.1 years. Myocardial flow reserve values were significantly lower (p &lt; 0.001) in the 128 patients classified with obstructive CAD than in the 906 patients without obstructive CAD: global MFR (median 1.9 [interquartile range 1.6-2.4] vs. 2.5 [2.1-2.9]); vessel MFR (1.6 [1.3-2.1] vs. 2.3 [1.9-2.6]); Segmental MFR (1.3 [0.9-1.7] vs. 1.9 [1.6-2.2]). The area under the curve of vessel MFR (0.79 ± 0.02) and segmental MFR (0.81 ± 0.02) were similar but significantly (p &lt; 0.001) larger than the area of global MFR (0.75 ± 0.03), as shown in the Figure. Conclusion The diagnostic value improved with the use of regional MFR instead of global MFR measurements in the detection of obstructive CAD. Therefore, it seems that visual assessment of PET images can best be combined with regional flow measurements either on a per vessel or a per segment basis in Rubidium-82 PET myocardial perfusion imaging.

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

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 &gt; 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 &lt; 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.

Prediction of obstructive coronary artery disease after Rb-82 PET myocardial perfusion imaging and coronary artery calcium scoring using machine learning

Funding Acknowledgements Type of funding sources: None. Background Accurate risk stratification in patients with suspected stable coronary artery disease (CAD) is essential for choosing an appropriate treatment strategy but remains challenging in clinical practice. Purpose Our aim was to develop and validate a risk model to predict the presence of obstructive CAD after Rubidium-82 PET and a coronary artery calcium score (CACS) scan using a machine learning (ML) algorithm. Methods We retrospectively included 1007 patients without prior cardiovascular history and a low-intermediate pre-test likelihood, referred for rest and regadenoson-induced stress Rubidium-82 PET combined with a CACS scan. Multiple features were included in the ML model; PET derived features such as summed difference score and flow values, CACS, cardiovascular risk factors (cigarette smoking, hypertension, hypercholesterolemia, diabetes, positive family history of CAD), medication; age; gender; body mass index; creatinine serum values; and visual PET interpretation. An XGBoost ML algorithm was developed using a subset of 805 patients to predict obstructive CAD by using 5-fold cross validation in combination with a grid search. Obstructive CAD during follow-up was defined as a significant stenosis during invasive coronary angiography, a percutaneous coronary intervention or a coronary artery bypass graft procedure. The ML algorithm was validated with unseen data of the remaining 202 patients. Results Application of the XGBoost algorithm resulted in an area under the curve (AUC) of 0.93 using the training data (n = 805) and an AUC of 0.89 using the unseen data (n = 202) in predicting obstructive CAD. The strongest predictors were the CAC-scores and quantitative PET derived features. The classical risk factors and medication hardly provided an added value in the prediction of obstructive CAD. Conclusion The developed ML algorithm is able to provide individualized risk stratification by predicting the probability of obstructive CAD. Although validation with a larger dataset could result in a more well defined performance range, this model already shows potential to be implemented in the diagnostic workflow.