Positron emission tomography-magnetic resonance imaging as a research tool in musculoskeletal conditions

Open-source phantom with dedicated in-house software for image quality assurance in hybrid PET systems

BACKGROUND

Patients' diagnosis, treatment and follow-up increasingly rely on multimodality imaging. One of the main limitations for the optimal implementation of hybrid systems in clinical practice is the time and expertise required for applying standardized protocols for equipment quality assurance (QA). Experimental phantoms are commonly used for this purpose, but they are often limited to a single modality and single quality parameter, lacking automated analysis capabilities. In this study, we developed a multimodal 3D-printed phantom and software for QA in positron emission tomography (PET) hybrid systems, with computed tomography (CT) or magnetic resonance (MR), by assessing signal, spatial resolution, radiomic features, co-registration and geometric distortions.

RESULTS

Phantom models and Python software for the proposed QA are available to download, and a user-friendly plugin compatible with the open-source 3D-Slicer software has been developed. The QA viability was proved by characterizing a Philips-Gemini-TF64-PET/CT in terms of signal response (mean, µ), intrinsic variability for three consecutive measurements (daily variation coefficient, CoVd) and reproducibility over time (variation coefficient across 5 months, CoVm). For this system, averaged recovery coefficient for activity concentration was µ = 0.90 ± 0.08 (CoVd = 0.6%, CoVm = 9%) in volumes ranging from 7 to 42 ml. CT calibration-curve averaged over time was HU = ( 951 ± 12 ) × density - ( 944 ± 15 ) with variability of slope and y-intercept of (CoVd = 0.4%, CoVm = 1.2%) and (CoVd = 0.4%, CoVm = 1.6%), respectively. Radiomics reproducibility resulted in (CoVd = 18%, CoVm = 30%) for PET and (CoVd = 15%, CoVm = 22%) for CT. Co-registration was assessed by Dice-Similarity-Coefficient (DSC) along 37.8 cm in superior-inferior (z) direction (well registered if DSC ≥ 0.91 and Δz ≤ 2 mm), resulting in 3/7 days well co-registered. Applicability to other scanners was additionally proved with Philips-Vereos-PET/CT (V), Siemens-Biograph-Vison-600-PET/CT (S) and GE-SIGNA-PET/MR (G). PET concentration accuracy was (µ = 0.86, CoVd = 0.3%) for V, (µ = 0.87, CoVd = 0.8%) for S, and (µ = 1.10, CoVd = 0.34%) for G. MR(T2) was well co-registered with PET in 3/4 cases, did not show significant distortion within a transaxial diameter of 27.8 cm and along 37 cm in z, and its radiomic variability was CoVd = 13%.

CONCLUSIONS

Open-source QA protocol for PET hybrid systems has been presented and its general applicability has been proved. This package facilitates simultaneously simple and semi-automated evaluation for various imaging modalities, providing a complete and efficient QA solution.

Physiopathology of patello-femoral osteoarthritis: current concepts

Patellofemoral osteoarthritis (PFOA) is the result of degeneration and loss of articular cartilage of the patella and trochlea, and is a common cause of anterior knee pain. PFOA is triggered by insufficient adaptation to overload of the articular cartilage of the PF joint created by abnormal biomechanics. It is important to understand the pathophysiology and natural history to make the diagnosis and to plan treatment. Innate factors including malalignment, patellar instability, kinematic disorders, and acquired factors like trauma, obesity, and endocrine diseases have been found to be causes of PFOA. Genetic predisposition is also described as a contributing cause but without much scientific evidence. The diagnosis will be based on clinical manifestations, such as anterior knee pain aggravated by overloading activities, identification of risk factors, and exclusion of referred pain from other pathologies, followed by a systematic and structured physical examination. Imaging will be useful for assessing the presence of early osteoarthritis in the other compartments, for classification of the PFOA, and to identify features to establish an adequate treatment. This paper discusses varying management options for different causes of patellofemoral disease and explains the complexity of the PF joint and its often poorly understood biomechanics.

Latest advancements in imaging techniques in OA

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.