Targeted radionuclide therapy (TRT) plays an increasingly important role in treatment of a number of cancers including thyroid cancer and non-Hodgkins lymphoma. TRT agents for other cancers are at various stages of development. For example, locoregional therapy using Y-90 labeled microspheres for non-resectable liver tumors appears promising. For these therapies, dosimetry is an essential part of their development, approval, and validation. Dosimetry can help reduce adverse reactions in trials and provides insight into the reasons for failure or success of the therapeutic agents both in individuals and in populations. It also plays an important role in patient-specific treatment planning. Dose estimates in TRT are based on results of quantitative planar or SPECT imaging studies. Quantitative planar imaging, though widely used, involves ad hoc combinations of compensations for various image degrading effects, resulting in variable accuracy and precision. Image reconstruction methods available on commercial SPECT systems are typically designed and optimized for diagnostic procedures involving visual interpretation and not for quantification. In the previous funding period of this grant we developed quantitative SPECT reconstruction methods and validated them in the context of whole organ dosimetry. However, for tumors, locoregional therapy or radiolabeled peptides, 3D dosimetry is essential, which requires estimates of the 3D activity distribution in organs at the sub-organ level. The accuracy of activity distribution estimates is limited by image degrading factors, noise, and partial volume effects. The ability to image bremsstrahlung radiation from TRT agents that do not emit gamma rays could have a number of important applications, but is complicated by the continuous energy spectrum of primary photons and the resulting high levels of photon scatter. In this competing renewal, we propose to develop and optimize quantitative SPECT acquisition and reconstruction methods for TRT dosimetry applications. These will include SPECT reconstruction methods that model the image degrading effects for both gamma ray and bremsstrahlung radiation emitters. They will use 3D and 4D maximum a posteriori (MAP) methods to provide noise reduction and incorporate anatomical information to reduce partial volume effects. The 4D methods will also provide further noise reduction through optimized smoothing in the time dimension and incorporate registration into the reconstruction algorithm. We propose to optimize and validate these new quantitative SPECT methods using a combination of physical phantom, realistic simulation and animal studies and to apply them in clinical trials of several TRT agents. Finally, we will rigorously evaluate the accuracy and precision of these methods in comparison with conventional methods in simulated populations of phantoms. The result of this research will be a set of well-validated quantitative SPECT reconstruction methods with well-characterized accuracies and precisions. These methods would provide substantial improvements in 3D dose estimates, and thus in the ability to predict and understand biological response and optimize therapeutic doses for TRT.

Public Health Relevance

Reliable dosimetry of targeted radionuclide therapies (TRTs) for cancer could facilitate the development and approval of TRT agents and provide patient specific optimization for improved outcomes. The results of this research will provide reliable, well-validated and well-characterized quantitative imaging methods designed for TRT dosimetry applications that would provide the basis for more reliable dose estimates for improved cancer therapy outcomes.

National Institute of Health (NIH)
National Cancer Institute (NCI)
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Biomedical Imaging Technology Study Section (BMIT)
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Deye, James
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Johns Hopkins University
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Jha, Abhinav K; Mena, Esther; Caffo, Brian et al. (2017) Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography. J Med Imaging (Bellingham) 4:011011
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