Verbeterde analyse van hersen-PET-beelden met machine learning

This thesis investigates how machine learning can be integrated with established PET kinetic modelling, non-invasive quantification, supervised classification, and network analysis to advance brain PET image analysis. Across these four topics, the work progresses from local voxel-wise estimates to global network-level characterizations, while maintaining a consistent emphasis on tracer kinetics, quantification, classification, and practical clinical applicability. Chapter 2 introduces a PET kinetics-informed neural network (PKiNN) to generate voxel-wise one-tissue compartment model (1TCM) parametric mapsfrom dynamic PET data using an arterial input function. Using a compact LSTM architecture, PKiNN enables fast inference and reduced computational load compared with conventional voxel-wise fits. Embedding 1TCM constraints into the loss function improves parameter estimation, yielding close agreement with conventional 1TCM fits in simulated data and strong concordance with volume of interest (VOI)-based kinetic parameter estimates in clinical 11C-UCB-J data, while mitigating the underestimation observed for Logan graphical analysis. Chapter 3 explores SVM-derived weight maps as a data-driven strategy for pseudo-reference region selection to support non-invasive quantification using SUVR or reference-tissue modelling and to facilitate voxel-wise group comparisons without arterial sampling. Applied to 11C-PiB (AD vs controls), 11C-UCB-J (baseline vs blocking), and 18F-DPA-714 (chemo-treated breast cancer patients vs controls), this approach produces tracer-specific pseudo-reference region candidates, with cerebellum and brainstem emerging as consistent options across cohorts. The stability of candidate region rankings supports feasibility in small cohorts while highlighting tracer- and dataset-specific requirements. Chapter 4 assesses the potential and limitations of voxel-wise 18F-FDG PET for ALS phenotyping using SVM classification. High performance is achieved for ALS versus healthy volunteers (>80%), with weight maps reflecting established ALS-related metabolic patterns. In contrast, classification remains limited for more challenging tasks, including ALS vs mimics and bulbar vs spinal onset, with accuracy