Machine learning methods for tracer kinetic modelling

Abstract

Tracer kinetic modelling based on dynamic PET is an important field of Nuclear Medicine for quantitative functional imaging. Yet, its implementation in clinical routine has been constrained by its complexity and computational costs. Machine learning poses an opportunity to improve modelling processes in terms of arterial input function prediction, the prediction of kinetic modelling parameters and model selection in both clinical and preclinical studies while reducing processing time. Moreover, it can help improving kinetic modelling data used in downstream tasks such as tumor detection. In this review, we introduce the basics of tracer kinetic modelling and present a literature review of original works and conference papers using machine learning methods in this field.

Zusammenfassung

Die Modellierung der Kinetik von Tracern auf der Grundlage der dynamischen PET ist ein wichtiger Bereich der quantitativen funktionellen Bildgebung in der Nuklearmedizin. Ihre Umsetzung in der klinischen Routine wird jedoch durch ihre Komplexität und ihre Rechenkosten eingeschränkt. Das maschinelle Lernen bietet die Möglichkeit, die Modellierungsprozesse im Hinblick auf die Vorhersage der arteriellen Eingangsfunktion, die Berechnung der kinetischen Modellierungsparameter und die Modellauswahl sowohl in klinischen als auch in präklinischen Studien zu verbessern und gleichzeitig die Verarbeitungszeit zu verkürzen. Darüber hinaus kann sie dazu beitragen, den Nutzen von kinetischen Modellierungsdaten bei nachgelagerten Aufgaben, wie z. B. der Tumorerkennung, zu verbessern. In dieser Übersicht stellen wir die Grundlagen der kinetischen Modellierung von Tracern vor und präsentieren eine Literaturübersicht über Originalarbeiten und Konferenzbeiträge, die Methoden des maschinellen Lernens in diesem Bereich verwenden.

Key words

tracer kinetic modelling - physiologically-based pharmacokinetic modelling - PBPK modelling - machine learning - deep learning

Publikationsverlauf

Eingereicht: 01. September 2023

Angenommen: 21. September 2023

Artikel online veröffentlicht:
11. Oktober 2023

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