Myocardial perfusion quantification with Rb-82 PET: good interobserver agreement of Carimas software on global, regional, and segmental levels.

PURPOSE: To estimate the interobserver agreement of the Carimas software package (SP) on global, regional, and segmental levels for the most widely used myocardial perfusion PET tracer-Rb-82. MATERIALS AND METHODS: Rest and stress Rb-82 PET scans of 48 patients with suspected or known coronary artery disease (CAD) were analyzed in four centers using the Carimas SP. We considered values to agree if they simultaneously had an intraclass correlation coefficient (ICC) > 0.75 and a difference < 20% of the median across all observers. RESULTS: The median values on the segmental level were 1.08 mL/min/g for rest myocardial blood flow (MBF), 2.24 mL/min/g for stress MBF, and 2.17 for myocardial flow reserve (MFR). For the rest MBF and MFR, all the values at all the levels fulfilled were in excellent agreement. For stress MBF, at the global and regional levels, all the 24 comparisons showed excellent agreement. Only 1 out of 102 segmental comparisons (seg. 14) was over the adequate agreement limit-23.5% of the median value (ICC = 0.95). CONCLUSION: Interobserver agreement for Rb-82 PET myocardial perfusion quantification analyzed with Carimas is good at any LV segmentation level-global, regional, and segmental. It is good for all the estimates-rest MBF, stress MBF, and MFR.

One-tissue compartment model for myocardial perfusion quantification with N-13 ammonia PET provides matching results: A cross-comparison between Carimas, FlowQuant, and PMOD.

PURPOSE: To cross-compare three software packages (SPs)-Carimas, FlowQuant, and PMOD-to quantify myocardial perfusion at global, regional, and segmental levels. MATERIALS AND METHODS: Stress N-13 ammonia PET scans of 48 patients with HCM were analyzed in three centers using Carimas, FlowQuant, and PMOD. Values agreed if they had an ICC > 0.75 and a difference < 20% of the median across all observers. RESULTS: When using 1TCM on the global level, the agreement was good, and the maximum difference between 1TCM MBF values was 17.2% (ICC = 0.83). On the regional level, the agreement was acceptable except in the LCx region (25.5% difference, ICC = 0.74) between FlowQuant and PMOD. Carimas-1TCM agreed well with PMOD-1TCM and FlowQuant-1TCM. Values obtained with FlowQuant-1TCM had a somewhat lesser agreement with PMOD-1TCM, especially at the segmental level. CONCLUSIONS: The global and regional MBF values (with one exception) agree well between the different software packages. There is significant variability in segmental values, mainly located in the LCx region and segments. Out of the studied tools, Carimas can be used interchangeably with both PMOD and FlowQuant for 1TCM implementation on all levels-global, regional, and segmental.

Quantification of myocardial blood flow in absolute terms using (82)Rb PET imaging: the RUBY-10 Study.

OBJECTIVES: The purpose of this study was to compare myocardial blood flow (MBF) and myocardial flow reserve (MFR) estimates from rubidium-82 positron emission tomography ((82)Rb PET) data using 10 software packages (SPs) based on 8 tracer kinetic models. BACKGROUND: It is unknown how MBF and MFR values from existing SPs agree for (82)Rb PET. METHODS: Rest and stress (82)Rb PET scans of 48 patients with suspected or known coronary artery disease were analyzed in 10 centers. Each center used 1 of 10 SPs to analyze global and regional MBF using the different kinetic models implemented. Values were considered to agree if they simultaneously had an intraclass correlation coefficient >0.75 and a difference <20% of the median across all programs. RESULTS: The most common model evaluated was the Ottawa Heart Institute 1-tissue compartment model (OHI-1-TCM). MBF values from 7 of 8 SPs implementing this model agreed best. Values from 2 other models (alternative 1-TCM and Axially distributed) also agreed well, with occasional differences. The MBF results from other models (e.g., 2-TCM and retention) were less in agreement with values from OHI-1-TCM. CONCLUSIONS: SPs using the most common kinetic model-OHI-1-TCM-provided consistent results in measuring global and regional MBF values, suggesting that they may be used interchangeably to process data acquired with a common imaging protocol.

SBP ratio in exercise stress testing: validation by perfusion imaging.

AIMS: The 3-min SBP ratio (SBPR) was proposed to detect significant coronary artery disease (CAD), but its relationship with abnormalities detected by myocardial perfusion-gated single-photon emission computed tomography (SPECT) was never examined. METHODS: In 156 consecutive patients submitted to resting and exercise gated SPECT for suspect CAD, perfusion scores, ejection fraction and transient ischemic dilatation (TID) were evaluated and compared with SBPR. RESULTS: There were weak correlations between SBPR and summed stress score (ρ = 0.232, P < 0.004), and summed difference score (SDS) (ρ = 0.228, P < 0.004). According to receiver operating characteristic analysis, SBPR was poorly effective for identifying patients with inducible ischemia (SDS ≥ 2): area under curve = 0.64, (P < 0.002), sensitivity = 82%, specificity = 40%. No relationship was registered between SBPR and postexercise ejection fraction drop, and the relationship with TID was poor (ρ = 0.263, P < 0.001). CONCLUSION: In patients submitted to gated SPECT for suspect CAD, SBPR appears poorly effective for the detection of significant CAD, and does not show any valuable relationship with exercise-induced functional abnormalities.

Prognostic implications of post-stress ejection fraction decrease detected by gated SPECT in the absence of stress-induced perfusion abnormalities.

PURPOSE: The prognostic meaning of a post-stress ejection fraction (EF) decrease detected by perfusion gated SPECT is still unclear.We therefore followed up patients with post-stress EF decrease in the absence of stress-induced perfusion abnormalities. METHODS: We prospectively enrolled 57 consecutive patients with post-stress EF drop ≥ 5 EF units and summed difference score (SDS) ≤ 1. They were followed up for more than 1 year and their outcome was compared with a group of sex- and age-matched controls with the same SDS but without EF decrease. RESULTS: During follow-up there were 13 events (1 cardiac death, 1 non-fatal myocardial infarction, 1 congestive heart failure and 10 late revascularizations). In the control group we registered six events. There was a significant difference (p<0.0001) between the event-free survival curves of the two groups. CONCLUSION: The event rate of patients with post-stress EF decrease ≥ 5 EF units is relatively high and is significantly worse than that of a control group of patients with similarly normal SDS but without EF changes. Therefore, a post-stress EF decrease without stress-induced perfusion abnormalities should be cautiously interpreted.