A New Computational Algorithm for Treatment Planning of Targeted Radionuclide Therapy
Supervision by: Dr. Kuangyu Shi, Shadi Albarqouni
, Prof. Nassir Navab
Contact: Dr. Kuangyu Shi, Email: email@example.com
Targeted radionuclide therapy  is a cutting-edge molecular therapy enabling precise treatment of several tumors such as neuroendocrine carcinoma [2,3] or prostate cancer . Significantly extended survival and markedly reduced complication  has been achieved and it is increasingly listed in several guidelines in oncological practices.
In contrast to conventional external beam radiotherapy, targeted radionuclide therapy can selectively deliver radiation to tumors or target organs. The cell-targeting protein is combined with a small amount of radionuclide. When injected into the patient’s bloodstream, this radiopeptide travels to and binds to tumor cells, delivering a high dose of radiation to the cancer. This pharmacokinetic-driven dose-delivery procedure however makes the treatment planning extremely difficult [6,7] due to two reasons: 1) the limited dynamic information obtained in planning imaging; 2) the limited consideration of complicated physiological individualities.
The proposed master thesis will develop a novel computational method to extrapolate the missing kinetic information and integrate more individual information to improve the dosimetry planning of targeted radionuclide therapy. Pharmacokinetic modeling and information retrieval will be applied to optimize the kinetic estimation and dose calculation.
The student is expected to have good knowledge in medical imaging. Advanced skill in python programming is required.
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