A Linearized Fit Model for Robust Shape Parameterization of FET-PET TACs

• Published in: IEEE transactions on medical imaging Vol. 40; no. 7; pp. 1852 - 1862
• Main Authors: Lerche, Christoph W., Radomski, Timon, Lohmann, Philipp, Caldeira, Liliana, Brambilla, Claudia Regio, Tellmann, Lutz, Scheins, Jurgen, Kops, Elena Rota, Galldiks, Norbert, Langen, Karl-Josef, Herzog, Hans, Jon Shah, N.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain modeling; Classification; Diagnostic systems; FET PET; Field effect transistors; Glioma; glioma classification; Image processing; Imaging; Inspection; Isocitrate dehydrogenase; Kinetic theory; Linearization; Mathematical models; Neuroscience; Parameter estimation; Parameter uncertainty; Parameterization; parametric imaging; Positron emission; Robustness (mathematics); Shape; Tomography; Tumors; Visual discrimination
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2021.3067169

The kinetic analysis of <inline-formula> <tex-math notation="LaTeX">^{{18}}\text{F} </tex-math></inline-formula>-FET time-activity curves (TAC) can provide valuable diagnostic information in glioma patients. The analysis is most often limited to the average TAC over a large tissue volume and is normally assessed by visual inspection or by evaluating the time-to-peak and linear slope during the late uptake phase. Here, we derived and validated a linearized model for TACs of <inline-formula> <tex-math notation="LaTeX">^{{18}}\text{F} </tex-math></inline-formula>-FET in dynamic PET scans. Emphasis was put on the robustness of the numerical parameters and how reliably automatic voxel-wise analysis of TAC kinetics was possible. The diagnostic performance of the extracted shape parameters for the discrimination between isocitrate dehydrogenase (IDH) wildtype (wt) and IDH-mutant (mut) glioma was assessed by receiver-operating characteristic in a group of 33 adult glioma patients. A high agreement between the adjusted model and measured TACs could be obtained and relative, estimated parameter uncertainties were small. The best differentiation between IDH-wt and IDH-mut gliomas was achieved with the linearized model fitted to the averaged TAC values from dynamic FET PET data in the time interval 4-50 min p.i.. When limiting the acquisition time to 20-40 min p.i., classification accuracy was only slightly lower (-3%) and was comparable to classification based on linear fits in this time interval. Voxel-wise fitting was possible within a computation time <inline-formula> <tex-math notation="LaTeX">\approx ~{1} </tex-math></inline-formula> min per image slice. Parameter uncertainties smaller than 80% for all fits with the linearized model were achieved. The agreement of best-fit parameters when comparing voxel-wise fits and fits of averaged TACs was very high (p < 0.001).