TY - JOUR AU - Küstner, Thomas; Hepp, Tobias; Seith, Ferdinand TI - Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities TT - Multiparametrische onkologische Hybridbildgebung: Herausforderungen und Chancen für maschinelles Lernen SN - 1438-9029 SN - 1438-9010 PY - 2022 JO - Rofo JF - RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren LA - DE VL - 194 IS - 06 SP - 605 EP - 612 DA - 2022/02/24 KW - machine learning KW - hybrid imaging KW - multiparametric imaging AB - Background Machine learning (ML) is considered an important technology for future data analysis in health care.Methods The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and – for PET imaging – reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers.Results and Conclusion In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future.Key Points: Citation Format PB - Georg Thieme Verlag KG DO - 10.1055/a-1718-4128 UR - http://www.thieme-connect.de/products/ejournals/abstract/10.1055/a-1718-4128 ER -