Search

Osteoarthritis is a common cause of pain and morbidity resulting in heavy economic burden and large societal costs. Although cross-sectional imaging and in particular MR imaging have largely contributed to a better understanding of the complexity of this complex disease, especially in large joints such as the hip and knee joints, metabolic information of the subchondral bone and periarticular synovial environment has been consistently suggested to provide valuable supplemental information to morphologic and compositional MR imaging. The aim of this narrative review is to provide an overview of the role of the hybrid PET imaging in osteoarthritis with particular focus on PET/MR imaging.

This mini review delves into the mechanisms of [18F]Sodium Fluoride positron emission tomography ([18F]NaF PET), which, by interrogating areas of newly mineralizing bone, provides a valuable tool to study the joint response to loading and areas of altered whole-joint function in osteoarthritis (OA).

Although computed tomography (CT) and MR imaging alone have been used extensively to evaluate various musculoskeletal disorders, hybrid imaging modalities of PET-CT and PET–MR imaging were recently developed, combining the advantages of each method: molecular information from PET and anatomical information from CT or MR imaging. Furthermore, different radiotracers can be used in PET to uncover different disease mechanisms. In this article, potential applications of PET-CT and PET–MR imaging for benign musculoskeletal disorders are organized by benign cell proliferation/dysplasia, diabetic foot complications, joint prostheses, degeneration, inflammation, and trauma, metabolic bone disorders, and pain (acute and chronic) and peripheral nerve imaging.

The osteoarthritis (OA) research community has been advocating a shift from radiography-based screening criteria and outcome measures in OA clinical trials to a magnetic resonance imaging (MRI)-based definition of eligibility and endpoint. For conventional morphological MRI, various semiquantitative evaluation tools are available. We have lately witnessed a remarkable technological advance in MRI techniques, including compositional/physiologic imaging and automated quantitative analyses of articular and periarticular structures. More recently, additional technologies were introduced, including positron emission tomography (PET)-MRI, weight-bearing computed tomography (CT), photon-counting spectral CT, shear wave elastography, contrast-enhanced ultrasound, multiscale X-ray phase contrast imaging, and spectroscopic photoacoustic imaging of cartilage. On top of these, we now live in an era in which artificial intelligence is increasingly utilized in medicine. Osteoarthritis imaging is no exception. Successful implementation of artificial intelligence (AI) will hopefully improve the workflow of radiologists, as well as the level of precision and reproducibility in the interpretation of images.

Injectable biomaterials are being developed for a wide range of biomedical applications; however, characterization of materials (e.g., distribution, chemical composition) after injection is often difficult and relies on invasive and destructive procedures. To address this problem, this study utilizes a new magnetic resonance imaging (MRI) acquisition technique based on chemical exchange saturation transfer (CEST), where the signal relies on the exchange of protons in specific molecules with bulk water protons. Such a signal can be generated from specific functional groups endogenous to or engineered into a desired material. Here, CEST MRI was used to visualize injectable hyaluronic acid (HA) hydrogels either alone or after injection into tissue. The CEST effect was shown to track with changes in material properties–as hydrogel macromer concentration was increased, the CEST contrast increased linearly. Furthermore, CEST MRI was used to detect hydrogels injected into cardiac explants with an increase in signal at the hydrogel site relative to the surrounding myocardial signal. Unlike conventional MRI, CEST can simultaneously visualize and discriminate between different injectable materials based on their unique chemistry. To illustrate this, we tuned the CEST signal to detect differences in two hydrogel systems based on their dominant functional groups. The covalent addition of an arginine-based peptide to HA hydrogels led to a 2-fold increase in signal when the exchangeable amine (−NH2) protons in the peptide were targeted. Thus, CEST MRI could become a valuable tool for studying injectable hydrogel properties and enable further optimization of biomaterial therapies aimed at clinical translation.