Characterization of diffusion-weighted imaging signal is typically performed by modeling the data based on biophysical, mathematical, and/or statistical models to estimate quantitative biomarkers. However, conventional nonlinear fitting, which is required for the estimation of model parameters, often suffers from instability and degeneracy. In this study, we propose a Model-free Diffusion-wEighted MRI technique (MODEM) with machine learning to detect cervical carcinomas by using diffusion signal intensities and the first-order statistical features extracted from the signal attenuation as the input. By using MODEM, superior diagnostic performance and stability can be achieved even with limited number of b-values in cervical cancer detection.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords