| dc.description.abstract | Patients who have undergone radiation therapy for brain metastases often have latent lesions such as recurrent tumor or radiation necrosis. Differentiating these lesions in vivo is critical for providing adequate care, but conventional imaging methods are unable to do so. Time-dependent diffusion targeting cell size is a potential method for making this distinction. While quantitative cell size models are time-consuming to implement in the brain, we propose a simple and fast method, SSIFT, to generate qualitative cell size-based contrast in brain tumors. This project comprises the development and preclinical validation of SSIFT, with applications to differentiate tumor from radiation necrosis. In this work, SSIFT is optimized for human imaging with considerations maximizing contrast and image quality. Simulation, in vitro imaging correlated with microscopy, and in vivo imaging of rat models of tumor and radiation necrosis are used to validate that cellularity of cancer-sized cells drives SSIFT contrast. A multiparametric protocol including SSIFT and other quantitative MRI methods is used to map the multivariate space to a binary classifier of tumor and necrosis in rats, providing insight into how the novel SSIFT contrast compares to and complements existing methods. Finally, a reproducible rodent model of radiation necrosis using clinical linear accelerator (LINAC)-based stereotactic radiosurgery (SRS) is provided and is characterized by lesion growth, targeting accuracy, and histological characterization of radiation-induced injury with confirmed necrosis. In summary, this work develops and validates SSIFT as a method to provide tumor-specific contrast in clinically feasible brain imaging. | |
