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QIBA DCE-MRI Analysis Algorithm Validation Specification and Testing Daniel Barboriak M.D. Duke University Medical Center 2008.9.9.

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Presentation on theme: "QIBA DCE-MRI Analysis Algorithm Validation Specification and Testing Daniel Barboriak M.D. Duke University Medical Center 2008.9.9."— Presentation transcript:

1 QIBA DCE-MRI Analysis Algorithm Validation Specification and Testing Daniel Barboriak M.D. Duke University Medical Center 2008.9.9

2 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.

3 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

4 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.

5 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.

6 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

7 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

8 Phase I  Review progress that has already has been made (http://dblab.duhs.duke.edu/modules/dce mri_test_images/index.php?id=1)

9 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

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

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


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