Utility of serum ketone levels for assessment of myocardial glucose suppression for 18F-fluorodeoxyglucose PET in patients referred for evaluation of endocarditis

Point-of-care β-hydroxybutyrate measurement predicts adequate glucose metabolism suppression in cardiac FDG-PET/CT

AIMS

The aims of our study were to evaluate whether point-of-care β-hydroxybutyrate (BHB) measurement can be used to identify patients with adequate cardiac glucose metabolism suppression for cardiac [18F]-fluoro-2-deoxy-d-glucose-positron emission tomography with computerized tomography (FDG-PET/CT) and to develop a pretest probability calculator of myocardial suppression using other metabolic factors attainable before imaging.

METHODS AND RESULTS

We recruited 193 patients with any clinical indication for whole body [18F]-FDG-PET/CT. BHB level was measured with a point-of-care device. Maximal myocardial standardized uptake value using lean body mass (SULmax) was measured from eight circular regions of interest with 1 cm circumference and background from left ventricular blood pool. Correlations SULmax and point-of-care measured BHB were analysed. The ability of BHB test to predict adequate suppression was evaluated with receiver operating characteristic analysis. Liver and spleen attenuation in computed tomography were measured to assess the presence of fatty liver. BHB level correlated with myocardial uptake and, using a cut-off value of 0.35 mmol/L to predict adequate myocardial suppression, we reached specificity of 90% and sensitivity of 56%. Other variables to predict adequate suppression were diabetes, obesity, ketogenic diet and fatty liver. Using information attainable before imaging, we created a pretest probability calculator of inadequate myocardial glucose metabolism suppression. The area under the curve for BHB test alone was 0.802 and was 0.857 for the pretest calculator (p = 0.319).

CONCLUSIONS

BHB level measured with a point-of-care device is useful in predicting adequate myocardial glucose metabolism suppression. More detailed assessment of other factors potentially contributing to cardiac metabolism is needed.

Human cardiac metabolism

The heart is the most metabolically active organ in the human body, and cardiac metabolism has been studied for decades. However, the bulk of studies have focused on animal models. The objective of this review is to summarize specifically what is known about cardiac metabolism in humans. Techniques available to study human cardiac metabolism are first discussed, followed by a review of human cardiac metabolism in health and in heart failure. Mechanistic insights, where available, are reviewed, and the evidence for the contribution of metabolic insufficiency to heart failure, as well as past and current attempts at metabolism-based therapies, is also discussed.

Automated Deep Learning Segmentation of Cardiac Inflammatory FDG PET

Background

Fluorodeoxyglucose positron emission tomography (FDG PET) with glycolytic metabolism suppression plays a pivotal role in diagnosing cardiac sarcoidosis. Reorientation of images to match perfusion datasets is critical and myocardial segmentation enables consistent image scaling and quantification. However, both are challenging and labor intensive. We developed a 3D U-Net deep learning (DL) algorithm for automated myocardial segmentation in cardiac sarcoidosis FDG PET.

Methods

The DL model was trained on 316 patients' FDG PET scans, and left ventricular contours derived from perfusion datasets. Qualitative analysis of clinical readability was performed to compare DL segmentation with the current automated method on a 50-patient test subset. Additionally, left ventricle displacement and angulation, as well as SUVmax sampling were compared to inter-user reproducibility results.

Results

DL segmentation enhanced readability scores in over 90% of cases compared to the standard segmentation currently used in the software. DL segmentation performed similarly to a trained technologist, surpassing standard segmentation for left ventricle displacement and angulation, as well as correlation of SUVmax.

Conclusion

The DL-based automated segmentation tool presents a marked improvement in the processing of cardiac sarcoidosis FDG PET, promising enhanced clinical workflow. This tool holds significant potential for accelerating clinical practice and improving consistency and quality. Further research with varied datasets is warranted to broaden its applicability.

Standardized ketogenic dietary preparation for metabolic PET imaging in suspected and known cardiac sarcoidosis

Aims

A major limitation of cardiac positron emission tomography (PET) with F18-fluorodeoxyglucose (F18-FDG) for the evaluation of cardiac sarcoidosis (CS) is associated with physiologic myocardial glucose uptake. The optimal dietary protocol to suppress physiologic myocardial F18-FDG uptake is not well-established. We aimed to evaluate the diagnostic performance of a novel dietary preparation using a ketone-based infant formula.

Methods and results

Between 2018 and 2021, consecutive studies using a ketogenic dietary preparation were identified (n = 198). The rate of non-diagnostic studies due to failure to suppress myocardial glucose was 7.1% (n = 14) with a similar incidence in diabetics (n = 6, 8.1%). Among studies reported to have no inflammation (n = 137), 130 studies (66%) had mean myocardial standardized uptake value (SUV) less than or equal to mean blood pool SUV.

Conclusion

Patient preparation with a ketone-based infant formula resulted in low rate of inappropriate myocardial glucose suppression in patients undergoing F18-FDG cardiac PET to evaluate CS.