Effects of motion correction, sampling rate and parametric modelling in dynamic contrast enhanced MRI of the temporomandibular joint in children affected with juvenile idiopathic arthritis
| Autor: | Ondřej Macíček, Karen Rosendahl, Thomas A. Augdal, Renate Grüner, Lea Starck, Oskar W Angenete, Radovan Jiřík, Erling Andersen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Adolescent Movement Biomedical Engineering Biophysics Least squares Sensitivity and Specificity 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine VDP::Teknologi: 500::Medisinsk teknologi: 620 Post-hoc analysis Statistics medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Child Mathematics Models Statistical Temporomandibular Joint Homogeneity (statistics) Magnetic Resonance Imaging Arthritis Juvenile Temporomandibular joint medicine.anatomical_structure Cross-Sectional Studies Child Preschool Parametric model Dynamic contrast-enhanced MRI Principal component analysis Female Analysis of variance Artifacts 030217 neurology & neurosurgery VDP::Technology: 500::Medical technology: 620 |
| Zdroj: | Magnetic Resonance Imaging |
| Popis: | 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. publishedVersion |
| Databáze: | OpenAIRE |
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