QIBA DCE-MRI Analysis Algorithm Validation Specification and Testing Daniel Barboriak M.D. Duke University Medical Center

Overview  Problems: Lack of standardized analysis Quantitative imaging results not comparable across labs  Multiple causes Different models Different implementations  Hinders broad acceptance CT perfusion example Goh V et al. Radiology 2007, 242:777.

Purpose of this initiative  To aid in the process of DCE-MRI analysis standardization by developing standard set of DICOM images simulating DCE-MRI images  To develop a framework for evaluation of DCE-MRI analysis methods

Simulated data  Good: Ideally, can test software to see that it performs as designed (verification, not validation)  Bad: Simulated data is not realistic Challenge is to create simulated data appropriate to the use case.

Measuring performance  Challenging: what is the proper metric? Robust vs. accurate  Realistic view: all software fails at extremes. Simulations can define the areas of “parameter space” for which results are inaccurate and/or unreliable.

Benefits of this initiative  Primary: evaluation of analysis tools for the clinical trial outlined in the use case  Secondary: Provide test data useful to analysis software developers before release Support FDA approval of analysis package Compare analysis methods after release

Action plan phases  Phase I Review and next steps Define acceptance criteria for software packages (don’t look for perfection)  Phase II Software package analysis  Phase III An analysis framework

Phase I  Review progress that has already has been made ( mri_test_images/index.php?id=1)

Steps  Define a “vascular input function”  Choose a model and create tissue simulations from the vascular function  Convert to signal intensities  Convert to DICOM images

Phase I  Next steps Analysis model Synthetic T1 mapping images Ratio image bias correction Noise addition

Phase I  Software output problems  Figure of merit  Acceptance criteria