Organ-specific SPECT activity calibration using 3D printed phantoms for molecular radiotherapy dosimetry

EJNMMI Physics - 2016
Andrew P. Robinson1, Jill Tipping2, David M. Cullen1, David Hamilton2, Richard Brown1, Alex Flynn1, Christopher Oldfield1, Emma Page1,2, Emlyn Price1, Andrew Smith1, Richard Snee1
1Schuster Laboratory, School of Physics and Astronomy, The University of Manchester, Manchester, UK
2Christie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Manchester, UK

Tóm tắt

Patient-specific absorbed dose calculations for molecular radiotherapy require accurate activity quantification. This is commonly derived from Single-Photon Emission Computed Tomography (SPECT) imaging using a calibration factor relating detected counts to known activity in a phantom insert. A series of phantom inserts, based on the mathematical models underlying many clinical dosimetry calculations, have been produced using 3D printing techniques. SPECT/CT data for the phantom inserts has been used to calculate new organ-specific calibration factors for 99m Tc and 177Lu. The measured calibration factors are compared to predicted values from calculations using a Gaussian kernel. Measured SPECT calibration factors for 3D printed organs display a clear dependence on organ shape for 99m Tc and 177Lu. The observed variation in calibration factor is reproduced using Gaussian kernel-based calculation over two orders of magnitude change in insert volume for 99m Tc and 177Lu. These new organ-specific calibration factors show a 24, 11 and 8 % reduction in absorbed dose for the liver, spleen and kidneys, respectively. Non-spherical calibration factors from 3D printed phantom inserts can significantly improve the accuracy of whole organ activity quantification for molecular radiotherapy, providing a crucial step towards individualised activity quantification and patient-specific dosimetry. 3D printed inserts are found to provide a cost effective and efficient way for clinical centres to access more realistic phantom data.

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