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Global View of All Collected Data -Phantom Data from all sites -Duke, Echosens, Langevin, Mayo, Philips, Siemens, SSI, UCSD, U Wisconsin, U Michigan -Each.

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Presentation on theme: "Global View of All Collected Data -Phantom Data from all sites -Duke, Echosens, Langevin, Mayo, Philips, Siemens, SSI, UCSD, U Wisconsin, U Michigan -Each."— Presentation transcript:

1 Global View of All Collected Data -Phantom Data from all sites -Duke, Echosens, Langevin, Mayo, Philips, Siemens, SSI, UCSD, U Wisconsin, U Michigan -Each site’s data is imported and ‘cleaned’ and combined on one Excel spreadsheet -Analyzed in JMP 5.1 Page 1

2 Global View of All Collected Data -Phantom Data from all sites -Duke, Echosens, Langevin, Mayo, Philips, Siemens, SSI, UCSD, U Wisconsin, U Michigan -Each Data Point is Mean of 10 measurements -Red quantiles: 10/90%, 25/75%, Median -Includes ‘Hard’ and ‘Soft’ -Philips’ phantom damaged -Phantoms confounded w/ site Page 2

3 Page 3 Soft Phantom Analysis 3/3

4 r Page 4 LevelMinimum10%25%Median75%90%Maximum Echosens0.873670.8755540.8793610.8905440.9309490.934470.934496 S20000.7720.79480.8140.8350.8680.8820.9 SSI0.890.90.930.9611.0281.19 Verisonics-Duke0.864970.8692250.873890.914510.938390.9405520.94221 Verisonics-Mayo0.86870.875620.89210.90170.91580.926040.9282 Soft Phantom Analysis 1/3

5 Need to recheck Echosens data Page 5 Soft Phantom Analysis 2/3 SSI Siemens

6 Page 6 Soft Phantom Analysis 2/3 Duke Mayo

7 To Do Appraiser is not a factor in all analysis ANOVA is missing DoF, so concern over providing estimates Even after eliminating Echosens data, residual plots and model are suspicious Continue analysis within site and within system for further insights Page 7 Soft Phantom Summary SourceNparmDFSum of SquaresF RatioProb > F PHANTOM#750.0766741013.2524<.0001 LostDFs DEPTH (cm)220.0433961418.7515<.0001 APPRAISER #[PHANTOM#]16 0.014984350.80930.6743 System310.30120535260.3023<.0001 LostDFs

8 Hard Phantom Analysis 1/3 Page 8

9 Hard Phantom Analysis 2/3 LevelMinimum10%25%Median75%90%Maximum Echosens2.0280422.0346332.0656772.1281922.1685322.2320992.28903 S20001.912.01572.115252.16052.215752.2592.371 SSI1.952.012.052.082.1 2.15 Verisonics-Duke2.006572.0122962.02032.084772.15162.1555442.16001 Verisonics-Mayo2.0252.035122.05042.13182.18352.200062.2131 Page 9

10 Hard Phantom Analysis 3/3 Page 10 SSI Siemens

11 Hard Phantom Analysis 3/3 Page 11 Duke Mayo

12 Summary Appraiser is not a factor in all analysis Seem to be a depth dependency To Do Finish ANOVA Analysis Contribute to understanding of results and outliers Do analysis for each system Determine how to analyze Duke – Phantom Measure Page 12 Summary


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