It is possible to obtain accurate estimates of myocardial blood flow and flow reserve with a one-compartment model of (82)Rb kinetics and a nonlinear extraction function.

Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AIbased algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.

(82)Rb MFR is an independent predictor of 3-vessel CAD and provided added value to relative MPI. Clinical integration of this approach should be considered to enhance detection and risk assessment of patients with known or suspected CAD.

Objective To compare myocardial blood flow (MBF) and myocardial flow reserve (MFR) estimates from 82Rb PET data using ten software packages (SPs): Carimas, Corridor4DM, FlowQuant, HOQUTO, ImagenQ, MunichHeart, PMOD, QPET, syngo MBF, and UW-QPP. Background It is unknown how MBF and MFR values from existing SPs agree for 82Rb PET. Methods Rest and stress 82Rb PET scans of 48 patients with suspected or known coronary artery disease (CAD) were analyzed in 10 centers. Each center used one of the 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 (ICC) > 0.75 and a difference < 20% of the median across all programs. Results The most common model evaluated was the one-tissue compartment model (1TCM) by Lortie et al. (2007). MBF values from seven of the eight software packages implementing this model agreed best (Carimas, Corridor4DM, FlowQuant, PMOD, QPET, syngoMBF, and UW-QPP). Values from two other models (El Fakhri et al. in Corridor4DM and Alessio et al. in UW-QPP) also agreed well, with occasional differences. The MBF results from other models (Sitek et al. 1TCM in Corridor4DM, Katoh et al. 1TCM in HOQUTO, Herrero et al. 2TCM in PMOD, Yoshida et al. retention in ImagenQ, and Lautamäki et al. retention in MunichHeart) were less in agreement with Lortie 1TCM values. Conclusions SPs using the same kinetic model, as described in Lortie et al. (2007), provided consistent results in measuring global and regional MBF values, suggesting they may be used interchangeably to process data acquired with a common imaging protocol.

Patient body motion can produce MBF estimation errors up to 500%. To reduce these errors, new motion correction algorithms must be effective in identifying motion in the left/right direction, and in the mid-to-late time-frames, since these conditions produce the largest errors in MBF, particularly for high resolution PET imaging. Ideally, motion correction should be done before or during image reconstruction to eliminate PET-CTAC misalignment artifacts.