Multi-modality imaging for treatment planning of selective internal radionuclide therapy (SIRT)
Xikai Tang (ESR2) will develop and validate algorithms for personalized treatment planning and treatment verification in selective internal radionuclide therapy (SIRT) for patients suffering from tumors or metastases in the liver. SIRT involves the selective arterial injection of radioactive microspheres (90Y, a beta emitter), maximizing their uptake in the tumor cells and minimizing the uptake in healthy tissues. Treatment planning is done with SPECT/CT (using 99mTc-MAA), PET (using 18F-FDG) with CT or MR, and contrast enhanced cone beam CT. Treatment verification will be done with TOF-PET/MR. This multi modal imaging creates a wealth of information which is currently under-used.
The aim of this project is to develop dedicated image alignment (registration) and segmentation techniques in order to extract more information from these images. This will lead to more accurate dose calculations and treatment verification, which in turn will improve the efficacy of the therapy. Treatment verification with PET is challenging, because 90Y emits only 32 positrons per million disintegrations (producing very noisy data). Therefore, this project also involves the evaluation and further development of PET reconstruction algorithms to suppress noise and at the same time maintain a good spatial resolution.
This work will be performed at the division of Nuclear Medicine at the Leuven University (KUL) and in collaboration with Mirada Medical, Oxford, UK, a company which develops and commercializes medical imaging software, including software dedicated to treatment planning and treatment verification based on multimodal imaging.
Jafargholi Rangraz E, Tang X, van Laeken C, Maleux G, Dekervel J, van Cutsem E, Verslype C, Baete K, Nuyts J, and Deroose CM (2020) Quantitative comparison of pre-treatment predictive and post-treatment measured dosimetry for selective internal radiation therapy using cone-beam CT for tumor and liver perfusion territory definition. EJNMMI Res. 10, 94. https://dx.doi.org/10.1186%2Fs13550-020-00675-5
Tang X, Jafargholi Rangraz E, Coudyzer W, Bertels J, Robben D, Schramm G, Deckers W, Maleux G, Baete K, Verslype C, Gooding MJ, Deroose CM, and Nuyts J (2020) Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. Eur J Nucl Med Mol Imaging 47, 2742–2752. https://doi.org/10.1007/s00259-020-04800-3
You can find a summary of this publication here.