Accuracy of PET quantification in [68Ga]Ga-pentixafor PET/MR imaging of carotid plaques

Potential novel imaging targets of inflammation in cardiac sarcoidosis

Cardiac sarcoidosis (CS) is an inflammatory disease with high morbidity and mortality, with a pathognomonic feature of non-caseating granulomatous inflammation. While 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a well-established modality to image inflammation and diagnose CS, there are limitations to its specificity and reproducibility. Imaging focused on the molecular processes of inflammation including the receptors and cellular microenvironments present in sarcoid granulomas provides opportunities to improve upon FDG-PET imaging for CS. This review will highlight the current limitations of FDG-PET imaging for CS while discussing emerging new nuclear imaging molecular targets for the imaging of cardiac sarcoidosis.

Potentials and caveats of AI in hybrid imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.