Effect of temporal sampling protocols on myocardial blood flow measurements using Rubidium-82 PET

Abstract

Background A variety of temporal sampling protocols is used worldwide to measure myocardial blood flow (MBF). Both the length and number of time frames in these protocols may alter MBF and myocardial flow reserve (MFR) measurements. We aimed to assess the effect of different clinically used temporal sampling protocols on MBF and MFR quantification in Rubidium-82 (Rb-82) PET imaging. Methods We retrospectively included 20 patients referred for myocardial perfusion imaging using Rb-82 PET. A literature search was performed to identify appropriate sampling protocols. PET data were reconstructed using 14 selected temporal sampling protocols with time frames of 5-10 seconds in the first-pass phase and 30-120 seconds in the tissue phase. Rest and stress MBF and MFR were calculated for all protocols and compared to the reference protocol with 26 time frames. Results MBF measurements differed (P ≤ 0.003) in six (43%) protocols in comparison to the reference protocol, with mean absolute relative differences up to 16% (range 5%-31%). Statistically significant differences were most frequently found for protocols with tissue phase time frames < 90 seconds. MFR did not differ (P ≥ 0.11) for any of the protocols. Conclusions Various temporal sampling protocols result in different MBF values using Rb-82 PET. MFR measurements were more robust to different temporal sampling protocols.

Patient-tailored risk assessment of obstructive coronary artery disease using Rubidium-82 PET-based myocardial flow quantification with visual interpretation

“…Ideally, other centers should recreate the probability function for their own settings as absolute values may differ from ours due to difference in patient population, PET scanner, acquisition, reconstruction, and post-processing techniques. 14 – 17 However, we do expect to see a similar shape of this function with a strong dependence on MFR, for both visually normal and abnormal scans.…”

“…Third, the probability of oCAD as a function of (global or segmental) MFR as derived in our study may not be similar for other centers. Both patient population and acquisition, reconstruction, and post-processing protocols may differ which can result in different MFR values [14][15][16][17] and, hence, in a different probability function. However, we do not expect its shape to be different: patient's probability to have oCAD is likely to depend strongly on MFR, for both visually normal and abnormal scans.…”

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

“…Although most input parameters of the ML model are or can be standardized (clinical information and CACS), MBF measurements based on PET are generally not standardized among different centers and depend on several technical aspects such as reconstructions settings and post-processing software. [21][22][23][24] Still, for each center it is possible and even recommendable to retrain and test the ML model to their unique patient data. Although this will take effort, it will lead to center-specific optimized hyperparameter values and hence likely to the best diagnostic performance.…”

Diagnostic value of regional myocardial flow reserve measurements using Rubidium-82 PET

“…Future studies will have to indicate which segmental MFR cut-off values are most suitable for this purpose. less affected by technical variations such as reconstruction settings including temporal sampling, kinetic modelling and the software being used as compared to MBF [22][23][24][25]. Therefore, we only focused on MFR.…”