Fereshteh Yousefirizi, PhD

To develop and evaluate a personalized physician-in-the-loop (PPitL) AI framework for accurate and efficient CT tumor segmentation through iterative clinician feedback.

Lesion tracking establishes correspondence across imaging time points to assess disease evolution and treatment response. Despite registration-, graph-, and AI-based methods across CT, MR, PET, and PET/MR, robust correspondence in whole-body CT remains challe...

Theranostic digital twins (TDTs) are virtual representations of individual patients, that integrate clinical patient history, imaging, and physiologically based pharmacokinetic (PBPK) modeling to simulate radiopharmaceutical therapies (RPTs). Their validation...

Automated quantification of tumor burden in PSMA PET/CT imaging is hampered by the low specificity of image-only AI models, which frequently misclassify physiological uptake as disease. This necessitates time-intensive manual corrections, limiting clinical ut...