On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

Julia Franziska Brosch-Lenz; Astrid Delker; Fabian Schmidt; Johannes Tran-Gia

doi:10.1055/a-2179-6872

Nuklearmedizin - NuclearMedicine, Inhaltsverzeichnis

Nuklearmedizin 2023; 62(06): 379-388
DOI: 10.1055/a-2179-6872

Review

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

Über die Verwendung von künstlicher Intelligenz für die Dosimetrie radiopharmazeutischer Therapien

Autor*innen

Julia Franziska Brosch-Lenz‡

¹Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany (Ringgold ID: RIN27190)
Astrid Delker‡

²Department of Nuclear Medicine, LMU University Hospital, Munich, Germany
Fabian Schmidt‡

³Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany

⁴Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Tuebingen, Germany (Ringgold ID: RIN505890)
Johannes Tran-Gia‡

⁵Department of Nuclear Medicine, University Hospital Wuerzburg, Wuerzburg, Germany (Ringgold ID: RIN27207)

Abstract

Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.

Zusammenfassung

Routinemäßige klinische Dosimetrie ist unabdingbar für die zukünftige Personalisierung von radiopharmazeutischen Therapien. Allgemein wird die Dosimetrie jedoch als komplex und zeitaufwendig betrachtet, und die erforderlichen Schritte innerhalb des Dosimetrie-Workflows sind mit verschiedenen Herausforderungen verbunden. Der allgemeine Workflow für die bildbasierte Dosimetrie besteht aus der quantitativen Bildgebung, der Segmentierung von Organen und Tumoren, dem Modellieren der Zeit-Aktivitäts-Kurven und der Umrechnung in die absorbierte Dosis. In dieser Arbeit werden das Potenzial und die Vorteile des Einsatzes von künstlicher Intelligenz zur Verbesserung der Geschwindigkeit und Genauigkeit jedes einzelnen Schrittes des Dosimetrie-Workflows untersucht.

Keywords

Dosimetry - Artificial Intelligence - Radiopharmaceutical Therapies

Volltext

Referenzen

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