Keywords: Data Processing, DSC & DCE Perfusion, Dynamic Contrast Enhanced MRI, Wavelet Analysis, Nested Model Selection, Radiation Induced Effect, Signal Coherence, Signal Time-Frequency Analysis, Physiological Tissue Characterization
Motivation: The current nested model selection (NMS) method for pharmacokinetic analysis of Dynamic Contrast Enhanced (DCE) MRI data assumes a single physiological model per voxel, potentially overlooking mixed-model scenarios in complex tissues like tumors.
Goal(s): To develop a probabilistic NMS (PNMS) technique that estimates voxel-wise probabilities of multiple physiological models for more accurate microvasculature parameter estimation in DCE-MRI.
Approach: Using DCE-MRI data from a rat glioblastoma model, we employed a Kohonen Self-Organizing Map (K-SOM) to estimate model probabilities and compared them to conventional NMS technique.
Results: PNMS provided comparable accuracy, with faster computation and more reliable permeability estimates.
Impact: Probabilistic nested model selection has the potential to improve the accuracy of DCE-based microvasculature parameter estimation. It enhanced DCE-MRI based tumor microenvironmental information assessment, that may translate to more precise diagnoses, targeted therapies, and informed clinical decision-making for cancer patients.
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