The temporomandibular joint (TMJ) is typically involved in 45-87% of children with Juvenile Idiopathic Arthritis (JIA). Accurate diagnosis of JIA is difficult as various clinical tests, including MRI, disagree. The purpose of this study is to optimize the methodological aspects of Dynamic Contrast Enhanced (DCE) MRI of the TMJ in children. In this cross-sectional study, including data from 73 JIA affected children, aged 6-15 years, effects of motion correction, sampling rate and parametric modelling on DCE-MRI data is investigated. Consensus among three radiologists determined the regions of interest. Quantitative perfusion parameters were estimated using four perfusion models; the Adiabatic Approximation to Tissue Homogeneity (AATH), Distributed Capillary Adiabatic Tissue Homogeneity (DCATH), Gamma Capillary Transit Time (GCTT) and Two Compartment Exchange (2CXM) models. Effects of motion correction were evaluated by a sum of least squares between corrected raw data and the GCTT model. The effect of systematically down sampling the raw data was tested. The sum of least squares was computed across all pharmacokinetic models. Relative difference perfusion parameters between the left and right TMJ were used for an unsupervised k-means based stratification of the data based on a principal component analysis, as well as for a supervised random forest classification. Diagnostic sensitivity and specificity were computed relative to structural image scorings. Paired sample t-tests, as well as ANOVA tests, were used (significant threshold: p < 0.05) with Tukeys post hoc test. High-level elastic motion correction provides the best least square fit to the GCTT model (percental improvement: 72-84%). A 4 s sampling rate captures more of the potentially disease relevant signal variations. The various parametric models all leave comparable residues (relative standard deviation: 3.4%). In further evaluation of DCE-MRI as a potential diagnostic tool for JIA a high-level elastic motion correction scheme should be adopted, with a sampling rate of at least 4 s. Results suggest that DCE-MRI data can be a valuable part in JIA diagnostics in the TMJ.

Department of Clinical Medicine UiT The Arctic University of Norway Norway

Department of Physics and Technology University of Bergen Bergen Norway; Mohn Medical Imaging and Visualization Centre Haukeland University Hospital Bergen Norway

Department of Physics and Technology University of Bergen Bergen Norway; Mohn Medical Imaging and Visualization Centre Haukeland University Hospital Bergen Norway; Department of Radiology Haukeland University Hospital Bergen Norway

Department of Radiology and Nuclear Medicine St Olav Hospital HF Trondheim Norway; Department of Circulation and Medical Imaging Norwegian University of Science and Technology Trondheim Norway

Institute of Scientific Instruments of the Czech Academy of Sciences Brno Czechia

Mohn Medical Imaging and Visualization Centre Haukeland University Hospital Bergen Norway; Department of Clinical Engineering Haukeland University Hospital Bergen Norway

Section for Paediatric Radiology University Hospital of North Norway Tromsø Norway; Department of Clinical Medicine UiT The Arctic University of Norway Norway

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