This is in part because implementation and standardization of DCE-MRI is challenging. Īlthough several individual studies have demonstrated the promise of DCE-MRI as an imaging biomarker, widespread clinical adoption of this technique remains limited. The use of DCE-MRI has also been explored in other fields such as obstetrics and the neurosciences. Studies have especially focused on using DCE-MRI to characterize tumor phenotypes or to evaluate the response of tumors to therapy. Given the inherent leakiness of tumor blood vessels (which enhances the signal observed at the tumor site due to more CA leakage), the majority of DCE-MRI studies have focused on oncology applications. The physiological properties of the tissue of interest are inferred by analyzing the image signal change kinetics induced by the CA within the tissue region of interest (ROI). In particular, physiological information garnered from techniques such as diffusion MRI, blood oxygen-level dependent (BOLD) MRI, iron-oxide imaging, dynamic susceptibility MRI (DSC-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) are being explored as imaging biomarkers in virtually all aspects of medicine, especially in oncology and neurological diseases.ĭCE-MRI involves the rapid serial acquisition of T1-weighted images before, during and after intravenous injection of a contrast agent (CA). The utility of dynamic magnetic resonance imaging (MRI) studies to diagnose diseases, characterize their progression, and evaluate treatment response is a topic of vigorous research. The availability of a flexible analysis tool will aid future studies using DCE-MRI.Ī public release of ROCKETSHIP is available at. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for changes or additions to be made by the advanced user as well. Its applicability to both preclinical and clinical datasets is shown. ConclusionĪ DCE-MRI software suite was implemented and tested using simulations. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown using DCE-MRI studies of the human brain and a murine tumor model. Simulations also demonstrate the utility of the data-driven nested model analysis. Robustness of the software to provide reliable fits using multiple kinetic models is demonstrated using simulated data. ROCKETSHIP was implemented using the MATLAB programming language. ![]() ROCKETSHIP incorporates analyses with multiple kinetic models, including data-driven nested model analysis. Here, we developed ROCKETSHIP, an open-source, flexible and modular software for DCE-MRI analysis. Few software tools are currently available that specifically focuses on DCE-MRI analysis with multiple kinetic models. ![]() However, analysis of DCE-MRI data is complex and benefits from concurrent analysis of multiple kinetic models and parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response.
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