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Pixel-by-pixel analysis of DCE-MRI curve shape patterns in knees of active and inactive juvenile idiopathic arthritis
Robert Hemke1, Cristina Lavini, Charlotte M Nusman
1Department of Radiology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands, r.hemke@amc.nl.
Objectives:
To compare DCE-MRI parameters and the relative number of time-intensity curve (TIC) shapes as derived from pixel-by-pixel DCE-MRI TIC shape analysis between knees of clinically active and inactive juvenile idiopathic arthritis (JIA) patients.
Methods:
DCE-MRI data sets were prospectively obtained. Patients were classified into two clinical groups: active disease (n = 43) and inactive disease (n = 34). Parametric maps, showing seven different TIC shape types, were created per slice. Statistical measures of different TIC shapes, maximal enhancement (ME), maximal initial slope (MIS), initial area under the curve (iAUC), time-to-peak (TTP), enhancing volume (EV), volume transfer constant (K(trans)), extravascular space fractional volume (V(e)) and reverse volume transfer constant (k(ep)) of each voxel were calculated in a three-dimensional volume-of-interest of the synovial membrane.
Results:
Imaging findings from 77 JIA patients were analysed. Significantly higher numbers of TIC shape 4 (P = 0.008), median ME (P = 0.015), MIS (P = 0.001) and iAUC (P = 0.002) were observed in clinically active compared with inactive patients. TIC shape 5 showed higher presence in the clinically inactive patients (P = 0.036).
Conclusions:
The pixel-by-pixel DCE-MRI TIC shape analysis method proved capable of differentiating clinically active from inactive JIA patients by the difference in the number of TIC shapes, as well as the descriptive parameters ME, MIS and iAUC.
Key Points:
• The pixel-by-pixel TIC shape method differentiates clinically active and inactive JIA patients • Significantly higher numbers of TIC shape 4 were observed in clinically active patients • DCE-MRI parameters ME, MIS and iAUC differ between active and inactive patients • The pixel-by-pixel analysis method allows direct visualization of the heterogeneously distributed disease • The DCE-MRI TIC shape method may serve as a quantitative outcome measure.
