About this Research Topic
Imaging has also been the backbone of the oncology world. It is used for diagnosis as well as planning. MRI has a superior soft tissue contrast but lacks the electron density information for the dose calculation, alternative approaches use synthetic CT for dose calculation. The development of the MRI-guided system and online adaptation can be considered as the bridge between many specialties, starting from learning about MRI safety by the therapy physicist to the use of AI for robust planning. Active research is done to improve the images (advanced MRI techniques, Nuclear medicine and PET-MRI, synthetic images, CBCT for dose calculation) for clinical workflows. However, all these topics have a lot of room for exploration.
In this Research Topic, we welcome research articles, and case reports focusing, but not limited to:
- AI applications in imaging improvement, robust planning, image improvement, or clinical outcome prediction.
- improving the clinical workflow
- clinical trials (eg: adaptive planning, segmentation, synthetic CT, etc.)
Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
Keywords: AI, MRI, CT, PET, Segmentation, oncology, clinical outcomes
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