pyPatlak: Open-Source Voxel-Wise Patlak Analysis of Dynamic PET Data

BackgroundThe Patlak graphical analysis is a widely used method for quantifying irreversible tracer uptake in dynamic Positron Emission Tomography (PET) studies, providing key kinetic parameters such as the net influx rate (Ki) and the total distribution volume (Vd). Currently, this analysis is primarily performed using proprietary software or vendor-specific workstations, which can limit its accessibility, flexibility, and reproducibility. This work presents pyPatlak, an open-source, Python-based, platform-independent tool that performs Patlak modelling directly from dynamic PET DICOM data, offering an accessible alternative to proprietary software. MethodsThe pyPatlak script processes dynamic PET data in DICOM format to generate 3D parametric images of Ki and Vd. Key features include the normalization of a population-based input function (PBIF) with a patient-specific arterial input function (AIF), and optional correction for the partial volume effect (PVE). The script performs a voxel-by-voxel linear regression to derive the kinetic parameters. To validate the script, we compared the Ki and Vd values generated by pyPatlak with those obtained from a commercial workstations direct Patlak analysis. This validation was performed on 21 patients by segmenting seven organs of interest and comparing the values of the kinetic parameters. ResultsPyPatlak showed a good agreement with the reference direct Patlak reconstruction. Correlation analysis demonstrated strong linear relationships (Ki: R = 0.91, Vd: R = 0.93), and Lins concordance coefficients confirmed high agreement (Ki: 0.89, Vd: 0.91). Bland-Altman analysis indicated that observed differences were minimal and clinically negligible. Mean biases were approximately -0.03 ml/min/100ml for Ki and +2.2 units for Vd. Equivalence testing further confirmed that all differences fell within predefined clinically acceptable limits, despite being statistically significant in Wilcoxon signed-rank tests. ConclusionpyPatlak offers a flexible, reproducible, and transparent alternative to proprietary software for Patlak analysis. Its open-source nature and compatibility with standard DICOM data make it a valuable tool for researchers, promoting greater accessibility and standardization of kinetic modelling in PET imaging.