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. To improve the performance of your Monte Carlo simulations, you can distribute the computations to run in parallel on multiple cores using Parallel Computing Toolbox™ and MATLAB Parallel Server™.Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response. Running Monte Carlo Simulations in Parallel Simulink Design Optimization™ provides interactive tools to perform this sensitivity analysis and influence your Simulink model design. Monte Carlo simulations help you gain confidence in your design by allowing you to run parameter sweeps, explore your design space, test for multiple scenarios, and use the results of these simulations to guide the design process through statistical analysis. The design and testing of these complex systems involves multiple steps, including identifying which model parameters have the greatest impact on requirements and behavior, logging and analyzing simulation data, and verifying the system design. You can model and simulate multidomain systems in Simulink ® to represent controllers, motors, gains, and other components. Risk Management Toolbox™ facilitates credit simulation, including the application of copula models.įor more control over input generation, Statistics and Machine Learning Toolbox™ provides a wide variety of probability distributions you can use to generate both continuous and discrete inputs. Financial Toolbox™ provides stochastic differential equation tools to build and evaluate stochastic models. In financial modeling, Monte Carlo Simulation informs price, rate, and economic forecasting risk management and stress testing. MATLAB is used for financial modeling, weather forecasting, operations analysis, and many other applications. MATLAB ® provides functions, such as uss and simsd, that you can use to build a model for Monte Carlo simulation and to run those simulations.
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