Software for an integrated image-guided dosimetry planning system to predict individual responses to delivery of targeted radiotherapy.
- Acquires, registers, and visualizes SPECT-CT data with anatomical MR imaging.
- Combines imaging data with computational dosimetry models for specific radionuclides in order to calculate absorbed doses on anatomical images.
- Provides quick assessment of chemotherapy effectiveness, even before the first cycle is complete.
- Allows clinician to adjust dose and chemotherapy agent earlier, sparing patients unnecessary treatments and side effects.
Imaging is used to measure the in vivo effect of radionuclide therapy on the target. These estimates of the efficacy and toxicity of radiotherapy are necessary for individualized treatment. Presently, however, personalized estimates of targeted radiotherapy are uncertain and most dose calculations for internal radiation have been limited to simple dose calculation methods. Although these methods have improved, image registration and anatomical segmentation have not been improved with these calculation methods which limit the clinical usefulness of the system as a whole. Development of patient-specific dosimetry for administered radionuclides combined with molecular imaging is essential for a better understanding of tumor response and normal tissue toxicity for individual patients.
Emory researchers have developed software modules that use deformable image registration methods to accurately register anatomical and functional imaging data and provide atlas-based and automated segmentation of normal anatomy of the treated organ. Registration and segmentation are the most intensive tasks. After the datasets are segmented and registered automatically, absorbed doses can be calculated and visualized on anatomical images, thus providing a targeted radionuclide therapy planning system in real-time. Treatment plans can then be modified, for example, to deliver a higher dose to regions of the tumor that are metabolically active. This method to accurately predict individual responses to treatments has wide applications in oncology, liver disease, and other indications in which radiotherapy or radiation therapy are required.
Software modules have been developed.